SNP/InDel-Based GWAS Reveals QTNs and Candidate Genes for Seed Oil and Protein Content in Northern China Soybean Core Accessions

SNP/InDel-Based GWAS Reveals QTNs and Candidate Genes for Seed Oil and Protein Content in Northern China Soybean Core Accessions | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article SNP/InDel-Based GWAS Reveals QTNs and Candidate Genes for Seed Oil and Protein Content in Northern China Soybean Core Accessions Dequan Liu, Jian Chen, Liantai Su, Mingwei Duan, Hao Li, Yunlong Hou, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8169697/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 23 Feb, 2026 Read the published version in Euphytica → Version 1 posted 10 You are reading this latest preprint version Abstract Seed oil and protein content in soybean [ Glycine max (L.) Merr.] are complex quantitative traits controlled by multiple genes and highly susceptible to environmental influences. To investigate the genetic basis of these traits in Northern China, a major soybean-producing area, we re-sequenced 334 core accessions of soybean landraces and elite cultivars from this area. Based on phenotypic data collected over multiple years, a subsequent SNP/InDel-based GWAS for seed oil and protein content identified fifteen quantitative trait nucleotides (QTNs) significantly associated with the traits. Noticeably, qOil05-1 was consistently detected across three years, accounting for 4.8 ~ 14.46% of phenotypic variance (PVE). Within the confidence interval of qOil05-1 , we identified GmHAD1 , a gene encoding a haloacid dehalogenase-like hydrolase, as a strong candidate gene. GmHAD1 expression differed substantially (~ 3.04 fold) between the two major haplotypes (H1 and H2 - 1). Further analysis confirmed that the two major haplotypes of GmHAD1 showed significant or highly significant differences in seed oil and protein content. Overall, our findings offer valuable information into the genetic mechanisms underlying oil and protein accumulation in soybean, providing guidance for future genetic improvement of soybean quality. Soybean Oil Protein GWAS Northeast China Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1. Introduction Soybean ( Glycine max (L.) Merr.) is an economically critical legume crop, supplying approximately 28.81% of the world’s vegetable oil and 69.40% of its protein consumption, according to data from 2023/2024 ( http://www.soystats.com ). Consequently, the development of cultivars with elevated levels of oil or protein content would benefit farmers and is key breeding objective to enhance farmer profitability. However, achieving this goal is challenging, as these traits are quantitatively controlled by multiple minor-effect genes, are susceptible to distinct environmental factors, and typically exhibit a strong negative correlation (Hartwig et al. 1972; Hyten et al. 2004 ; Patil et al. 2017 ). Therefore, identifying and characterizing the genetic loci governing oil and protein content in soybean is crucial for advancing molecular breeding efforts. To date, over 300 quantitative trait loci/nucleotides (QTLs/QTNs) for seed oil and 240 for seed protein, have been registered in SoyBase ( https://www.soybase.org/ ). However, numerous QTLs/QTNs for soybean oil and protein content have been reported, most of which were duplicated or not validated. Furthermore, the low density of molecular markers used in previous studies has hindered fine mapping and cloning of causal genes within these typically broad QTL/QTN regions (Cao et al. 2017 ). The advent of re-sequencing technology has overcome this limitation by generating high-density single nucleotide polymorphism (SNP) and insertion-deletion (InDel) markers. Genome-Wide Association Studies (GWAS) leverage these markers and linkage disequilibrium (LD) to enable precise localization of trait-associated loci (Shao et al. 2022 ). GWAS has been successfully applied to clone functional genes in several plants, including cotton (Ma et al. 2018 ), wheat (Yang et al. 2019 ), rice (Yano et al. 2016 ), maize (Li et al. 2013 ), and Arabidopsis thaliana (Ren et al. 2019 ). However, most studies rely solely on SNP markers, with only a few incorporating InDel markers as a complementary approach (Hu et al. 2022 ; Guan et al. 2022 ; Wang et al. 2022 ). Recent evidence suggests that InDel-GWAS served as a simple yet effective supplement to SNP-GWAS and can enhance the identification of candidate genes (Hu et al. 2024 ). In recent years, GWAS have emerged as a prominent method for identifying genes regulating soybean oil and protein content. For instance, Zhang et al. ( 2019 a) used Recombinant Inbred Lines (RILs) and a panel of 200 accessions to identify QTLs, including qPro20-1 on chromosome 20 (Chr.20), which stably explained more than 7% of PVE for both traits. Zhang et al. ( 2019b ) identified GqOil20 , a significant QTL accounting for 23.70% of the PVE for oil content across multiple environments, and further proposed GmOLEO1 , located within the GqOil20 region, as a positive regulator of oil accumulation. Similarly, Miao et al. ( 2020 ) used a core population of 382 diverse cultivated soybean accessions, including 71,293 SNPs, to identify loci associated with seed oil content using LD analysis. They detected a strong selection signature, cqSeed oil-010/007 , overlapped with oil content QTL, and pinpointed GmSWEET39 as a candidate gene that controls seed oil content and may have been selected during soybean improvement. Goettel et al. ( 2022 ) employed both SNPs and InDels to analyze the whole-genome resequencing data from 278 soybean accessions, and identified POWR1 that pleiotropically controls seed protein, oil content, and yield. Many other studies have also used GWAS to identify QTLs/ QTNs and candidate genes associated with seed oil or protein content in soybean (Hwang et al. 2014 ; Lee et al. 2019 ; Liu et al. 2020 ; Jin et al. 2023 ). Despite these advances, the genes that have been identified as regulating seed oil and protein content in soybean remain insufficient for breeding programs targeting diverse agro-environment. Northern China, a primary area for cultivated soybean production and domestication (Song et al. 2023 ; Zhuang et al. 2022 ), harbors abundant soybean germplasm resources ideal for breeding. Leveraging this diversity, we perform a GWAS on a core collection of 334 accessions (including landraces and cultivars) genotyped with 3,306,713 SNPs and 249,898 InDels. A total of 15 QTNs for seed oil and protein content were identified across multiple environments. Notably, integrated gene expression and haplotype analysis pinpointed GmHAD1 , encoding a Haloacid dehalogenase-like hydrolase protein, within the qOil05-1 locus as a strong candidate gene regulating oil content. 2. Materials and Methods 2.1 Plant materials and field experiments A natural panel of 334 soybean accessions was used in this study, comprising 141 landraces and 192 improved cultivars. Of these, 333 accessions were collected from five provinces in northern China (latitudes ranging from 38°43’ N to 50°22’ N), with the U.S. cultivar (‘Williams82’) included as a reference (Table S1). The panel was planted at the experimental farm of the Jilin Academy of Agricultural Sciences in Gongzhuling City (124°49′E, 43°31′N) over four consecutive years (2020–2023), following a randomized complete block design with three replications. Sowing dates were May 5 (2020), May 8 (2021), May 9 (2022) and May 15 (2023). Harvesting occurred from September 1 to October 23 each year upon maturity. The field experiments followed a randomized complete block design. Each accession was planted in a four-row plot (5 m in length × 0.6 m row spacing) at a plant density of 10 cm between individuals. Standard field management practices were implemented throughout the entire growing season. 2.2 Phenotyping and statistical analysis for protein and oil content Oil and protein content were analyzed from three independent biological replicates per accession, each composed of a bulk sample collected from at least ten plants. Three independent biological replicates were established in total. An Infratec™ 1241 Grain Analyzer (FOSS, Sweden) was used to quantify oil and protein content in three technical replicates per plot. Each replicate consisted of 300 healthy seeds randomly sampled from the plot. The mean values were used for statistical analysis. Broad-sense heritability ( H 2 ) was conducted estimated on an entry-mean basis with the formula: H 2 = σ g 2 /( σ g 2 + σ g*e 2 / n e + σ 2 /( n e * r )), where the variance components are defined as genetics ( σ g 2 ), genotype-by-environment interaction ( σ g*e 2 ), and error ( σ 2 ), with n e is the number of environments and r is the number of replicates, respectively (Knapp et al. 1985 ). 2.3 Re‑sequencing of the soybean accessions Genomic DNA was extracted from the young leaves of each soybean accession using a modified CTAB method (Hamwieh and Xu 2008 ). The genomic DNA was re-sequenced and 150 bp paired-end sequencing libraries were constructed using the HiSeq PE Cluster Kit v4-cBot-HS (Illumina, San Diego, CA, United States) following the manufacturer’s instructions. The G . max Wm82.a2.v1 was downloaded ( https://phytozome-next.jgi.doe.gov/ ) as the soybean reference genome. The average mapping rate between the sample and reference genome was 97.19% with an average coverage depth of 10 × and rate of 91.71%. Using Plink2 software, SNPs and InDels were filtered and removed with minor allele frequency (MAF) 20% and InDel length > 10 bp. The annotations of SNP and InDel were labeled according to the G . max Wm82.a2.v1 by using ANNOVAR software (Wang et al. 2010 ). CMplot4.3.1 (Yin et al. 2021 ) R package was used to draw the distribution maps of SNPs and InDels on chromosomes. 2.4 GWAS analysis of soybean accessions SNPs and InDels derived from autosomes (GGA) were used for GWAS analysis. Using the Genomic Association and Prediction Integrated Tool (GAPIT3) package in R software (Zhang et al. 2010), a mixed linear model (MLM) was used for the GWAS based on the SNPs and InDels (MAF ≥ 0.05) from the 334 soybean accessions (Lipka et al. 2012 ), respectively. Population structure analysis was conducted using ADMIXTURE3.2 for maximum likelihood estimation. The principal component analysis (PCA) and kinship were calculated by the VanRaden method in GAPIT3 (VanRaden 2008 ). The threshold for significant association was set as − log 10 (1/n) ≥ 6.52 for SNP-GWAS or − log 10 (1/n) ≥ 5.40 for InDel-GWAS (n is the SNP or InDel number in soybean accessions re-sequencing) (Yang et al. 2014 ). Manhattan plots and quantile-quantile (QQ) plots were generated using the CMplot 4.3.1. The PVE of SNPs and InDels was estimated with reference to Teslovich et al. ( 2010 ). 2.5 Linkage disequilibrium (LD) analysis The soybean accessions were divided into two categories based on landraces and improved cultivars. The square of the correlation coefficients ( r 2 ) between two SNPs within a 300 kb in each chromosome was calculated using PopLDdecay3.40. The LD decay distance for each chromosome and category was estimated as the point where r 2 dropped to half of its maximum value (Zhang et al. 2019 ). The larger value between the two categories was used as the final LD decay distance for each chromosome (Table S2). 2.6 Prediction of candidate genes The regions of QTNs, which correspond to the physical distance where r 2 drops to half its maximum in different chromosomes, were defined based on the LD decay value. We identified potential candidate genes by focusing on the genes from the LD decay distance QTNs on both sides (Wang et al.2016b). Candidate gene predictions were then performed using the gene annotation database of Glycine max Wm82.a2.v1 combined with homologous gene annotations from Arabidopsis thaliana TAIR10 available at Phytozome 13. 2.7 RNA isolation and qRT-PCR Total RNA was extracted from seeds of different soybean varieties at 30, 40, and 50 days after flowering using RNeasy Plant Mini Kit (Qiagen, Hilden, Germany). The first-strand cDNA was generated using PrimeScript RT Reagent Kit (Takara, Shiga, Japan). Quantitative real-time PCR (qRT-PCR) was performed on StepOne Real-time PCR System (Thermo Fisher Scientific) with SYBR Select Master Mix (Thermo Fisher Scientific, Waltham, MA, USA). The relative expression levels were quantified using the 2 −∆∆CT method (Livak et al. 2001), with the house-keeping gene Glyma.08G146500 ( GmACTIN ) as an internal control. Three biological replicates were performed for each tissue. 3. Results 3.1 Phenotypic data The frequency distribution and statistical analysis of oil and protein contents across 334 soybean accessions over four consecutive years are shown in Fig. 1 . The seed oil contents ranged from 15.87 to 25.50%, 17.17 to 22.47%, 14.17 to 22.47%, and 14.83 to 23.53%, respectively (Table 1 ). The mean oil content ranged from 20.44 ~ 21.5%. The seed protein contents ranged from 38.37 to 51.83%, 38.97 to 51.77%, 37.10 to 54.53%, and 37.73 to 54.17%, respectively. The mean protein content ranged from 42.02 to 43.57%. The coefficient of variance (CV) of oil and protein was 4.02 ~ 4.37% and 4.13 ~ 4.65%, respectively. The broad-sense heritability ( H 2 ) of two traits was 93.08% and 95.22%, respectively. The variance, CV, and H 2 range revealed substantial phenotypic variation in protein and oil contents. The skewness and kurtosis analyses indicated that the traits showed a normal distribution in the histograms, suggesting their suitability for GWAS. Table 1 Statistical analysis of protein and oil contents in 334 soybean accessions Traits Environments Mean ± Std (%) a Variance b CV (%) c Range (%) Kurtosis d Skewness e F Value of Variance Analysis H 2 (%) f Genotype (G) Environment (E) G×E Oil 2020 year 20.99 ± 0.90 0.81 4.29 15.87 ~ 25.50 6.03 -1.68 57.11*** 1561.96*** 4.12*** 93.08 2021 year 20.44 ± 0.84 0.70 4.11 17.17 ~ 22.47 1.46 -0.80 2022 year 20.66 ± 0.83 0.68 4.02 14.17 ~ 22.47 11.16 -1.95 2023 year 21.50 ± 0.94 0.87 4.37 14.38 ~ 23.53 8.31 -1.74 Protein 2020 year 42.02 ± 1.87 3.48 4.45 38.37 ~ 51.83 4.10 1.15 101.08*** 1582.34*** 7.87*** 92.22 2021 year 43.57 ± 1.80 3.25 4.13 38.97 ~ 51.77 1.25 0.61 2022 year 42.03 ± 1.85 3.41 4.40 37.10 ~ 54.53 6.02 1.08 2023 year 42.41 ± 1.97 3.89 4.65 37.73 ~ 54.17 3.93 0.89 a Std: Standard deviation of the phenotypic trait. b Variance: Variance of the phenotypic trait. c CV: Coefficient of variation. d Kurtosis: A measure of the phenotypic trait of the probability distribution of a real-valued random variable. e Skewness: A measure of the phenotypic trait of the probability distribution of a real-valued random variable about its mean. ***: Indicate significance at 0.001 levels. f H 2 : Broad-sense heritability. 3.2 The PCA and LD decay analysis of SNPs and InDels In total, 3,306,713 SNPs and 249,898 InDels were identified across the genome, with average maker densities of 305.46 maker/kb and 23.08 maker/kb, respectively (Fig. 2 A, B). The distributions of SNPs and InDels were uneven, showing higher density in distal chromosomal regions than near centromeres (Table S4, Table S5). The results demonstrated that recombination rates in the distal chromosomal regions significantly exceeded those in pericentromeric regions (Barton et al. 2008 ). To examine genetic relatedness among soybean accessions, the population structure was constructed using ADMIXTURE under a maximum likelihood framework (Fig. S1). When K = 8, the accessions clustered into 8 major groups, which corresponded to the minimum CV value (Cross Validation Error, CV value = 0.35946). Principal component analysis (PCA) was conducted based on SNPs and InDels to explain genetic variance, with the top three principal components (PCs) accounting for 52.52%, 24.66%, and 22.82% (SNPs) and 36.95%, 32.96%, and 30.09% (InDels) of the variance (Fig. S2A, S2B). Additionally, LD analysis showed that LD decreased with physical distance between SNP/InDel markers across the population (Fig. S2C, S2D). 3.3 Genome-wide association study (GWAS) of loci associated with seed oil and protein content Quantile-quantile (QQ) plots showed acceptable deviation of the observed data from the expected distribution (Figs. 3 , S3 , S4 , S5 ). The GWAS revealed a total of 104 significant SNPs for oil and 28 significant SNPs for protein, as well as 71 significant InDels for oil and 66 InDels for protein across the four years, using the MLM models (Table S6 and Table S7). Among these significant loci, 12 significant SNPs or 22 significant InDels were detected more than two years (Table S8). These SNPs and InDels were distributed on seven chromosomes, including Chr.3, Chr.4, Chr.5, Chr.8, Chr.17, Chr.18, and Chr.20. The PVE for seed oil and protein ranged from 1.73% to 31.45% and 1.02% to 4.74%, respectively. The additive effect on oil and protein varied from − 2.36% to 1.19% and from − 2.39% to 3.63%, respectively. Among stable loci detected across multiple years, one significant oil-associated SNP (SGM05_36581160) on Chr.5 was identified in three years, and two protein-associated InDels (SGM20_19421831 and SGM20_43687148) on Chr.20 were detected in three years. Genomic regions surrounding these significant SNPs or InDels were defined as QTNs based on LD decay analysis. Additionally, SNPs or InDels with PVE more than 5% in at least one environment were classified as QTNs. Therefore, five QTNs (each containing at least one SNP) were identified across three chromosomes, showing significant associations in two or more environments and traits (Table 2 ). In addition, ten QTNs (each encompassing one InDel) were mapped to six chromosomes, also exhibiting stable detection across multiple environments and traits (Table 3 ). Among these, five QTNs were consistently detected in three years or harbored more than two makers. Specifically, on Chr.5, the locus qOil5-1 spans a 35.67 kb region and includes three significant SNPs and one significant InDel associated with oil content across three environments. The PVE of qOil5-1 ranges from 4.87% to 14.46%, with positive additive effects of 0.89%~1.09%. On Chr.18, qOil18-2 covers an extended region of 108.80 kb, containing one significant SNP and one significant InDel for oil detected in two environments. This locus explains 9.67%~29.38% of the phenotypic variation, and its additive effect shows a positive correlation of 0.69%~0.74%. Additionally, qOil18-3 spans 109.88 kb with three significant SNPs for oil identified across two years, accounting for 16.63%~23.38% of the variation and a positive additive effect of 0.81%~0.95%. On Chr.20, the SNP-based QTN qOP20-1 was significantly associated with oil content in two environments and protein content in one environment. Notably, qOil20-1 showed the highest PVE (31.23%~34.68%) for oil across two environments. Table 2 Details of QTNs with seed oil and protein content via GWAS in soybean QTN Name Significant SNP/Indel makers Chr. a Region (bp) b Environment PVE (%) c SNP or Indel with the highest P-value Add. (%) d gene_location Gene and functional annotationd e QTL and Reference f qOil5-1 SNP:SGM05_36571289/ SGM05_36574164/ SGM05_36581160; Indel:SGM05_36574450 5 36558389–36594060 Oil:2020\2021\2023 Oil:4.87 ~ 14.46 Oil:1.36*10 − 9 Oil:0.89 ~ 1.09 upstream/intronic/intronic/intronic Glyma.05G177000(HAD_2) ,HAD hydrolase, subfamily IA Oil 4 − 1/Seed protein 41 − 1 (Priolli et al. 2019 ) qOil18-2 SNP:SGM18_56414949; Indel:SGM18_56410929 18 56360549–56469349 Oil:2022\2023 Oil:9.67 ~ 29.38 Oil:2.56*10 − 9 Oil: 0.69 ~ 0.74 intronic/downstream Glyma.18G283300(PP2C) ,Protein phosphatase 2C qOil18-3 SNP:SGM18_57241943/SGM18_57242007/ SGM18_57242685/SGM18_57242802/SGM18_57243027 18 57187543–57297427 Oil:2020\2022 Oil:16.63 ~ 23.38 Oil:3.68*10 − 9 Oil:0.81 ~ 0.95 intronic/intronic/intronic/intronic/intronic Glyma.18G295000(CRS1_YhbY) ,Poly(A)-specific exoribonuclease PARN qOP20-1 SNP:SGM20_232139 20 195639–268639 Oil:2020\2023 Protein:2020 Oil:16.44 ~ 16.89; Protein:4.74 Oil:7.61*10 − 9 Protein:2.22*10 − 8 Oil:1.16 ~ 1.19 Protein:-2.39 upstream Glyma.20G002301(JMJC) ,JmjC domain qOil20-1 SNP:SGM20_897062 20 860562–933562 Oil:2021\2022 Oil:31.23 ~ 34.68 Oil:2.45*10 − 8 Oil:0.65 ~ 0.67 UTR3 Glyma.20G010600(PPR) ,pentatricopeptide repeat domain Seed oil 27 − 3 (Reinprecht et al. 2006 ) s Chr. chromosome. b Region in base pairs for the Significant SNP is provided according to Glycine max Wm82.a2v1 c PVE. explanation of phenotypic variation d Add. Additive effect e Positive value means Ref. allele contributed to the traits f Reported quantitative trait loci (QTL) in Soybase database ( https://www.soybase.org/ ) that overlapped our QTL here. Table 3 Details of significant InDels with seed oil and protein content via GWAS in soybean QTN Name Significant Indel makers Position (bp) Environment PVE (%) SNP or Indel with the highest P-value Additive effect (%) gene_location Gene and functional annotationd QTL/Gene and Reference qOil3-1 SGM03_3986531 Chr.3:3986531 Oil:2020\2022 Oil:26.22 ~ 31.45 Oil:9.40*10 − 7 Oil:0.27 ~ 0.44 UTR3 Glyma.03G034100 (SEC61B, SBH2) ,Protein transport protein SecG/Sec61-beta/Sbh Seed oil 39 − 14 (Wang et al. 2014 ) qOil4-1 SGM04_2178115 Chr.4:2178115 Oil:2020\2022 Oil:11.99 ~ 14.92 Oil:9.47*10 − 7 Oil:0.50 ~ 0.68 intronic Glyma.04G026800 ,Kinesin-like protein(DUF3490) qOil4-2 SGM04_8102921 Chr.4:8102921 Oil:2022\2023 Oil:11.95 ~ 14.87 Oil:2.11*10 − 6 Oil:0.08 ~ 0.10 upstream Glyma.04G091800 ,Aspartic peptidase, Acid proteases Seed protein 7 − 2 (Orf et al. 1999 ) qOP8-1 SGM08_15502732 Chr.8:15502732 Oil:2023; Protein:2020\2023 Oil:5.92; Protein:1.46–1.51 Oil:3.93*10 − 7 Protein:1.69*10 − 6 Oil: 0.95 Protein:-1.78 exonic_nonframeshift_insertion Glyma.08G192700(groEL, HSPD1) ,Chaperone tailless complex polypeptide 1 (TCP-1) Oil 4 − 1/Seed protein 41 − 1 (Priolli et al. 2019 ) qOil17-1 SGM17_8335202 Chr.17:8335202 Oil:2020\2022; Protein:2020 Oil:6.86 ~ 8.69; Protein:1.70 Oil:2.04 − 8 Protein:2.55 − 7 Oil:0.83 ~ 0.95 Protein: -1.39 intergenic Glyma.17G106100 ,Family not named; Glyma.17G106200(DUF1817) ,Protein chlororespiratory reduction 6 Seed oil 23 − 3 (Hyten et al. 2004 ) qOil17-2 SGM17_8462234 Chr.17:8462234 Oil:2020\2022 Oil:8.28 ~ 10.29 Oil:1.14*10 − 8 Oil:0.69 ~ 0.93 upstream Glyma.17G107800 ,Ubiquitin thiolesterase Seed oil 23 − 3 (Hyten et al. 2004 ) qOil18-1 SGM18_48599328 Chr.18:48599328 Oil:2020\2022 Oil:5.86 ~ 7.48 Oil:5.67*10 − 8 Oil:0.91 ~ 0.97 intergenic Glyma.18G203700 ,Ribosomal protein S4/S9; Glyma.18G203800 ,Peptidase_M24 GmFAD7-1 (Andreu et al. 2010 ) qOil20-2 SGM20_16829930 Chr.20:16829930 Oil:2022\2023; Protein:2020\2023 Oil:10.01 ~ 10.37; Protein:2.60 ~ 2.61 Oil:1.50*10 − 7 Protein:1.37*10 − 7 Oil:0.50 ~ 0.59 Protein:-1.49~-1.34 intergenic Glyma.20G057500(UDPGT) ,UDP-glucuronosyl/UDP-glucosyltransferase; Glyma.20G057600(FAD_FR) ,Oxidoreductase FAD/NAD(P)-binding Seed oil 42 − 37 (Han et al. 2015 ) qOP20-2 SGM20_19421831 Chr.20:19421831 Oil:2022\2023; Protein:2020\2022\2023 Oil:7.76 ~ 9.91; Protein:1.61 ~ 2.22 Oil:5.93*10 − 8 Protein:1.7*10 − 7 Oil:0.67 ~ 0.74 Protein:-1.67~-1.90 intergenic Glyma.20G062000(NUDIX) ,Nucleoside Triphosphate Pyrophosphohydrolase; Glyma.20G062100 ,ion transport Seed oil 42 − 17 (Han et al. 2015 ) qOP20-3 SGM20_43687148 Chr.20:43687148 Oil:2020\2022; Protein:2020\2022\2023 Oil:4.25 ~ 5.40; Protein:1.03 ~ 1.18 Oil:3.90*10 − 7 Protein:6.16*10 − 8 Oil:0.97~-0.83 Protein:-1.59~-1.66 intergenic Glyma.20G199200(PPR) ,pentatricopeptide repeat domain; Glyma.20G199300(SANT) ,SANT/Myb domain GmOLEO1 (Zhang et al. 2019 ) 3.4 Mining of candidate genes underlying QTNs The regions of five QTNs were searched based on the annotation of the soybean reference genome G . max Wm82.a2.v1(Table S8). Within the qOil18-2 interval (Chr18-56360549 and Chr18-56469349), 14 genes are annotated (Table S9), with eight candidate genes exhibiting expression levels exceeding 3 FPKM during seed development. Among these, Glyma.18G283300 is a Protein phosphatase 2C family protein (PP2C) gene and has been reported to regulate key genes involved in grain oil accumulation (Lu et al. 2017 ). Within the qOil18-3 interval (Chr18-57187543 and Chr18-57297427), 18 genes are annotated, with nine candidate genes exhibiting expression levels exceeding 3 FPKM during seed development (Table S10). The significant locus is located in Glyma.18G295000 , which has not been proven to be involved in grain oil accumulation in previous studies. However, this gene is adjacent to the downstream gene Glyma.18G295100 , a bidirectional sugar transporter ( Sweet9 ). Notably, the homologous gene GmSweet39 of this transporter has been demonstrated to affect the oil and protein accumulation in soybean grains in prior reports (Zhang et al. 2020 ). Within the qOP20-1 interval (Chr20-195639 and Chr20-268639), there are eleven genes (Table S9), four of which are candidate genes with expression levels exceeding 3 FPKM during seed development. One of these genes, Glyma.20G002301 , encodes a zinc finger protein. Previous studies have shown that the causal genes of multi-year repeatable locus, GmZF351 and GmZF392 , positively regulate the biosynthesis of soybean lipids (Hu et al. 2021; Li et al. 2017 ; Lu et al. 2021 ). Within the qOil20-1 interval (Chr20-860562 and Chr20-268639), there are 13 genes (Table S9), four of which are candidate genes with expression levels exceeding 3 FPKM during seed development. The Glyma.20G010100 , which is highly expressed at the end of seed maturity, encodes a tetratricopeptide repeat (TPR)-like superfamily protein. Crucially, TPR domains have been shown to increase acetyl-coenzyme A activity and catalyze lipid synthesis (Ramsey et al. 2002; Chen et al. 1997). Acetyl-coenzyme A is a key enzyme for the metabolic and synthetic pathway of soybean oil (Baud et al. 2003 ). 3.5 GmHAD1 encoding a Haloacid dehalogenase-like hydrolase protein is the candidate gene for qOil05-1 The qOil05-1 interval (Chr05-36558389 and Chr05-36594060) contains seven genes (Table S9). According to public RNA-seq data (Gmax_508_Wm82.a2.v1), only Glyma.05G177000 , Glyma.05G177100 , and Glyma.05G177400 show expression levels exceeding 3 FPKM during soybean grain development. Among them, Glyma.05G177000 ( GmHAD1 ) encodes a Haloacid dehalogenase-like hydrolase ( HAD ) superfamily. Previous studies have shown that its homolog AtHAD1 in Arabidopsis plays a role in repressing the ABA response (Lee et al. 2022 ). Notably, the ABA signaling pathway can regulate the expression of multiple key genes associated with grain oil biosynthesis, thereby influencing the final oil content in seeds (Zheng et al. 2010 ; To et al. 2012 ; Zhang et al. 2016 ). Further exploration of SNP and InDel variations in Glyma.05G177000 within 334 soybean accessions revealed 49 SNPs and 9 InDels across its 8.09 kb genomic region. Analysis of these variants showed that 40 markers were in the same linkage block, including three multi-year repeatable significant loci (Fig. S6). Further analysis of haplotypes in this genomic interval across 334 soybean accessions identified seven distinct haplotypes, categorized into two major clades (H1 and H2). Notably, H1 exhibited further differentiation into four subgroups, while H2 was divided into three subgroups. Analysis of protein sequences across haplotypes revealed no variation within H1 subgroup, but identified four divergent protein sequences between H1 and H2 (Fig. S7). Among 334 soybean accessions, H1 comprised 322 varieties, while H2 contained three subgroups: H2-1 with 10 varieties, H2-2 with 1 variety, and H2-3 with 1 variety. The expression of the six candidate genes was analyzed through qPCR for the qOil05-1 H1 and H2-1 during the seed-filling stage at 30, 40, and 50 days after flowering. Four genes ( Glyma.05G177000 , Glyma.05G177100 , Glyma.05G177200 , and Glyma.05G177400 ) were detected in soybean seed (Fig. 4 ). Glyma.05G177000 ( GmHAD1 ) showed significantly different ( P < 0.05) expression between H1 and H2-1 at 50 days. Phenotypic comparisons (2020–2023) showed significant or highly significant differences ( P < 0.05 or P < 0.01) in oil and protein content between H1 and H2-1 (Fig. 5 ). These data suggested that different haplotypes of GmHAD1 may influence grain oil and protein accumulation. 4. Discussion Soybean seed oil and protein accumulation are complex quantitative traits governed by multiple genetic loci and modulated by environmental factors (Li et al. 2019 ). Notably, the genetic architecture underlying these traits often vary across different cultivation regions. Advances in high-depth re-sequencing for SNP and InDel discovery, combined with GWAS, have significantly enhanced the precision of locus identification and candidate gene screening compared with traditional genetic mapping methods such as AFLP, RFLP, and SSR (Zhou et al. 2015 ; Kim et al. 2021 ; Shao et al. 2022 ). The northern region of China, serving as both the primary soybean production region and the cradle of soybean domestication and cultivation (Hao et al. 2020 ), harbors abundant soybean genetic resources. However, most of these resources remain genetically uncharacterized, creating a critical gap in leveraging their potential for complex trait dissection. To address this, we utilized a core collection of 334 soybean accessions from Northeast China, combining deep re-sequencing technology with GWAS, to identify QTNs for oil and protein content. This approach yielded 3,306,713 SNPs and 249,898 InDels across the entire genome, with average maker densities of 305.46 makers/kb and 23.08 makers/kb for SNPs and InDels, respectively. Our GWAS analysis using a MLM model identified 115 significant SNPs and 101 significant InDels associated with oil and protein content (Tables S4-S5). Among these, 12 significant SNPs and 22 significant InDels were consistently detected across more than two years. Based on LD decay analysis, QTNs was defined as the genomic regions flanking significant SNP or InDel that explain more than 5% of the PVE in at least one environment (Wang et al. 2016 b). This led to the identification of five SNP-based and ten InDel-based QTNs that were consistently detected across multiple environments. Notably, eleven of these QTNs intervals overlapped with or were close to previously reported loci, while four represents novel discoveries (Table 2 – 3 ). Based on linkage disequilibrium (LD) analysis, we prioritized candidate genes within the associated QTNs. The qOil18-2 contained Glyma.18G283300 , encoding a Protein Phosphatase 2C (PP2C). Given that PP2C activities can affect soybean seed size (Lu et al. 2017 ) a trait highly correlated with oil and protein content (Duan et al. 2023 ) this gene represents a promising candidate. Within the qOil18-3 , we identified the candidate gene Glyma.18G295100 , a bidirectional sugar transporter ( Sweet9 ). As its homolog GmSweet39 has been shown to regulate oil and protein accumulation (Zhang et al. 2020 ), Sweet9 may exert a similar function. Glyma.20G002301 , encoding a zinc finger protein, was identified as a candidate gene associated with oil and protein content at qOP20-1. Homologs of this gene, including GmZF351 and GmZF392 , have been demonstrated to influence soybean lipid biosynthesis. Similarly, Glyma.20G010100 , a tetratricopeptide repeat (TPR)-like superfamily protein gene, was characterized as a candidate gene specifically associated with oil content at qOil20-1. While these genes represent the most likely candidate genes for each QTNs, their precise functions require further validation by future studies. Within the qOil05-1 , Glyma.05G177000 ( GmHAD1) emerged as a prime candidate, exhibiting expression levels exceeding 3 FPKM during soybean grain development. Previous studies have shown that AtHAD1 , an HAD ortholog in Arabidopsis thaliana , represses the ABA response (Lee et al. 2022 ). Importantly, alteration in ABA content affects the expression of multiple key genes involved in grain development and oil content in seeds (Zheng et al. 2010 ; To et al. 2012 ; Zhang et al. 2016 ). GmHAD1 showed a ~ 3.04-fold difference in expression level between H1 and H2-1 during the seed of oil accumulation stage at 50 days after flowering. Analysis of GmHAD1 haplotypes in the population revealed that H1 and H2-1 showed significant or highly significant differences in oil and protein content. Collectively, these findings suggest that GmHAD1 may be the promising candidate gene regulating seed oil and protein content in soybean. 5. Conclusions In this study, GWAS was employed to identify novel QTNs associated with seed oil and protein content in a northern China soybean core collection. Over four consecutive years of observation, 15 QTNs related to oil and protein were consistently detected in at least two years. Notably, a novel oil-related QTN, qOil5-1 , was identified across three years and harbored seven candidate genes. Among these, GmHAD1 emerged as a key novel candidate gene potentially involved in seed oil accumulation. Collectively, these findings provide valuable genetic loci and candidate genes for the molecular breeding of soybean cultivars with enhanced oil or protein content. Declarations Competing Interests The authors have no relevant financial or non-financial interests to disclose. Funding This work was supported by the Jilin Province Agricultural Science and Technology Innovation Project (Grant numbers: CXGC2024RCB001) and the Agriculture Science and Technology Major Project. D. Liu has received research support from Jilin Provincial Department of Human Resources and Social Security for scholarships. Author Contribution All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Dequan Liu, Jian Chen, Liantai Su, Mingwei Duan, Hao Li, Yunlong Hou, Zhengguo Cui, Liang Chen, Fuxin Li, Hongmei Qiu, and Yueqiang Wang. The first draft of the manuscript was written by Dequan Liu and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript. Acknowledgement This work was supported by the Jilin Province Agricultural Science and Technology Innovation Project (Grant numbers: CXGC2024RCB001) and the Agriculture Science and Technology Major Project. Dr. Liu gratefully acknowledges the Jilin Provincial Department of Human Resources and Social Security for scholarships. Data Availability The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request. References Andreu V, Lagunas B, Collados R, Picorel R, Alfonso M (2010) The GmFAD7 gene family from soybean: identification of novel genes and tissue-specific conformations of the FAD7 enzyme involved in desaturase activity. 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1","display":"","copyAsset":false,"role":"figure","size":653036,"visible":true,"origin":"","legend":"\u003cp\u003eFrequency distribution of seed oil content (A) and protein content (B) in the 334 soybean accessions over the four growing years (2020-2023).\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8169697/v1/1e2235b5b99f7358580163ae.png"},{"id":98774621,"identity":"9c9ac292-28ec-4974-b3d2-37c508df8e84","added_by":"auto","created_at":"2025-12-22 12:05:29","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":807800,"visible":true,"origin":"","legend":"\u003cp\u003eGenomic distribution of the 334 soybean accessions. The distributions map of SNPs (A) and Indels (B).\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8169697/v1/dc5729e45b190b078dfaa3ba.png"},{"id":98635532,"identity":"c2b951d1-1166-4dd7-9174-654d3bf7bbd7","added_by":"auto","created_at":"2025-12-19 17:26:15","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":361615,"visible":true,"origin":"","legend":"\u003cp\u003eManhattan plots and QQ plots from the SNP-GWAS for seed oil content across four years: (A) 2020, (B) 2021, (C) 2022 and (D) 2023. The red horizontal dashed line indicates the genome-wide significance threshold (−log\u003csub\u003e10\u003c/sub\u003e(1/n) ≥ 6.52).\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-8169697/v1/7474130894038caa07c2c3b8.png"},{"id":98635866,"identity":"e479bc53-342b-4736-a58d-e182e965af5d","added_by":"auto","created_at":"2025-12-19 17:26:37","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":84050,"visible":true,"origin":"","legend":"\u003cp\u003eExpression profiles of \u003cem\u003eqOil05-1 \u003c/em\u003ecandidate genes across seed development. Transcript level of (A) \u003cem\u003eGlyma.05G177000,\u003c/em\u003e(B) \u003cem\u003eGlyma.05G177100,\u003c/em\u003e (C) \u003cem\u003eGlyma.05G177200, \u003c/em\u003eand (D) \u003cem\u003eGlyma.05G177400\u003c/em\u003ewere measured in seeds at 20, 30 and 50 days after flowering from three H1 amd three H2-1 accessions. Gene expression was normalized to \u003cem\u003eGmACTIN,\u003c/em\u003e and data showed mean ±SD (n = 3). * Indicates statistically significant differences between means (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05)\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-8169697/v1/4bf403331c4886e5f3fc1592.png"},{"id":98635221,"identity":"cb3b3da6-5660-49a1-a31b-571437d989a8","added_by":"auto","created_at":"2025-12-19 17:26:10","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":97321,"visible":true,"origin":"","legend":"\u003cp\u003ePhenotypic variation in seed oil and protein content across haplotypes H1 and H2-1. The box plots depict the median (center line), interquartile range (box), and minimum/maximum values (whiskers). Data are represented as mean ± standard deviation. ** Indicates statistically significant differences between means (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01).\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-8169697/v1/00e372c0e55721abc2d5f2cf.png"},{"id":103765430,"identity":"f005e49f-235a-4cc5-bc8c-89101e26620f","added_by":"auto","created_at":"2026-03-02 16:01:07","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2896671,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8169697/v1/33cc4c25-99d6-4c12-9327-182ef9e361ef.pdf"},{"id":98635800,"identity":"aff3db07-984f-420f-9239-0acb54fe6406","added_by":"auto","created_at":"2025-12-19 17:26:30","extension":"xlsx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":90766,"visible":true,"origin":"","legend":"","description":"","filename":"TableS.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-8169697/v1/6bd5c18dde443ffed34f2235.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"SNP/InDel-Based GWAS Reveals QTNs and Candidate Genes for Seed Oil and Protein Content in Northern China Soybean Core Accessions","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eSoybean (\u003cem\u003eGlycine max\u003c/em\u003e (L.) Merr.) is an economically critical legume crop, supplying approximately 28.81% of the world\u0026rsquo;s vegetable oil and 69.40% of its protein consumption, according to data from 2023/2024 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.soystats.com\u003c/span\u003e\u003cspan address=\"http://www.soystats.com\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Consequently, the development of cultivars with elevated levels of oil or protein content would benefit farmers and is key breeding objective to enhance farmer profitability. However, achieving this goal is challenging, as these traits are quantitatively controlled by multiple minor-effect genes, are susceptible to distinct environmental factors, and typically exhibit a strong negative correlation (Hartwig et al. 1972; Hyten et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Patil et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Therefore, identifying and characterizing the genetic loci governing oil and protein content in soybean is crucial for advancing molecular breeding efforts.\u003c/p\u003e \u003cp\u003eTo date, over 300 quantitative trait loci/nucleotides (QTLs/QTNs) for seed oil and 240 for seed protein, have been registered in SoyBase (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.soybase.org/\u003c/span\u003e\u003cspan address=\"https://www.soybase.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). However, numerous QTLs/QTNs for soybean oil and protein content have been reported, most of which were duplicated or not validated. Furthermore, the low density of molecular markers used in previous studies has hindered fine mapping and cloning of causal genes within these typically broad QTL/QTN regions (Cao et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). The advent of re-sequencing technology has overcome this limitation by generating high-density single nucleotide polymorphism (SNP) and insertion-deletion (InDel) markers. Genome-Wide Association Studies (GWAS) leverage these markers and linkage disequilibrium (LD) to enable precise localization of trait-associated loci (Shao et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). GWAS has been successfully applied to clone functional genes in several plants, including cotton (Ma et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), wheat (Yang et al. \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), rice (Yano et al. \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), maize (Li et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), and \u003cem\u003eArabidopsis thaliana\u003c/em\u003e (Ren et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). However, most studies rely solely on SNP markers, with only a few incorporating InDel markers as a complementary approach (Hu et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Guan et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Wang et al. \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Recent evidence suggests that InDel-GWAS served as a simple yet effective supplement to SNP-GWAS and can enhance the identification of candidate genes (Hu et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn recent years, GWAS have emerged as a prominent method for identifying genes regulating soybean oil and protein content. For instance, Zhang et al. (\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2019\u003c/span\u003ea) used Recombinant Inbred Lines (RILs) and a panel of 200 accessions to identify QTLs, including \u003cem\u003eqPro20-1\u003c/em\u003e on chromosome 20 (Chr.20), which stably explained more than 7% of PVE for both traits. Zhang et al. (\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2019b\u003c/span\u003e) identified \u003cem\u003eGqOil20\u003c/em\u003e, a significant QTL accounting for 23.70% of the PVE for oil content across multiple environments, and further proposed \u003cem\u003eGmOLEO1\u003c/em\u003e, located within the \u003cem\u003eGqOil20\u003c/em\u003e region, as a positive regulator of oil accumulation. Similarly, Miao et al. (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) used a core population of 382 diverse cultivated soybean accessions, including 71,293 SNPs, to identify loci associated with seed oil content using LD analysis. They detected a strong selection signature, \u003cem\u003ecqSeed oil-010/007\u003c/em\u003e, overlapped with oil content QTL, and pinpointed \u003cem\u003eGmSWEET39\u003c/em\u003e as a candidate gene that controls seed oil content and may have been selected during soybean improvement. Goettel et al. (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) employed both SNPs and InDels to analyze the whole-genome resequencing data from 278 soybean accessions, and identified \u003cem\u003ePOWR1\u003c/em\u003e that pleiotropically controls seed protein, oil content, and yield. Many other studies have also used GWAS to identify QTLs/ QTNs and candidate genes associated with seed oil or protein content in soybean (Hwang et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Lee et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Liu et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Jin et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Despite these advances, the genes that have been identified as regulating seed oil and protein content in soybean remain insufficient for breeding programs targeting diverse agro-environment.\u003c/p\u003e \u003cp\u003eNorthern China, a primary area for cultivated soybean production and domestication (Song et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Zhuang et al. \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), harbors abundant soybean germplasm resources ideal for breeding. Leveraging this diversity, we perform a GWAS on a core collection of 334 accessions (including landraces and cultivars) genotyped with 3,306,713 SNPs and 249,898 InDels. A total of 15 QTNs for seed oil and protein content were identified across multiple environments. Notably, integrated gene expression and haplotype analysis pinpointed \u003cem\u003eGmHAD1\u003c/em\u003e, encoding a Haloacid dehalogenase-like hydrolase protein, within the \u003cem\u003eqOil05-1 locus\u003c/em\u003e as a strong candidate gene regulating oil content.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Plant materials and field experiments\u003c/h2\u003e \u003cp\u003eA natural panel of 334 soybean accessions was used in this study, comprising 141 landraces and 192 improved cultivars. Of these, 333 accessions were collected from five provinces in northern China (latitudes ranging from 38\u0026deg;43\u0026rsquo; N to 50\u0026deg;22\u0026rsquo; N), with the U.S. cultivar (\u0026lsquo;Williams82\u0026rsquo;) included as a reference (Table S1). The panel was planted at the experimental farm of the Jilin Academy of Agricultural Sciences in Gongzhuling City (124\u0026deg;49\u0026prime;E, 43\u0026deg;31\u0026prime;N) over four consecutive years (2020\u0026ndash;2023), following a randomized complete block design with three replications. Sowing dates were May 5 (2020), May 8 (2021), May 9 (2022) and May 15 (2023). Harvesting occurred from September 1 to October 23 each year upon maturity. The field experiments followed a randomized complete block design. Each accession was planted in a four-row plot (5 m in length \u0026times; 0.6 m row spacing) at a plant density of 10 cm between individuals. Standard field management practices were implemented throughout the entire growing season.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Phenotyping and statistical analysis for protein and oil content\u003c/h2\u003e \u003cp\u003eOil and protein content were analyzed from three independent biological replicates per accession, each composed of a bulk sample collected from at least ten plants. Three independent biological replicates were established in total. An Infratec\u0026trade; 1241 Grain Analyzer (FOSS, Sweden) was used to quantify oil and protein content in three technical replicates per plot. Each replicate consisted of 300 healthy seeds randomly sampled from the plot. The mean values were used for statistical analysis. Broad-sense heritability (\u003cem\u003eH\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e) was conducted estimated on an entry-mean basis with the formula: \u003cem\u003eH\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;\u003cem\u003eσ\u003c/em\u003e\u003csub\u003e\u003cem\u003eg\u003c/em\u003e\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e/(\u003cem\u003eσ\u003c/em\u003e\u003csub\u003e\u003cem\u003eg\u003c/em\u003e\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;+\u0026thinsp;\u003cem\u003eσ\u003c/em\u003e\u003csub\u003e\u003cem\u003eg*e\u003c/em\u003e\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e/\u003cem\u003en\u003c/em\u003e\u003csub\u003e\u003cem\u003ee\u003c/em\u003e\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;\u003cem\u003eσ\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e/(\u003cem\u003en\u003c/em\u003e\u003csub\u003e\u003cem\u003ee\u003c/em\u003e*\u003c/sub\u003e\u003cem\u003er\u003c/em\u003e)), where the variance components are defined as genetics (\u003cem\u003eσ\u003c/em\u003e\u003csub\u003e\u003cem\u003eg\u003c/em\u003e\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e), genotype-by-environment interaction (\u003cem\u003eσ\u003c/em\u003e\u003csub\u003e\u003cem\u003eg*e\u003c/em\u003e\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e), and error (\u003cem\u003eσ\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e), with \u003cem\u003en\u003c/em\u003e\u003csub\u003e\u003cem\u003ee\u003c/em\u003e\u003c/sub\u003e is the number of environments and \u003cem\u003er\u003c/em\u003e is the number of replicates, respectively (Knapp et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e1985\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Re‑sequencing of the soybean accessions\u003c/h2\u003e \u003cp\u003eGenomic DNA was extracted from the young leaves of each soybean accession using a modified CTAB method (Hamwieh and Xu \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). The genomic DNA was re-sequenced and 150 bp paired-end sequencing libraries were constructed using the HiSeq PE Cluster Kit v4-cBot-HS (Illumina, San Diego, CA, United States) following the manufacturer\u0026rsquo;s instructions. The \u003cem\u003eG\u003c/em\u003e. \u003cem\u003emax\u003c/em\u003e Wm82.a2.v1 was downloaded (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://phytozome-next.jgi.doe.gov/\u003c/span\u003e\u003cspan address=\"https://phytozome-next.jgi.doe.gov/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) as the soybean reference genome. The average mapping rate between the sample and reference genome was 97.19% with an average coverage depth of 10 \u0026times; and rate of 91.71%. Using Plink2 software, SNPs and InDels were filtered and removed with minor allele frequency (MAF)\u0026thinsp;\u0026lt;\u0026thinsp;0.05, missing genotype rates\u0026thinsp;\u0026gt;\u0026thinsp;20% and InDel length\u0026thinsp;\u0026gt;\u0026thinsp;10 bp. The annotations of SNP and InDel were labeled according to the \u003cem\u003eG\u003c/em\u003e. \u003cem\u003emax\u003c/em\u003e Wm82.a2.v1 by using ANNOVAR software (Wang et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). CMplot4.3.1 (Yin et al. \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) R package was used to draw the distribution maps of SNPs and InDels on chromosomes.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 GWAS analysis of soybean accessions\u003c/h2\u003e \u003cp\u003eSNPs and InDels derived from autosomes (GGA) were used for GWAS analysis. Using the Genomic Association and Prediction Integrated Tool (GAPIT3) package in R software (Zhang et al. 2010), a mixed linear model (MLM) was used for the GWAS based on the SNPs and InDels (MAF\u0026thinsp;\u0026ge;\u0026thinsp;0.05) from the 334 soybean accessions (Lipka et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), respectively. Population structure analysis was conducted using ADMIXTURE3.2 for maximum likelihood estimation. The principal component analysis (PCA) and kinship were calculated by the VanRaden method in GAPIT3 (VanRaden \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). The threshold for significant association was set as \u0026minus;\u0026thinsp;log\u003csub\u003e10\u003c/sub\u003e(1/n)\u0026thinsp;\u0026ge;\u0026thinsp;6.52 for SNP-GWAS or \u0026minus;\u0026thinsp;log\u003csub\u003e10\u003c/sub\u003e(1/n)\u0026thinsp;\u0026ge;\u0026thinsp;5.40 for InDel-GWAS (n is the SNP or InDel number in soybean accessions re-sequencing) (Yang et al. \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Manhattan plots and quantile-quantile (QQ) plots were generated using the CMplot 4.3.1. The PVE of SNPs and InDels was estimated with reference to Teslovich et al. (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2010\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Linkage disequilibrium (LD) analysis\u003c/h2\u003e \u003cp\u003eThe soybean accessions were divided into two categories based on landraces and improved cultivars. The square of the correlation coefficients (\u003cem\u003er\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e) between two SNPs within a 300 kb in each chromosome was calculated using PopLDdecay3.40. The LD decay distance for each chromosome and category was estimated as the point where \u003cem\u003er\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e dropped to half of its maximum value (Zhang et al. \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The larger value between the two categories was used as the final LD decay distance for each chromosome (Table S2).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Prediction of candidate genes\u003c/h2\u003e \u003cp\u003eThe regions of QTNs, which correspond to the physical distance where \u003cem\u003er\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e drops to half its maximum in different chromosomes, were defined based on the LD decay value. We identified potential candidate genes by focusing on the genes from the LD decay distance QTNs on both sides (Wang et al.2016b). Candidate gene predictions were then performed using the gene annotation database of Glycine max Wm82.a2.v1 combined with homologous gene annotations from \u003cem\u003eArabidopsis thaliana\u003c/em\u003e TAIR10 available at Phytozome 13.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.7 RNA isolation and qRT-PCR\u003c/h2\u003e \u003cp\u003eTotal RNA was extracted from seeds of different soybean varieties at 30, 40, and 50 days after flowering using RNeasy Plant Mini Kit (Qiagen, Hilden, Germany). The first-strand cDNA was generated using PrimeScript RT Reagent Kit (Takara, Shiga, Japan). Quantitative real-time PCR (qRT-PCR) was performed on StepOne Real-time PCR System (Thermo Fisher Scientific) with SYBR Select Master Mix (Thermo Fisher Scientific, Waltham, MA, USA). The relative expression levels were quantified using the 2\u003csup\u003e\u0026minus;∆∆CT\u003c/sup\u003e method (Livak et al. 2001), with the house-keeping gene \u003cem\u003eGlyma.08G146500\u003c/em\u003e (\u003cem\u003eGmACTIN\u003c/em\u003e) as an internal control. Three biological replicates were performed for each tissue.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Phenotypic data\u003c/h2\u003e \u003cp\u003eThe frequency distribution and statistical analysis of oil and protein contents across 334 soybean accessions over four consecutive years are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The seed oil contents ranged from 15.87 to 25.50%, 17.17 to 22.47%, 14.17 to 22.47%, and 14.83 to 23.53%, respectively (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The mean oil content ranged from 20.44\u0026thinsp;~\u0026thinsp;21.5%. The seed protein contents ranged from 38.37 to 51.83%, 38.97 to 51.77%, 37.10 to 54.53%, and 37.73 to 54.17%, respectively. The mean protein content ranged from 42.02 to 43.57%. The coefficient of variance (CV) of oil and protein was 4.02\u0026thinsp;~\u0026thinsp;4.37% and 4.13\u0026thinsp;~\u0026thinsp;4.65%, respectively. The broad-sense heritability (\u003cem\u003eH\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e) of two traits was 93.08% and 95.22%, respectively. The variance, CV, and \u003cem\u003eH\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e range revealed substantial phenotypic variation in protein and oil contents. The skewness and kurtosis analyses indicated that the traits showed a normal distribution in the histograms, suggesting their suitability for GWAS.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eStatistical analysis of protein and oil contents in 334 soybean accessions\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"12\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eTraits\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eEnvironments\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;Std (%) \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariance \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCV (%) \u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eRange (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eKurtosis \u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSkewness \u003csup\u003ee\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c11\" namest=\"c9\"\u003e \u003cp\u003eF Value of Variance Analysis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eH\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e(%) \u003csup\u003ef\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eGenotype (G)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eEnvironment (E)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eG\u0026times;E\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eOil\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2020 year\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20.99\u0026thinsp;\u0026plusmn;\u0026thinsp;0.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e15.87\u0026thinsp;~\u0026thinsp;25.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-1.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e57.11***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e1561.96***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e4.12***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e93.08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2021 year\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20.44\u0026thinsp;\u0026plusmn;\u0026thinsp;0.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e17.17\u0026thinsp;~\u0026thinsp;22.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.80\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2022 year\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20.66\u0026thinsp;\u0026plusmn;\u0026thinsp;0.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e14.17\u0026thinsp;~\u0026thinsp;22.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e11.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-1.95\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2023 year\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21.50\u0026thinsp;\u0026plusmn;\u0026thinsp;0.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e14.38\u0026thinsp;~\u0026thinsp;23.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e8.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-1.74\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eProtein\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2020 year\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e42.02\u0026thinsp;\u0026plusmn;\u0026thinsp;1.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e38.37\u0026thinsp;~\u0026thinsp;51.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e101.08***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e1582.34***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e7.87***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e92.22\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2021 year\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e43.57\u0026thinsp;\u0026plusmn;\u0026thinsp;1.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e38.97\u0026thinsp;~\u0026thinsp;51.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.61\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2022 year\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e42.03\u0026thinsp;\u0026plusmn;\u0026thinsp;1.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e37.10\u0026thinsp;~\u0026thinsp;54.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2023 year\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e42.41\u0026thinsp;\u0026plusmn;\u0026thinsp;1.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e37.73\u0026thinsp;~\u0026thinsp;54.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.89\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003csup\u003ea\u003c/sup\u003e Std: Standard deviation of the phenotypic trait.\u003c/p\u003e \u003cp\u003e \u003csup\u003eb\u003c/sup\u003e Variance: Variance of the phenotypic trait.\u003c/p\u003e \u003cp\u003e \u003csup\u003ec\u003c/sup\u003e CV: Coefficient of variation.\u003c/p\u003e \u003cp\u003e \u003csup\u003ed\u003c/sup\u003e Kurtosis: A measure of the phenotypic trait of the probability distribution of a real-valued random variable.\u003c/p\u003e \u003cp\u003e \u003csup\u003ee\u003c/sup\u003e Skewness: A measure of the phenotypic trait of the probability distribution of a real-valued random variable about its mean.\u003c/p\u003e \u003cp\u003e***: Indicate significance at 0.001 levels.\u003c/p\u003e \u003cp\u003e \u003csup\u003ef\u003c/sup\u003e \u003cem\u003eH\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e: Broad-sense heritability.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.2 The PCA and LD decay analysis of SNPs and InDels\u003c/h2\u003e \u003cp\u003eIn total, 3,306,713 SNPs and 249,898 InDels were identified across the genome, with average maker densities of 305.46 maker/kb and 23.08 maker/kb, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA, B). The distributions of SNPs and InDels were uneven, showing higher density in distal chromosomal regions than near centromeres (Table S4, Table S5). The results demonstrated that recombination rates in the distal chromosomal regions significantly exceeded those in pericentromeric regions (Barton et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). To examine genetic relatedness among soybean accessions, the population structure was constructed using ADMIXTURE under a maximum likelihood framework (Fig. S1). When K\u0026thinsp;=\u0026thinsp;8, the accessions clustered into 8 major groups, which corresponded to the minimum CV value (Cross Validation Error, CV value\u0026thinsp;=\u0026thinsp;0.35946). Principal component analysis (PCA) was conducted based on SNPs and InDels to explain genetic variance, with the top three principal components (PCs) accounting for 52.52%, 24.66%, and 22.82% (SNPs) and 36.95%, 32.96%, and 30.09% (InDels) of the variance (Fig. S2A, S2B). Additionally, LD analysis showed that LD decreased with physical distance between SNP/InDel markers across the population (Fig. S2C, S2D).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Genome-wide association study (GWAS) of loci associated with seed oil and protein content\u003c/h2\u003e \u003cp\u003eQuantile-quantile (QQ) plots showed acceptable deviation of the observed data from the expected distribution (Figs.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e3\u003c/span\u003e, \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003eS3\u003c/span\u003e, \u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003eS4\u003c/span\u003e, \u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003eS5\u003c/span\u003e). The GWAS revealed a total of 104 significant SNPs for oil and 28 significant SNPs for protein, as well as 71 significant InDels for oil and 66 InDels for protein across the four years, using the MLM models (Table S6 and Table S7). Among these significant loci, 12 significant SNPs or 22 significant InDels were detected more than two years (Table S8). These SNPs and InDels were distributed on seven chromosomes, including Chr.3, Chr.4, Chr.5, Chr.8, Chr.17, Chr.18, and Chr.20. The PVE for seed oil and protein ranged from 1.73% to 31.45% and 1.02% to 4.74%, respectively. The additive effect on oil and protein varied from \u0026minus;\u0026thinsp;2.36% to 1.19% and from \u0026minus;\u0026thinsp;2.39% to 3.63%, respectively. Among stable loci detected across multiple years, one significant oil-associated SNP (SGM05_36581160) on Chr.5 was identified in three years, and two protein-associated InDels (SGM20_19421831 and SGM20_43687148) on Chr.20 were detected in three years. Genomic regions surrounding these significant SNPs or InDels were defined as QTNs based on LD decay analysis. Additionally, SNPs or InDels with PVE more than 5% in at least one environment were classified as QTNs. Therefore, five QTNs (each containing at least one SNP) were identified across three chromosomes, showing significant associations in two or more environments and traits (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). In addition, ten QTNs (each encompassing one InDel) were mapped to six chromosomes, also exhibiting stable detection across multiple environments and traits (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Among these, five QTNs were consistently detected in three years or harbored more than two makers. Specifically, on Chr.5, the locus \u003cem\u003eqOil5-1\u003c/em\u003e spans a 35.67 kb region and includes three significant SNPs and one significant InDel associated with oil content across three environments. The PVE of \u003cem\u003eqOil5-1\u003c/em\u003e ranges from 4.87% to 14.46%, with positive additive effects of 0.89%~1.09%. On Chr.18, \u003cem\u003eqOil18-2\u003c/em\u003e covers an extended region of 108.80 kb, containing one significant SNP and one significant InDel for oil detected in two environments. This locus explains 9.67%~29.38% of the phenotypic variation, and its additive effect shows a positive correlation of 0.69%~0.74%. Additionally, \u003cem\u003eqOil18-3\u003c/em\u003e spans 109.88 kb with three significant SNPs for oil identified across two years, accounting for 16.63%~23.38% of the variation and a positive additive effect of 0.81%~0.95%. On Chr.20, the SNP-based QTN \u003cem\u003eqOP20-1\u003c/em\u003e was significantly associated with oil content in two environments and protein content in one environment. Notably, \u003cem\u003eqOil20-1\u003c/em\u003e showed the highest PVE (31.23%~34.68%) for oil across two environments.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDetails of QTNs with seed oil and protein content via GWAS in soybean\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"11\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQTN Name\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSignificant SNP/Indel makers\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eChr.\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRegion (bp)\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEnvironment\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePVE (%)\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSNP or Indel with the highest P-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eAdd. (%)\u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003egene_location\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eGene and functional\u003c/p\u003e \u003cp\u003eannotationd\u003csup\u003ee\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eQTL and Reference\u003csup\u003ef\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eqOil5-1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSNP:SGM05_36571289/ SGM05_36574164/ SGM05_36581160; Indel:SGM05_36574450\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e36558389\u0026ndash;36594060\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOil:2020\\2021\\2023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eOil:4.87\u0026thinsp;~\u0026thinsp;14.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eOil:1.36*10\u003csup\u003e\u0026minus;\u0026thinsp;9\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eOil:0.89\u0026thinsp;~\u0026thinsp;1.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eupstream/intronic/intronic/intronic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cem\u003eGlyma.05G177000(HAD_2)\u003c/em\u003e,HAD hydrolase, subfamily IA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cem\u003eOil 4\u0026thinsp;\u0026minus;\u0026thinsp;1/Seed protein 41\u0026thinsp;\u0026minus;\u0026thinsp;1\u003c/em\u003e(Priolli et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2019\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eqOil18-2\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSNP:SGM18_56414949;\u003c/p\u003e \u003cp\u003eIndel:SGM18_56410929\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e56360549\u0026ndash;56469349\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOil:2022\\2023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eOil:9.67\u0026thinsp;~\u0026thinsp;29.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eOil:2.56*10\u003csup\u003e\u0026minus;\u0026thinsp;9\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eOil: 0.69\u0026thinsp;~\u0026thinsp;0.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eintronic/downstream\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cem\u003eGlyma.18G283300(PP2C)\u003c/em\u003e,Protein phosphatase 2C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eqOil18-3\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSNP:SGM18_57241943/SGM18_57242007/ SGM18_57242685/SGM18_57242802/SGM18_57243027\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e57187543\u0026ndash;57297427\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOil:2020\\2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eOil:16.63\u0026thinsp;~\u0026thinsp;23.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eOil:3.68*10\u003csup\u003e\u0026minus;\u0026thinsp;9\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eOil:0.81\u0026thinsp;~\u0026thinsp;0.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eintronic/intronic/intronic/intronic/intronic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cem\u003eGlyma.18G295000(CRS1_YhbY)\u003c/em\u003e,Poly(A)-specific exoribonuclease PARN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eqOP20-1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSNP:SGM20_232139\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e195639\u0026ndash;268639\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOil:2020\\2023\u003c/p\u003e \u003cp\u003eProtein:2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eOil:16.44\u0026thinsp;~\u0026thinsp;16.89;\u003c/p\u003e \u003cp\u003eProtein:4.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eOil:7.61*10\u003csup\u003e\u0026minus;\u0026thinsp;9\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eProtein:2.22*10\u003csup\u003e\u0026minus;\u0026thinsp;8\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eOil:1.16\u0026thinsp;~\u0026thinsp;1.19\u003c/p\u003e \u003cp\u003eProtein:-2.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eupstream\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cem\u003eGlyma.20G002301(JMJC)\u003c/em\u003e,JmjC domain\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eqOil20-1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSNP:SGM20_897062\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e860562\u0026ndash;933562\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOil:2021\\2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eOil:31.23\u0026thinsp;~\u0026thinsp;34.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eOil:2.45*10\u003csup\u003e\u0026minus;\u0026thinsp;8\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eOil:0.65\u0026thinsp;~\u0026thinsp;0.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eUTR3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cem\u003eGlyma.20G010600(PPR)\u003c/em\u003e,pentatricopeptide repeat domain\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cem\u003eSeed oil 27\u0026thinsp;\u0026minus;\u0026thinsp;3\u003c/em\u003e(Reinprecht et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2006\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003csup\u003es\u003c/sup\u003e Chr. chromosome.\u003c/p\u003e \u003cp\u003e \u003csup\u003eb\u003c/sup\u003e Region in base pairs for the Significant SNP is provided according to \u003cem\u003eGlycine max\u003c/em\u003e Wm82.a2v1\u003c/p\u003e \u003cp\u003e \u003csup\u003ec\u003c/sup\u003e PVE. explanation of phenotypic variation\u003c/p\u003e \u003cp\u003e \u003csup\u003ed\u003c/sup\u003e Add. Additive effect\u003c/p\u003e \u003cp\u003e \u003csup\u003ee\u003c/sup\u003e Positive value means Ref. allele contributed to the traits\u003c/p\u003e \u003cp\u003e \u003csup\u003ef\u003c/sup\u003e Reported quantitative trait loci (QTL) in Soybase database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.soybase.org/\u003c/span\u003e\u003cspan address=\"https://www.soybase.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) that overlapped our QTL here.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDetails of significant InDels with seed oil and protein content via GWAS in soybean\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQTN Name\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSignificant Indel makers\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePosition (bp)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEnvironment\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePVE (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSNP or Indel with the highest P-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eAdditive effect (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003egene_location\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eGene and functional\u003c/p\u003e \u003cp\u003eannotationd\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eQTL/Gene and Reference\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eqOil3-1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSGM03_3986531\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eChr.3:3986531\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOil:2020\\2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOil:26.22\u0026thinsp;~\u0026thinsp;31.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eOil:9.40*10\u003csup\u003e\u0026minus;\u0026thinsp;7\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eOil:0.27\u0026thinsp;~\u0026thinsp;0.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eUTR3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cem\u003eGlyma.03G034100 (SEC61B, SBH2)\u003c/em\u003e,Protein transport protein SecG/Sec61-beta/Sbh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cem\u003eSeed oil 39\u0026thinsp;\u0026minus;\u0026thinsp;14\u003c/em\u003e (Wang et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2014\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eqOil4-1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSGM04_2178115\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eChr.4:2178115\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOil:2020\\2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOil:11.99\u0026thinsp;~\u0026thinsp;14.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eOil:9.47*10\u003csup\u003e\u0026minus;\u0026thinsp;7\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eOil:0.50\u0026thinsp;~\u0026thinsp;0.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eintronic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cem\u003eGlyma.04G026800\u003c/em\u003e,Kinesin-like protein(DUF3490)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eqOil4-2\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSGM04_8102921\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eChr.4:8102921\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOil:2022\\2023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOil:11.95\u0026thinsp;~\u0026thinsp;14.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eOil:2.11*10\u003csup\u003e\u0026minus;\u0026thinsp;6\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eOil:0.08\u0026thinsp;~\u0026thinsp;0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eupstream\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cem\u003eGlyma.04G091800\u003c/em\u003e,Aspartic peptidase, Acid proteases\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cem\u003eSeed protein 7\u0026thinsp;\u0026minus;\u0026thinsp;2\u003c/em\u003e (Orf et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e1999\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eqOP8-1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSGM08_15502732\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eChr.8:15502732\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOil:2023; Protein:2020\\2023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOil:5.92;\u003c/p\u003e \u003cp\u003eProtein:1.46\u0026ndash;1.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eOil:3.93*10\u003csup\u003e\u0026minus;\u0026thinsp;7\u003c/sup\u003e Protein:1.69*10\u003csup\u003e\u0026minus;\u0026thinsp;6\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eOil: 0.95 Protein:-1.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eexonic_nonframeshift_insertion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cem\u003eGlyma.08G192700(groEL, HSPD1)\u003c/em\u003e,Chaperone tailless complex polypeptide 1 (TCP-1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cem\u003eOil 4\u0026thinsp;\u0026minus;\u0026thinsp;1/Seed protein 41\u0026thinsp;\u0026minus;\u0026thinsp;1\u003c/em\u003e (Priolli et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2019\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eqOil17-1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSGM17_8335202\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eChr.17:8335202\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOil:2020\\2022; Protein:2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOil:6.86\u0026thinsp;~\u0026thinsp;8.69; Protein:1.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eOil:2.04\u003csup\u003e\u0026minus;\u0026thinsp;8\u003c/sup\u003e Protein:2.55\u003csup\u003e\u0026minus;\u0026thinsp;7\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eOil:0.83\u0026thinsp;~\u0026thinsp;0.95 Protein: -1.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eintergenic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cem\u003eGlyma.17G106100\u003c/em\u003e,Family not named;\u003cem\u003eGlyma.17G106200(DUF1817)\u003c/em\u003e,Protein chlororespiratory reduction 6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cem\u003eSeed oil 23\u0026thinsp;\u0026minus;\u0026thinsp;3\u003c/em\u003e (Hyten et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2004\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eqOil17-2\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSGM17_8462234\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eChr.17:8462234\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOil:2020\\2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOil:8.28\u0026thinsp;~\u0026thinsp;10.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eOil:1.14*10\u003csup\u003e\u0026minus;\u0026thinsp;8\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eOil:0.69\u0026thinsp;~\u0026thinsp;0.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eupstream\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cem\u003eGlyma.17G107800\u003c/em\u003e,Ubiquitin thiolesterase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cem\u003eSeed oil 23\u0026thinsp;\u0026minus;\u0026thinsp;3\u003c/em\u003e (Hyten et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2004\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eqOil18-1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSGM18_48599328\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eChr.18:48599328\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOil:2020\\2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOil:5.86\u0026thinsp;~\u0026thinsp;7.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eOil:5.67*10\u003csup\u003e\u0026minus;\u0026thinsp;8\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eOil:0.91\u0026thinsp;~\u0026thinsp;0.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eintergenic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cem\u003eGlyma.18G203700\u003c/em\u003e,Ribosomal protein S4/S9;\u003cem\u003eGlyma.18G203800\u003c/em\u003e,Peptidase_M24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cem\u003eGmFAD7-1\u003c/em\u003e (Andreu et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2010\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eqOil20-2\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSGM20_16829930\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eChr.20:16829930\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOil:2022\\2023; Protein:2020\\2023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOil:10.01\u0026thinsp;~\u0026thinsp;10.37;\u003c/p\u003e \u003cp\u003eProtein:2.60\u0026thinsp;~\u0026thinsp;2.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eOil:1.50*10\u003csup\u003e\u0026minus;\u0026thinsp;7\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eProtein:1.37*10\u003csup\u003e\u0026minus;\u0026thinsp;7\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eOil:0.50\u0026thinsp;~\u0026thinsp;0.59\u003c/p\u003e \u003cp\u003eProtein:-1.49~-1.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eintergenic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cem\u003eGlyma.20G057500(UDPGT)\u003c/em\u003e,UDP-glucuronosyl/UDP-glucosyltransferase;\u003cem\u003eGlyma.20G057600(FAD_FR)\u003c/em\u003e,Oxidoreductase FAD/NAD(P)-binding\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cem\u003eSeed oil 42\u0026thinsp;\u0026minus;\u0026thinsp;37\u003c/em\u003e (Han et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2015\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eqOP20-2\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSGM20_19421831\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eChr.20:19421831\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOil:2022\\2023; Protein:2020\\2022\\2023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOil:7.76\u0026thinsp;~\u0026thinsp;9.91;\u003c/p\u003e \u003cp\u003eProtein:1.61\u0026thinsp;~\u0026thinsp;2.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eOil:5.93*10\u003csup\u003e\u0026minus;\u0026thinsp;8\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eProtein:1.7*10\u003csup\u003e\u0026minus;\u0026thinsp;7\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eOil:0.67\u0026thinsp;~\u0026thinsp;0.74\u003c/p\u003e \u003cp\u003eProtein:-1.67~-1.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eintergenic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cem\u003eGlyma.20G062000(NUDIX)\u003c/em\u003e,Nucleoside Triphosphate Pyrophosphohydrolase;\u003cem\u003eGlyma.20G062100\u003c/em\u003e,ion transport\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cem\u003eSeed oil 42\u0026thinsp;\u0026minus;\u0026thinsp;17\u003c/em\u003e (Han et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2015\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eqOP20-3\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSGM20_43687148\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eChr.20:43687148\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOil:2020\\2022; Protein:2020\\2022\\2023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOil:4.25\u0026thinsp;~\u0026thinsp;5.40;\u003c/p\u003e \u003cp\u003eProtein:1.03\u0026thinsp;~\u0026thinsp;1.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eOil:3.90*10\u003csup\u003e\u0026minus;\u0026thinsp;7\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eProtein:6.16*10\u003csup\u003e\u0026minus;\u0026thinsp;8\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eOil:0.97~-0.83\u003c/p\u003e \u003cp\u003eProtein:-1.59~-1.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eintergenic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cem\u003eGlyma.20G199200(PPR)\u003c/em\u003e,pentatricopeptide repeat domain;\u003cem\u003eGlyma.20G199300(SANT)\u003c/em\u003e,SANT/Myb domain\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cem\u003eGmOLEO1\u003c/em\u003e (Zhang et al. \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2019\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Mining of candidate genes underlying QTNs\u003c/h2\u003e \u003cp\u003eThe regions of five QTNs were searched based on the annotation of the soybean reference genome \u003cem\u003eG\u003c/em\u003e. \u003cem\u003emax\u003c/em\u003e Wm82.a2.v1(Table S8). Within the \u003cem\u003eqOil18-2\u003c/em\u003e interval (Chr18-56360549 and Chr18-56469349), 14 genes are annotated (Table S9), with eight candidate genes exhibiting expression levels exceeding 3 FPKM during seed development. Among these, \u003cem\u003eGlyma.18G283300\u003c/em\u003e is a Protein phosphatase 2C family protein (PP2C) gene and has been reported to regulate key genes involved in grain oil accumulation (Lu et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Within the \u003cem\u003eqOil18-3\u003c/em\u003e interval (Chr18-57187543 and Chr18-57297427), 18 genes are annotated, with nine candidate genes exhibiting expression levels exceeding 3 FPKM during seed development (Table S10). The significant locus is located in \u003cem\u003eGlyma.18G295000\u003c/em\u003e, which has not been proven to be involved in grain oil accumulation in previous studies. However, this gene is adjacent to the downstream gene \u003cem\u003eGlyma.18G295100\u003c/em\u003e, a bidirectional sugar transporter (\u003cem\u003eSweet9\u003c/em\u003e). Notably, the homologous gene \u003cem\u003eGmSweet39\u003c/em\u003e of this transporter has been demonstrated to affect the oil and protein accumulation in soybean grains in prior reports (Zhang et al. \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Within the \u003cem\u003eqOP20-1\u003c/em\u003e interval (Chr20-195639 and Chr20-268639), there are eleven genes (Table S9), four of which are candidate genes with expression levels exceeding 3 FPKM during seed development. One of these genes, \u003cem\u003eGlyma.20G002301\u003c/em\u003e, encodes a zinc finger protein. Previous studies have shown that the causal genes of multi-year repeatable locus, \u003cem\u003eGmZF351\u003c/em\u003e and \u003cem\u003eGmZF392\u003c/em\u003e, positively regulate the biosynthesis of soybean lipids (Hu et al. 2021; Li et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Lu et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Within the \u003cem\u003eqOil20-1\u003c/em\u003e interval (Chr20-860562 and Chr20-268639), there are 13 genes (Table S9), four of which are candidate genes with expression levels exceeding 3 FPKM during seed development. The \u003cem\u003eGlyma.20G010100\u003c/em\u003e, which is highly expressed at the end of seed maturity, encodes a tetratricopeptide repeat (TPR)-like superfamily protein. Crucially, TPR domains have been shown to increase acetyl-coenzyme A activity and catalyze lipid synthesis (Ramsey et al. 2002; Chen et al. 1997). Acetyl-coenzyme A is a key enzyme for the metabolic and synthetic pathway of soybean oil (Baud et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2003\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.5 \u003cem\u003eGmHAD1\u003c/em\u003e encoding a Haloacid dehalogenase-like hydrolase protein is the candidate gene for \u003cem\u003eqOil05-1\u003c/em\u003e\u003c/h2\u003e \u003cp\u003eThe \u003cem\u003eqOil05-1\u003c/em\u003e interval (Chr05-36558389 and Chr05-36594060) contains seven genes (Table S9). According to public RNA-seq data (Gmax_508_Wm82.a2.v1), only \u003cem\u003eGlyma.05G177000\u003c/em\u003e, \u003cem\u003eGlyma.05G177100\u003c/em\u003e, and \u003cem\u003eGlyma.05G177400\u003c/em\u003e show expression levels exceeding 3 FPKM during soybean grain development. Among them, \u003cem\u003eGlyma.05G177000\u003c/em\u003e (\u003cem\u003eGmHAD1\u003c/em\u003e) encodes a Haloacid dehalogenase-like hydrolase (\u003cem\u003eHAD\u003c/em\u003e) superfamily. Previous studies have shown that its homolog \u003cem\u003eAtHAD1\u003c/em\u003e in Arabidopsis plays a role in repressing the ABA response (Lee et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Notably, the ABA signaling pathway can regulate the expression of multiple key genes associated with grain oil biosynthesis, thereby influencing the final oil content in seeds (Zheng et al. \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; To et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Zhang et al. \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Further exploration of SNP and InDel variations in \u003cem\u003eGlyma.05G177000\u003c/em\u003e within 334 soybean accessions revealed 49 SNPs and 9 InDels across its 8.09 kb genomic region. Analysis of these variants showed that 40 markers were in the same linkage block, including three multi-year repeatable significant loci (Fig. S6). Further analysis of haplotypes in this genomic interval across 334 soybean accessions identified seven distinct haplotypes, categorized into two major clades (H1 and H2). Notably, H1 exhibited further differentiation into four subgroups, while H2 was divided into three subgroups. Analysis of protein sequences across haplotypes revealed no variation within H1 subgroup, but identified four divergent protein sequences between H1 and H2 (Fig. S7). Among 334 soybean accessions, H1 comprised 322 varieties, while H2 contained three subgroups: H2-1 with 10 varieties, H2-2 with 1 variety, and H2-3 with 1 variety. The expression of the six candidate genes was analyzed through qPCR for the \u003cem\u003eqOil05-1\u003c/em\u003e H1 and H2-1 during the seed-filling stage at 30, 40, and 50 days after flowering. Four genes (\u003cem\u003eGlyma.05G177000\u003c/em\u003e, \u003cem\u003eGlyma.05G177100\u003c/em\u003e, \u003cem\u003eGlyma.05G177200\u003c/em\u003e, and \u003cem\u003eGlyma.05G177400\u003c/em\u003e) were detected in soybean seed (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e4\u003c/span\u003e). \u003cem\u003eGlyma.05G177000\u003c/em\u003e (\u003cem\u003eGmHAD1\u003c/em\u003e) showed significantly different (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) expression between H1 and H2-1 at 50 days. Phenotypic comparisons (2020\u0026ndash;2023) showed significant or highly significant differences (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 or \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01) in oil and protein content between H1 and H2-1 (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e5\u003c/span\u003e). These data suggested that different haplotypes of \u003cem\u003eGmHAD1\u003c/em\u003e may influence grain oil and protein accumulation.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eSoybean seed oil and protein accumulation are complex quantitative traits governed by multiple genetic loci and modulated by environmental factors (Li et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Notably, the genetic architecture underlying these traits often vary across different cultivation regions. Advances in high-depth re-sequencing for SNP and InDel discovery, combined with GWAS, have significantly enhanced the precision of locus identification and candidate gene screening compared with traditional genetic mapping methods such as AFLP, RFLP, and SSR (Zhou et al. \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Kim et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Shao et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The northern region of China, serving as both the primary soybean production region and the cradle of soybean domestication and cultivation (Hao et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), harbors abundant soybean genetic resources. However, most of these resources remain genetically uncharacterized, creating a critical gap in leveraging their potential for complex trait dissection. To address this, we utilized a core collection of 334 soybean accessions from Northeast China, combining deep re-sequencing technology with GWAS, to identify QTNs for oil and protein content. This approach yielded 3,306,713 SNPs and 249,898 InDels across the entire genome, with average maker densities of 305.46 makers/kb and 23.08 makers/kb for SNPs and InDels, respectively.\u003c/p\u003e \u003cp\u003eOur GWAS analysis using a MLM model identified 115 significant SNPs and 101 significant InDels associated with oil and protein content (Tables S4-S5). Among these, 12 significant SNPs and 22 significant InDels were consistently detected across more than two years. Based on LD decay analysis, QTNs was defined as the genomic regions flanking significant SNP or InDel that explain more than 5% of the PVE in at least one environment (Wang et al. \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2016\u003c/span\u003eb). This led to the identification of five SNP-based and ten InDel-based QTNs that were consistently detected across multiple environments. Notably, eleven of these QTNs intervals overlapped with or were close to previously reported loci, while four represents novel discoveries (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eBased on linkage disequilibrium (LD) analysis, we prioritized candidate genes within the associated QTNs. The \u003cem\u003eqOil18-2\u003c/em\u003e contained \u003cem\u003eGlyma.18G283300\u003c/em\u003e, encoding a Protein Phosphatase 2C (PP2C). Given that PP2C activities can affect soybean seed size (Lu et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) a trait highly correlated with oil and protein content (Duan et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) this gene represents a promising candidate. Within the \u003cem\u003eqOil18-3\u003c/em\u003e, we identified the candidate gene \u003cem\u003eGlyma.18G295100\u003c/em\u003e, a bidirectional sugar transporter (\u003cem\u003eSweet9\u003c/em\u003e). As its homolog \u003cem\u003eGmSweet39\u003c/em\u003e has been shown to regulate oil and protein accumulation (Zhang et al. \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), \u003cem\u003eSweet9\u003c/em\u003e may exert a similar function. \u003cem\u003eGlyma.20G002301\u003c/em\u003e, encoding a zinc finger protein, was identified as a candidate gene associated with oil and protein content at \u003cem\u003eqOP20-1.\u003c/em\u003e Homologs of this gene, including \u003cem\u003eGmZF351\u003c/em\u003e and \u003cem\u003eGmZF392\u003c/em\u003e, have been demonstrated to influence soybean lipid biosynthesis. Similarly, \u003cem\u003eGlyma.20G010100\u003c/em\u003e, a tetratricopeptide repeat (TPR)-like superfamily protein gene, was characterized as a candidate gene specifically associated with oil content at \u003cem\u003eqOil20-1.\u003c/em\u003e While these genes represent the most likely candidate genes for each QTNs, their precise functions require further validation by future studies.\u003c/p\u003e \u003cp\u003eWithin the \u003cem\u003eqOil05-1\u003c/em\u003e, \u003cem\u003eGlyma.05G177000\u003c/em\u003e (\u003cem\u003eGmHAD1)\u003c/em\u003e emerged as a prime candidate, exhibiting expression levels exceeding 3 FPKM during soybean grain development. Previous studies have shown that \u003cem\u003eAtHAD1\u003c/em\u003e, an \u003cem\u003eHAD\u003c/em\u003e ortholog in \u003cem\u003eArabidopsis thaliana\u003c/em\u003e, represses the ABA response (Lee et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Importantly, alteration in ABA content affects the expression of multiple key genes involved in grain development and oil content in seeds (Zheng et al. \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; To et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Zhang et al. \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). \u003cem\u003eGmHAD1\u003c/em\u003e showed a\u0026thinsp;~\u0026thinsp;3.04-fold difference in expression level between H1 and H2-1 during the seed of oil accumulation stage at 50 days after flowering. Analysis of \u003cem\u003eGmHAD1\u003c/em\u003e haplotypes in the population revealed that H1 and H2-1 showed significant or highly significant differences in oil and protein content. Collectively, these findings suggest that \u003cem\u003eGmHAD1\u003c/em\u003e may be the promising candidate gene regulating seed oil and protein content in soybean.\u003c/p\u003e"},{"header":"5. Conclusions","content":"\u003cp\u003eIn this study, GWAS was employed to identify novel QTNs associated with seed oil and protein content in a northern China soybean core collection. Over four consecutive years of observation, 15 QTNs related to oil and protein were consistently detected in at least two years. Notably, a novel oil-related QTN, \u003cem\u003eqOil5-1\u003c/em\u003e, was identified across three years and harbored seven candidate genes. Among these, \u003cem\u003eGmHAD1\u003c/em\u003e emerged as a key novel candidate gene potentially involved in seed oil accumulation. Collectively, these findings provide valuable genetic loci and candidate genes for the molecular breeding of soybean cultivars with enhanced oil or protein content.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eCompeting Interests\u003c/h2\u003e \u003cp\u003eThe authors have no relevant financial or non-financial interests to disclose.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis work was supported by the Jilin Province Agricultural Science and Technology Innovation Project (Grant numbers: CXGC2024RCB001) and the Agriculture Science and Technology Major Project. D. Liu has received research support from Jilin Provincial Department of Human Resources and Social Security for scholarships.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eAll authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Dequan Liu, Jian Chen, Liantai Su, Mingwei Duan, Hao Li, Yunlong Hou, Zhengguo Cui, Liang Chen, Fuxin Li, Hongmei Qiu, and Yueqiang Wang. The first draft of the manuscript was written by Dequan Liu and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThis work was supported by the Jilin Province Agricultural Science and Technology Innovation Project (Grant numbers: CXGC2024RCB001) and the Agriculture Science and Technology Major Project. Dr. Liu gratefully acknowledges the Jilin Provincial Department of Human Resources and Social Security for scholarships.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e \u003cp\u003eThe datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAndreu V, Lagunas B, Collados R, Picorel R, Alfonso M (2010) The GmFAD7 gene family from soybean: identification of novel genes and tissue-specific conformations of the FAD7 enzyme involved in desaturase activity. Journal of experimental botany 61(12):3371\u0026ndash;3384\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBarton AB, Pekosz MR, Kurvathi RS, Kaback DB (2008) Meiotic recombination at the ends of chromosomes in Saccharomyces cerevisiae. 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Nature plants 8(3):233\u0026ndash;244\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"euphytica","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"euph","sideBox":"Learn more about [Euphytica](https://www.springer.com/journal/10681)","snPcode":"10681","submissionUrl":"https://submission.springernature.com/new-submission/10681/3","title":"Euphytica","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Soybean, Oil, Protein, GWAS, Northeast China","lastPublishedDoi":"10.21203/rs.3.rs-8169697/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8169697/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eSeed oil and protein content in soybean [\u003cem\u003eGlycine max\u003c/em\u003e (L.) Merr.] are complex quantitative traits controlled by multiple genes and highly susceptible to environmental influences. To investigate the genetic basis of these traits in Northern China, a major soybean-producing area, we re-sequenced 334 core accessions of soybean landraces and elite cultivars from this area. Based on phenotypic data collected over multiple years, a subsequent SNP/InDel-based GWAS for seed oil and protein content identified fifteen quantitative trait nucleotides (QTNs) significantly associated with the traits. Noticeably, \u003cem\u003eqOil05-1\u003c/em\u003e was consistently detected across three years, accounting for 4.8\u0026thinsp;~\u0026thinsp;14.46% of phenotypic variance (PVE). Within the confidence interval of \u003cem\u003eqOil05-1\u003c/em\u003e, we identified \u003cem\u003eGmHAD1\u003c/em\u003e, a gene encoding a haloacid dehalogenase-like hydrolase, as a strong candidate gene. \u003cem\u003eGmHAD1\u003c/em\u003e expression differed substantially (~\u0026thinsp;3.04 fold) between the two major haplotypes (H1 and H2\u003cem\u003e-\u003c/em\u003e1). Further analysis confirmed that the two major haplotypes of \u003cem\u003eGmHAD1\u003c/em\u003e showed significant or highly significant differences in seed oil and protein content. Overall, our findings offer valuable information into the genetic mechanisms underlying oil and protein accumulation in soybean, providing guidance for future genetic improvement of soybean quality.\u003c/p\u003e","manuscriptTitle":"SNP/InDel-Based GWAS Reveals QTNs and Candidate Genes for Seed Oil and Protein Content in Northern China Soybean Core Accessions","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-19 17:17:04","doi":"10.21203/rs.3.rs-8169697/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-01-26T11:41:21+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-23T13:22:42+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"15317219666387449935653371383861623420","date":"2026-01-17T09:10:07+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-16T11:10:06+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"107697421203933896971322288025921511112","date":"2026-01-13T14:00:19+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"149173503660664573621958584438301240834","date":"2025-12-19T11:29:48+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-12-17T11:18:10+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-11-22T01:47:15+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-11-22T01:46:08+00:00","index":"","fulltext":""},{"type":"submitted","content":"Euphytica","date":"2025-11-21T05:01:02+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"euphytica","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"euph","sideBox":"Learn more about [Euphytica](https://www.springer.com/journal/10681)","snPcode":"10681","submissionUrl":"https://submission.springernature.com/new-submission/10681/3","title":"Euphytica","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"422946d6-fbc7-45e8-960b-966d2595b431","owner":[],"postedDate":"December 19th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-03-02T16:00:17+00:00","versionOfRecord":{"articleIdentity":"rs-8169697","link":"https://doi.org/10.1007/s10681-026-03694-w","journal":{"identity":"euphytica","isVorOnly":false,"title":"Euphytica"},"publishedOn":"2026-02-23 15:57:18","publishedOnDateReadable":"February 23rd, 2026"},"versionCreatedAt":"2025-12-19 17:17:04","video":"","vorDoi":"10.1007/s10681-026-03694-w","vorDoiUrl":"https://doi.org/10.1007/s10681-026-03694-w","workflowStages":[]},"version":"v1","identity":"rs-8169697","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8169697","identity":"rs-8169697","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}