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Article

QTLs Mapping and Identification of Candidate Genes Associated with Stachyose and Sucrose in Soybean (Glycine max L.)

by
Chuanrong He
1,
Yipu Wang
1,
Changning Li
1,
Yue Yang
1,
Qian You
1,
Aiqin Yue
1,
Jingping Niu
2,
Lixiang Wang
1,3,
Weijun Du
1 and
Min Wang
1,*
1
College of Agriculture, Shanxi Agricultural University, Jinzhong 030801, China
2
College of Life Sciences, Shanxi Agricultural University, Jinzhong 030801, China
3
Houji Labortary in Shanxi Province, College of Agriculture, Shanxi Agricultural University, Jinzhong 030801, China
*
Author to whom correspondence should be addressed.
Agronomy 2025, 15(4), 972; https://doi.org/10.3390/agronomy15040972
Submission received: 17 February 2025 / Revised: 5 April 2025 / Accepted: 15 April 2025 / Published: 17 April 2025
(This article belongs to the Section Crop Breeding and Genetics)
Browse Figures
Versions Notes

Abstract

:
Soluble sugars are essential components in the physiology and metabolism of soybeans (Glycine max), playing a critical role in regulating key processes such as development, germination, and flavor formation. The soluble sugar content in soybean seeds is primarily composed of stachyose, raffinose, sucrose, and glucose. This study aims to elucidate the genetic mechanisms underlying variation in the composition of soluble sugars in soybean seeds. A 128 recombinant inbred line (RIL) population was used, and concentrations of these four sugars were quantified across three years (2015, 2016, and 2017 in Shanxi). The analysis revealed that Jin Da 53 exhibited significantly higher sucrose and total sugar contents compared to Ping Nan, while stachyose levels were notably elevated in Ping Nan. Except for glucose content in 2017 and 2019, the RIL population’s traits all exhibited a normal distribution, making it suitable for QTL analyses. A total of twenty QTLs were identified for the four sugar components: five for glucose, four for raffinose, four for sucrose, three for stachyose, and four for total sugar, all with LOD > 2.5. Notably, three QTLs located on chromosome 10 (S10_37101443-S10_38298307, S10_38681635-S10_39134900, and S10_36697685-S10_36697916) were found to be associated with stachyose content, identifying one candidate gene, Glyma.10g154400, which was implicated in carbohydrate metabolic processes; a QTL located on chromosome 11 (96.881–105.5 cM) was identified in 2019 as a significant locus influencing sucrose content, identifying another candidate gene, Glyma.11g136200, which was linked to sugar/inositol transporter activity. Expression analysis of these candidate genes demonstrated the Glyma.10g154400 gene exhibited higher expression levels in varieties with lower stachyose content, whereas Glyma.11g136200 showed increased expression in lines with elevated sucrose levels. This study provides an important genetic basis for the breeding of soybean varieties with increased sugar content.

1. Introduction

Soybean (Glycine max L.), as a diploid plant with 20 pairs of chromosomes (2n = 40), is a globally essential crop valued for its high oil and protein content. Originating in China, it serves as a critical source of food for humans and feed for livestock worldwide. The seeds of soybeans contain approximately 40% protein, 20% oil, and 33% carbohydrates [1,2,3,4,5], underscoring their significant nutritional value and versatility in agricultural and food systems. Soluble sugars, which are a key component of carbohydrates, primarily consist of glucose, sucrose, raffinose, and stachyose [6]. The proportion of sugar components in soybean seeds significantly influences the quality, digestibility, and nutritional values of soy-based foods. Sucrose is known to enhance cognitive function [7]. Raffinose contributes to physiological functions, including immune system enhancement [8], whereas stachyose has been demonstrated to promote gut health [9].
A substantial body of research has investigated the relationship between the diverse compositions of soluble sugars in soybeans. For instance, the soluble sugar content of various soybean varieties was analyzed, revealing significant correlations among sucrose, raffinose, and stachyose [2]. A positive correlation was observed between sucrose content and fat content, whereas a negative correlation was identified with protein content [2]. Similarly, a negative correlation was reported between protein content and the total amounts of raffinose and stachyose [10]. Furthermore, highly significant correlations were demonstrated between the total oligosaccharide content and the levels of raffinose and stachyose, as well as between raffinose and stachyose themselves [11]. Collectively, these findings indicate that research on soluble sugar compositions has predominantly focused on phenotypic traits.
A variety of genetic approaches have been used to identify QTLs or genes associated with seed traits, including linkage mapping [12,13,14,15], genome-wide association studies (GWAS) [16,17,18,19], and bulked segregant analysis (BSA) [20,21,22]. Hundreds of QTLs associated with agronomic and qualitative traits have been mapped across nearly all soybean chromosomes. However, only a limited number of studies have focused on the genetic basis and QTL analysis of soluble sugars in soybean seeds [23,24]. Reduced-representation genome sequencing is a high-throughput sequencing technology that employs enzymatic cleavage to fragment the genome and sequence specific regions, thereby simplifying genomic complexity. Molecular markers developed through reduced-representation genome sequencing (SNP, SLAF-seq, etc.) offer significant advantages over traditional molecular markers (RFLP, SSR, AFLP) derived from techniques such as molecular hybridization or PCR. Unlike traditional methods, reduced-representation genomic technology allows for the identification of thousands of variant sites in a single sequencing run while simultaneously facilitating the genotyping of multiple individuals during marker development. Consequently, reduced-representation genome sequencing demonstrates superior efficiency and cost-effectiveness compared to conventional approaches [25]. Previous studies have reported the use of reduced-representation genomic techniques for QTL mapping of various plant traits. For example, 13 quantitative trait nucleotides (QTN) and several candidate genes associated with total soluble sugar content in soybean seeds were identified using SLAF-seq [24]. Similarly, two stable QTL regions for seed-related traits on chromosomes 2 and 16 in peanuts were detected based on a high-density genetic map (HDGM) constructed using SLAF-seq and SSRs [26]. In another study, SLAF-seq was utilized for linkage mapping of leaflet traits in soybeans, resulting in the identification of 32 QTLs, including 17 novel QTLs [27]. Additionally, 181 recombinant inbred lines (RILs) derived from a cross between wild soybean (ZYD00463) and cultivated soybean (WDD01514) were genotyped using SLAF-seq, leading to the identification of 24 stable QTLs associated with seed oil content and composition across multiple environments [28]. Furthermore, 72 QTLs linked to fructose, glucose, sucrose, stachyose, and raffinose, as well as 14 QTLs associated with soluble sugars, were identified using SNP markers [29]. Collectively, these studies highlight the efficacy of reduced-representation genome sequencing for molecular marker-assisted breeding.
Studies on the QTL mapping of soluble sugar components remain relatively limited [30]. For instance, 11 QTLs associated with sugar composition were identified, including one glucose QTL, three fructose QTLs, three sucrose QTLs, two raffinose QTLs, and two stachyose QTLs, using a recombinant inbred line (RIL) population derived from V97-3000 × F2:3 of V99-5089 [31]. In another study, a population of 92 F5:7 RILs derived from MD965722 × Spencer was used to detect 14 significant QTLs on eight chromosomes (1, 3, 6, 9, 12, 14, 15, and 16), which were associated with sugar components, including three sucrose QTLs, seven raffinose QTLs, and four stachyose QTLs [32]. Although these studies have explored QTL mapping for individual soluble sugar components, the number of loci identified for components other than sucrose remains limited. Therefore, further investigation into QTLs associated with the content of individual soluble sugar components is of significant value.
In this study, an RIL population was developed by crossing the soybean cultivar ‘Jin Da 53’ with the wild soybean ‘Ping Nan’, followed by successive selfing. The soluble sugar composition of the seeds was quantified using high-performance liquid chromatography (HPLC) over multiple years under natural environmental conditions. Subsequently, SLAF-seq was utilized to construct a high-density genetic map (HDGM), enabling the identification of quantitative trait loci (QTLs) associated with soluble sugar compositions in soybean seeds and the discovery of potential candidate genes. This study establishes a foundation for further investigation into the genetic mechanisms regulating soluble sugar composition and provides a scientific basis for improving soybean quality traits.

2. Materials and Methods

2.1. Plant Materials

The soybean cultivar ‘Jin Da 53’ (Shanxi Agricultural University., Jinzhong, China), used as the maternal parent in this study, is a high-performing variety developed by Shanxi Agricultural University. This variety features oval grains, a yellow seed coat, a yellow hilum, a brown umbilicus, and a 100-grain weight ranging from 20 to 22 g. The wild soybean ‘Ping Nan’ (Shanxi Agricultural University, Pingyao, China), utilized as the paternal parent, was collected in Pingyao County, Shanxi Province. It is characterized by oval seeds, a mud film, a black seed coat, a yellow hilum, a brown umbilicus, and a 100-grain weight of 2 to 3 g. An RIL population consisting of 128 lines was created by crossing ‘Jin Da 53’ and ‘Ping Nan’, followed by six generations of successive selfing, within-line mixing, and seed harvesting. The plants were cultivated in 2017, 2018, and 2019, with seeds harvested at maturity for trait analysis.

2.2. Evaluation of the Content of Soluble Sugar Composition

Quantitative analysis was carried out using an Agilent 1260 high-performance liquid chromatograph equipped with a ZORBAX SB-carbohydrate column (5 µm, 25 cm × SB-carb) (Chengdu Mole Scientific Instrument CO., Chengdu, China). The mobile phase consisted of a 70% acetonitrile solution, delivered at a constant flow rate of 1.0 mL/min. The column temperature was maintained at 30 °C, and the reference cell temperature was set to 35 °C. An injection volume of 20 µL was used, with a total run time of 30 min. Standard solutions of stachyose, sucrose, raffinose, and glucose were prepared to generate a calibration curve, which enabled the accurate quantification of individual sugar concentrations in the samples. Measurement was performed at 0.15 g per sample and repeated 3 times.

2.3. Whole-Genome Re-Sequencing Analysis

Genomic DNA (gDNA) was extracted from fresh leaves of ‘Jin Da53’ and ‘Ping Nan’ using the cetyltrimethylammonium bromide (CTAB) method [33]. Whole-genome resequencing was performed following standard protocols provided by Bemac Biotechnology, which included sample quality assessment, library construction, and sequencing.

2.4. High-Density Genetic Linkage Map Construction

A high-density molecular marker map was constructed for genetic mapping in the soybean RIL population using Specific-Locus Amplified Fragment Sequencing (SLAF-seq) technology developed by BioMaker Technologies [34] and HighMap software [35]. Genetic distances were calculated using the Kosambi mapping function.

2.5. QTL Mapping Analysis

The QTL IciMapping V4.2 software was employed to perform composite interval mapping for glucose, sucrose, raffinose, and stachyose traits within the RIL population. A likelihood ratio (LR) of 11.50, equivalent to a LOD score of 2.5, was used as the threshold for QTL localization analysis.

2.6. Candidate Gene Mining and Sequence Alignment

Based on QTL analyses of soluble sugar composition across three years, genes located within the candidate QTL intervals were annotated using the Phytozome V13 database (https://phytozome-next.jgi.doe.gov/ (accessed on 10 November 2024)) and sequence information from the corresponding genomic regions. Candidate genes related to soluble sugar metabolism were then selected for further analysis. Cloning and sequencing were performed on the parental lines ‘Jin Da53’ and ’Ping Nan’ to obtain CDS of the candidate genes, from which the corresponding amino acid sequences were derived. The amino acid sequences of homologous genes from different species were analyzed using MEGA 11 Software. The encoded amino acid sequences were predicted using SWISS-MODEL (https://swissmodel.expasy.org/ (accessed on 10 November 2024)), which was also employed to assess whether any mutations led to changes in the translated amino acid sequences or the tertiary structure of the proteins.

2.7. Real-Time Quantitative PCR Analyses of Candidate Genes

Based on three years of data regarding the soluble sugar content in 128 soybean varieties and parental lines of the RIL population, two parental lines were selected: one with high sucrose content, one with low sucrose content, one with high stachyose content, and one with low stachyose. Primers were designed for these lines, and quantitative RT-qPCR analysis was conducted (Table S7). Each sample was analyzed in triplicate to ensure accuracy and reproducibility.

2.8. Statistics and Analysis

The data in this study were processed and analyzed using Microsoft Excel 2007 and IBM SPSS Statistics 22 software. All experiments were conducted with three biological replicates, and the assumptions for analysis of variance (ANOVA) were verified through the Shapiro–Wilk normality test and Levene’s test for homogeneity of variance.
Statistical significance was determined by the LSD multiple comparison test. Letter notation: Groups sharing the same lowercase letter (e.g., a) indicate no significant difference (p > 0.05). Groups labeled with adjacent lowercase letters (e.g., ab) show no significant difference with either group a or b (p > 0.05), but a significant difference exists between groups a and b (p < 0.05). Groups with different lowercase letters (e.g., a, b) represent statistically significant differences (p < 0.05); Asterisk notation: * indicates significant difference (p < 0.05), and ** indicates highly significant difference (p < 0.01). Error bars in figures represent ± standard error of the mean (SEM). All significance notations were based on statistical analysis at a 95% confidence level.

3. Results

3.1. Phenotyping of Soluble Sugar Composition in Parents and RIL Individuals

To identify novel QTLs or genes regulating soybean soluble sugar composition, an RIL population comprising 128 lines was developed by crossing the soybean cultivar ‘Jin Da 53’ with the wild soybean ‘Ping Nan’, followed by six generations of successive selfing. Over a three-year period (2017, 2018, and 2019), the contents of various soluble sugar components in the RIL population were quantified. The results demonstrated that the sucrose and total sugar contents were significantly higher in the maternal parent (Jin Da 53) than in the paternal parent (Ping Nan). In contrast, the stachyose content was markedly higher in the paternal parent (Ping Nan) compared to the maternal parent (Jin Da 53). These trends remained consistent across the three-year study period. However, no significant differences were observed in the glucose and raffinose contents between the two parental lines (Table 1). The analysis of soluble sugar components in the RIL population revealed that the mean values for most traits were intermediate between the two parental lines, with the presence of transgressive segregation indicating significant variability. Furthermore, the large coefficients of variation for glucose and raffinose suggest substantial potential for genetic improvement of these traits within the population (Table 1). Except for glucose content in 2017 and 2019, all traits exhibited a normal distribution, which supports further QTL mapping analysis (Figure 1).
The results of the ANOVA indicated significant variation between years, as well as notable year × material interactions concerning raffinose content. In contrast, no significant differences were observed for other traits (Table S1). This indicates that raffinose content is highly susceptible to environmental influences and demonstrates varying adaptability across different environments. Consequently, the maximum potential of raffinose content can only be achieved under optimal environmental conditions. In contrast, the levels of other soluble sugar components are less affected by environmental factors, exhibiting minimal genotype × environment interactions. These findings underscore the importance of prioritizing genetic improvement in research efforts.
The correlation analysis of soluble sugars and their components within the population revealed that all four soluble sugars—sucrose, raffinose, stachyose, and glucose—showed highly significant positive correlations with total sugar content, a trend consistent across all three years. Additionally, a negative correlation was observed between glucose and stachyose content, reaching a highly significant level in 2017 and a significant level in 2018. Sucrose content exhibited a significant positive correlation with raffinose and stachyose content in 2017 and 2019; however, this correlation was not significant in 2018. Furthermore, the contents of raffinose and stachyose displayed a highly significant positive correlation in 2017 and 2018, but this relationship was not significant in 2019 (Table 2, Table 3 and Table 4).

3.1.1. Heredity Analysis of Soluble Sugar Traits in Soybean Seeds in Main Gene + Multigene Mixed Model

The soluble sugar components of the RIL population derived from the ‘Jin Da 53’ × ‘Ping Nan’ cross were analyzed using a mixed genetic model that incorporates both major genes and polygenes for plant quantitative traits. The Akaike Information Criterion (AIC) values were calculated for five genetic models: polygenic, one major gene, two major genes, three major genes, and four major genes (Table S2). For each trait combination, the two models with the lowest AIC values were selected as candidate models.
In the 2019 RIL population, the models 0MG and 2MG-CE exhibited the lowest AIC values for glucose and were thus deemed suitable for investigating glucose inheritance. For the 2018 RIL population, the models 2MG-ER, 2MG-AE, and 2MG-CE showed the lowest AIC values, making them appropriate for analyzing glucose inheritance. Similarly, in the 2017 RIL population, the models 2MG-CE and 4MG-AI demonstrated the lowest AIC values for glucose, indicating their suitability for studying glucose inheritance (Table S2). For sucrose, the 2MG-AE and 2MG-CE models exhibited the lowest AIC values in the 2019 RIL population, thus identifying them as suitable for modeling sucrose inheritance. In the 2018 RIL population, the 0MG and 2MG-CE models displayed the lowest AIC values for sucrose and were selected as appropriate for analyzing sucrose inheritance. Similarly, in the 2017 RIL population, the 0MG, 2MG-DE, and 2MG-IE models demonstrated the lowest AIC values for sucrose, indicating their suitability for studying sucrose inheritance (Table S2). For raffinose, the 3MG-AI and 2MG-CE models exhibited the lowest AIC values in the 2019 RIL population, indicating their suitability for modeling raffinose inheritance. In the 2018 RIL population, the 2MG-ER and 2MG-CE models also displayed the lowest AIC values for raffinose, making them appropriate for analyzing raffinose inheritance. Similarly, in the 2017 RIL population, the 0MG and 2MG-CE models demonstrated the lowest AIC values for raffinose, further suggesting their effectiveness in studying raffinose inheritance (Table S2). For stachyose, the 4MG-AI and 2MG-CE models exhibited the lowest AIC values in the 2019 RIL population, indicating their suitability for modeling stachyose inheritance. In the 2018 RIL population, the 0MG and 2MG-CE models also displayed the lowest AIC values, making them appropriate for analyzing stachyose inheritance. Similarly, in the 2017 RIL population, the 0MG, 2MG-DE, and 2MG-IE models demonstrated the lowest AIC values, further confirming their effectiveness in studying stachyose inheritance (Table S2).

3.1.2. Genetic Model Fitness Test for Various Soluble Sugar Traits in Soybean Seeds

The candidate models for various soluble sugar compositions were evaluated over multiple years. The model with the lowest AIC value, along with the fewest statistically significant levels, was identified as the optimal genetic model. Candidate models for various soluble sugar compositions were evaluated over multiple years. The model with the lowest AIC value and the fewest statistically significant levels was identified as the optimal genetic model. Our findings revealed that the models for sucrose in 2019, as well as glucose, sucrose, raffinose, and stachyose in 2018, and raffinose in 2017, did not achieve statistical significance based on the mean test results. Among these, the 2MG-CE model exhibited the lowest AIC value, making it the most suitable choice. For glucose in 2019, one test result reached statistical significance; however, the 0MG model, with its lower AIC value, was deemed more appropriate. Similarly, the raffinose model in 2019 did not yield statistically significant results, but the 3MG-AI model was preferred due to its lower AIC value. For stachyose in 2019, although no significant results were obtained, the 4MG-AI model was identified as the most suitable based on its lower AIC value. Furthermore, for sucrose and stachyose in 2017, the 0MG model was considered more appropriate despite the lack of significant test results. Lastly, the glucose model in 2017 also did not present significant results; however, the 4MG-AI model was favored due to its smaller AIC value (Table S3).

3.1.3. Estimation of Genetic Parameters for Soluble Sugar Traits in Soybean Seeds

Under the optimal genetic model, we calculated parameters for soluble sugar traits. The inheritance of raffinose was governed by two pairs of major genes in two out of three years. Specifically, in 2017 and 2018, the varietal-environment interaction effects were 1.2176 and 0.886, respectively, while the heritability of the major genes was 32.7648% and 46.8152%. Similarly, the inheritance of sucrose was controlled by two pairs of major genes in two out of three years, with varietal-environment interaction effects of 4.4152 and 6.0772 in 2018 and 2019, respectively, and heritabilities of the major genes of 40.1834% and 36.791% (Table 5).
In 2019, the inheritance of raffinose content was modeled using a 3MG-AI epistatic effect model, which revealed an additive effect of 4.2527, a varietal-environment interaction effect of 2.0075, and a main gene heritability of 78.9812%. In contrast, a 4MG-AI epistatic effect model for stachyose in 2019 indicated an additive effect of 3.7659, a varietal-environment interaction effect of −0.1103, and a main gene heritability of 97.7313% (Table 5).
In 2018, the inheritance of glucose content was influenced by two pairs of primary genes, exhibiting a varietal-environment interaction effect of 1.3564 and a heritability of 61.83%. Similarly, the inheritance of stachyose content in 2018 was governed by two pairs of primary genes, with a varietal-environment interaction effect of 4.2367 and a heritability of 38.51% (Table 5).
In 2017, a 4MG-AI epistatic effect model was employed to analyze glucose content, revealing an additive effect of 2.3537, a varietal-environment interaction effect of 1.5349, and a main gene heritability of 95.5996% (Table 5).

3.2. SLAF-Seq Library Construction

The reference genome sequence (Glycine max Cv. Williams 82.a2.v1) was analyzed using the enzyme HaeIII for digestion. Sequences with fragment lengths ranging from 264 to 364 bp were classified as specific-locus amplified fragment (SLAF) tags, resulting in a predicted total of 114,027 SLAF tags. Notably, 7.11% of these SLAF tags were found within repetitive sequence regions, and their distribution across the chromosomes was relatively uniform, demonstrating the effectiveness of the enzyme digestion strategy.
Following sequencing, a total of 52.02 Gb of raw data were generated, comprising 260,187,738 reads. The average GC content was 41.04%, and the average Q30 ratio was 89.24%. Ultimately, 140,864 SLAF tags were identified, which included 44,298 polymorphic SLAF tags and 33,742 tags suitable for genetic map construction, resulting in an effective polymorphism rate of 23.95% among the parental samples. To ensure the accuracy of genetic mapping, the polymorphic SLAF tags were filtered, yielding 8223 high-quality SLAF tags for mapping purposes.

3.3. Construction of the High-Density Genetic Map

In the construction of the HDGM, a total of 8266 markers (8223 SLAF tags and 43 Simple Sequence Repeats (SSRs)) were assigned to 20 linkage groups (LGs) (Figure 2; Table 6 and Table S4). Each chromosome corresponds to a specific linkage group, and the linear arrangement of markers within each group was determined using HighMap software, which analyzed the linkage groups as individual units. The genetic distance between adjacent markers was calculated, resulting in a genetic map with a total length of 3148.46 cM, an average marker distance of 0.81 cM, and a total of 7945 markers (Table 6). Chromosome 18 contained the highest number of markers, totaling 740, while chromosome 1 had the fewest, with only 57 markers. Chromosome 20 exhibited the largest total map distance of 209.99 cM, encompassing 668 markers and yielding an average genetic marker distance of 0.31 cM. In contrast, chromosome 16 displayed the smallest total map distance of 92.51 cM, comprising 28 markers with an average genetic marker distance of 3.43 cM (see Table 6).

3.4. QTL Analysis of Soluble Sugar Composition in Soybean

QTL mapping for soluble sugar and its component contents in soybean seeds was conducted using composite interval mapping. Over three years, a total of 20 QTLs associated with soluble sugar were identified, distributed across 11 chromosomes: 1, 2, 3, 4, 6, 8, 10, 11, 12, 13, 17, and 20 (see Figure 3 and Table 7).
In the three-year RIL population, three QTL associated with stachyose were identified, all localized to chromosome 10 within similar positional intervals. These loci exhibited LOD values ranging from 4.0954 to 5.5375, accounting for up to 13.9% of the maximum genetic variation. The positive additive effects suggest that the efficiency-enhancing alleles at these loci were primarily derived from the parent ‘Ping Nan’ (Table 7).
Additionally, four QTL associated with sucrose were identified within the same RIL population, distributed across three chromosomes: 3, 11, and 13, all exhibiting LOD values exceeding 2.5. Notably, the locus on chromosome 11 accounted for the largest proportion of genetic variation, at 12.89%. QTL analysis indicated that only the additive effect on chromosome 11 was positive, suggesting that the beneficial alleles at this locus were primarily inherited from the parent ‘Ping Nan’. In contrast, the loci on the other chromosomes exhibited negative additive effects, indicating that the beneficial alleles at these loci were mainly derived from the parent ‘Jin Da 53’ (Table 7).
A total of five QTL associated with glucose content were identified in the three-year RIL population, distributed across five chromosomes: 1, 2, 12, 17, and 20, all exhibiting LOD values exceeding 2.5. Notably, the locus on chromosome 20 accounted for the highest proportion of genetic variation, at 10.94%. QTL analysis revealed that the additive effects on chromosomes 1 and 12 were positive, suggesting that the beneficial alleles at these loci were predominantly inherited from the parent ‘Ping Nan’. In contrast, the additive effects on the remaining chromosomes were negative, indicating that the beneficial alleles at these loci primarily originated from the parent ‘Jin Da 53’ (see Table 7).
Similarly, four QTL associated with raffinose content were identified in the three-year RIL population, distributed across four chromosomes, each exhibiting LOD values exceeding 2.5. Among these loci, the one on chromosome 6 accounted for the highest proportion of genetic variation, at 10.98%. QTL analysis indicated a positive additive effect on chromosome 2, suggesting that the beneficial alleles at this locus were primarily inherited from the parent ‘Ping Nan’. In contrast, the additive effects on the other chromosomes were negative, indicating that the beneficial alleles at these loci were mainly derived from the parent ‘Jin Da 53’ (Table 7).
Furthermore, four QTL associated with total sugar content were identified in the three-year RIL population, distributed across four chromosomes, all exhibiting LOD values exceeding 2.5. The locus on chromosome 8 accounted for the highest proportion of genetic variation, at 10.48%. QTL analysis revealed positive additive effects across all chromosomes, indicating that the beneficial alleles at these loci were primarily inherited from the parent ‘Ping Nan’ (Table 7).

3.5. Screening of Candidate QTLs for Soluble Sugars and Their Components in Soybean Seed

QTL analysis of soybean seed soluble sugar composition revealed that loci associated with stachyose content were consistently mapped to the genomic interval of 72.231 cM to 87.740 cM on chromosome 10 across all three years of the study. Specifically, these loci include the regions S10_37101443–S10_38298307, S10_38681635–S10_39134900, and S10_36697685–S10_36697916, all located on chromosome 10. Additionally, a locus exhibiting significant genetic variation in sucrose content was identified in 2019, localized to the interval of 96.881 cM to 105.5 cM on chromosome 11 (Table 8). These genomic intervals represent potential candidate regions for further investigation into the genetic mechanisms underlying soluble sugar composition in soybean seeds.

3.6. Characterization of Candidate Genes Associated with the Soluble Sugar Component QTLs in Soybean

To elucidate the genetic mechanisms underlying soluble sugar composition in soybean seeds, we sought to identify candidate genes within the quantitative trait locus (QTL) intervals associated with soluble sugar and its components. Gene annotations were performed using the soybean reference genome (Glycine max Wm82.a2.v1) and sequence information from the targeted genomic regions (Tables S5 and S6). Within the interval of 72.23–87.74 cM on chromosome 10, a total of 151 genes were identified, of which 104 exhibited polymorphism between the two parental lines. Among these, one gene, Glyma.10g154400, was implicated in carbohydrate metabolic processes, suggesting its potential role in soluble sugar synthesis (Table 9). Similarly, within the interval of 96.88–105.5 cM on chromosome 11, 138 genes were identified, with 76 showing polymorphism between the parental lines. Notably, three genes were associated with soluble sugar; however, only one gene, Glyma.11g136200, which is linked to sugar/inositol transporter activity, exhibited polymorphism between the parental lines (Table 9).
To further validate the candidate genes associated with seed soluble sugar components, we analyzed the relative expression levels of the candidate genes in parental lines and RILs with high and low stachyose/sucrose content. As shown in Figure 4, the expression pattern of Glyma.10g154400 in soybean seeds correlates with stachyose content. The expression of Glyma.10g154400 in the low stachyose-containing line G161 and Ping Nan seeds was significantly higher than that in the high stachyose-containing line G169 and Jin Da 53 seeds (Figure 4A). Similarly, the expression pattern of Glyma.11g136200 in soybean seeds is consistent with sucrose content. The expression of Glyma.11g136200 in the high sucrose-containing line G81 and Jin Da 53 was significantly higher than in the low sucrose-containing line G136 and Ping Nan (Figure 4B). These results further evidence that Glyma.10g154400 may be negatively correlated with stachyose content, while Glyma.11g136200 may be positively correlated with sucrose content.
Sequence comparisons of gene Glyma.10g154400 revealed that the 1649th base was mutated from A to G in the ‘Jin Da 53’ (JD53) variety of soybeans, resulting in the 550th amino acid being mutated from histidine to arginine (Figure 5A). Further tertiary structure predictions indicated that this amino acid mutation did not significantly affect the 3D structure of the protein (Figure 5C). Phylogenetic analysis of the amino acids showed that Glyma.10g154400 and its homologues in other plants formed a closely related legume clade with the homologues from Medicago sativa, Lotus japonicus, and common bean. Additionally, amino acid alignments demonstrated that the mutation occurs in motif 4, and the mutated amino acids were found to be not conserved among other homologues (Figure 5E and Figure S1).
Sequence comparisons of the gene Glyma.11g136200 revealed that the 608th base was mutated from T to G in the ‘Ping Nan’ (PN) variety of soybeans, resulting in the 203rd amino acid being mutated from isoleucine to arginine (Figure 5B). Further tertiary structure predictions indicated that this amino acid mutation did not significantly affect the 3D structure of the protein (Figure 5D). Phylogenetic analysis of the amino acids showed that Glyma.11g136200 and its homologues in other plants formed a closely related legume clade with homologues from Medicago sativa, Lotus japonicus, and common bean. Additionally, amino acid alignments demonstrated that the mutation occurs in motif 7, and the mutated amino acids were found to be conserved among other homologues (Figure 5F and Figure S2).

4. Discussion

Soluble sugar is not only a critical indicator of carbohydrate metabolism in plants but also exhibits beneficial effects on human health, particularly in improving the micro-ecological environment within the human body, enhancing immune function, and promoting the absorption of calcium and other essential minerals. In this study, we analyzed the soluble sugar composition of seeds using RIL populations. The results demonstrated that the content of each soluble sugar in soybean seeds exhibited significant phenotypic variation across RIL populations from different years, as indicated by large coefficients of variation. This phenomenon is consistent with previous studies on complex traits in crops, where variations in quantitative traits are primarily attributed to multiple genetic factors [36,37,38,39]. Such genetic diversity plays a crucial role in crop improvement, as it enhances the potential for trait optimization.
Furthermore, correlation analysis revealed significant relationships between total soluble sugar content and its four components. Specifically, glucose showed a negative correlation with stachyose, while sucrose, raffinose, and stachyose exhibited positive correlations with each other. These findings are partially supported by Qi et al. [40], who reported a high correlation between sucrose and total soluble sugar content, and raffinose and stachyose influenced total soluble sugar accumulation. In 2017 and 2019, sucrose content was significantly and positively correlated with the contents of raffinose and stachyose; however, in 2018, this correlation was not significant. Hu et al. [29] reported that sugar metabolism is fundamental to all organisms, and the components of sugar are interchangeable. Specifically, fructose and glucose combine to form sucrose, while raffinose and stachyose are derived from sucrose. This explains the positive correlation between sucrose, raffinose, and stachyose. The synthesis of sucrose may lead to increased production of raffinose and stachyose. However, soluble sugar content can vary significantly across different environments [41], which may account for the inconsistent correlations among soluble sugar components over the three years.
Through QTL mapping of soluble sugar content and composition in soybean seeds, the analysis consistently identified a stachyose-related QTL on chromosome 10 across three consecutive years, while a sucrose-related QTL was localized on chromosome 11. Leveraging the sequence information from these QTL regions, gene annotation analysis revealed one candidate gene associated with soluble sugar synthesis within the 72.231–87.74 cM interval on chromosome 10 and another within the 96.88–105.5 cM interval on chromosome 11. As a result, two candidate QTL regions linked to soybean soluble sugars were successfully identified: the candidate gene Glyma.10g154400, implicated in stachyose synthesis, was located within the genomic interval S10_38681635-S10_39134900 (Chr10), while the candidate gene Glyma.11g154400, associated with sucrose synthesis, was mapped to the interval S11_10267062-S11_10446348 (Chr11). Both QTL intervals demonstrated a contribution rate exceeding 10%.
The QTL mapping of soluble sugars in soybean has been partially explored in previous studies. Xu et al. [23] conducted a genome-wide association study (GWAS) using 264 vegetable soybean germplasm across two environments, identifying five significant associations involving 27 SNPs and nine candidate genes potentially regulating sugar content in vegetable soybean seeds. In another study, Lu et al. [24] performed a GWAS analysis on 278 diverse soybean germplasm and detected 13 quantitative trait nucleotides (QTNs) associated with total soluble sugar content. They reported that Glyma.02G293200 and Glyma.02G294900 were positively correlated with soluble sugar content, while Glyma.02G294000 exhibited a negative correlation. Furthermore, Glyma.02G294000 was highlighted as a key candidate gene influencing soluble sugar content. Liu et al. [42] used the QTL-Sequencing method to identify 16 QTLs affecting sucrose and soluble sugar, including a major sucrose-associated QTL, qSU1901, with 10.6–13.2% phenotypic variation for sucrose. Kim et al. [43] used 115 segregating F10 lines and 117 F2:10 RILs to conduct molecular mapping with SSR markers and detected six QTLs for oligosaccharides and sucrose on six chromosomes (2, 11, 12, 16, and 19). Additionally, Jamison et al. [44] employed RIL populations derived from R08-3221 and R07-2000 to map two QTLs related to sucrose content in soybean, located on chromosomes 11 and 14, respectively. Meanwhile, in the report by Kang et al. [45], a sucrose-related QTL region (Gm11_17237725-24186948) was identified, which is in close proximity to our sucrose locus, although it does not overlap with it. This confirms the localization of sucrose on chromosome 11.
In contrast to sucrose, there have been limited investigations into the QTL mapping of stachyose in soybeans. Among the few studies conducted, Akond et al. [32] mapped four QTLs for stachyose, located on chromosomes 1, 6, 12, and 14, respectively. Additionally, Knizia et al. [46] mapped four QTLs for stachyose, located on chromosomes 13, 16, 17, 19, and 20, while Zeng et al. [47] mapped stachyose QTLs on chromosomes 10 and 11. It is located on the same chromosome as the locus for water thujose that we previously identified. Knizia et al. [46] localized a sucrose-associated QTL (Gm10_621706) on chromosome 10, which coincides with the chromosome containing the stachyose locus we identified. Since raffinose and stachyose are derived from sucrose [29], it is reasonable to find that the stachyose locus in these studies is in close proximity to the sucrose locus we localized.
Sequence comparisons of the candidate genes revealed that the candidate gene Glyma.11g136200 exhibited sequence mutations in the wild soybean parent ‘Ping Nan’. In this study, the sucrose content of the cultivated soybean ‘Jin Da 53’ was found to be higher than that of the wild soybean ‘Ping Nan’. Conversely, the candidate gene Glyma.10g154400 displayed sequence mutations in the cultivated soybean ‘Jin Da 53’, while the wild soybean ‘Ping Nan’ exhibited a higher stachyose content than ‘Jin Da 53’. This suggests that both wild and cultivated soybeans possess desirable traits associated with high soluble sugar content. Pan et al. [48] mapped eight QTLs for soluble sugars in soybean and identified three candidate genes in wild soybean. Subsequent comparisons of the sequence information of three genes across different varieties revealed a single SNP mutation in Glyma.19G122500 in ZYD00006, which resulted in a change in the coding sequence from G to A at position 19 bp, leading to an amino acid substitution from G (glycine) to D (aspartic acid), further altered the protein’s tertiary structure. The two candidate genes we localized also exhibit base mutations in the parental species, consistent with the findings of this study. Therefore, investigating the soluble sugar content of soybeans, exploring the differences in soluble sugar levels, and identifying relevant mutation sites for further analysis and verification represent valuable research directions for understanding the mechanisms regulating soluble sugars in soybeans.

5. Conclusions

In summary, we identified two candidate intervals associated with the soluble sugar compositions in soybean seeds through whole genome resequencing and QTL localization. A stable locus controlling stachyose content was detected on chromosome 10 within the interval of 72.231 cM to 87.740 cM, and a candidate gene, Glyma.10g154400, was identified as a regulator of stachyose content in soybean seeds. Furthermore, a stable locus regulating sucrose content was identified on chromosome 11 within the interval of 96.881 cM to 97.529 cM, with another candidate gene, Glyma.11g136200, also identified, and the mutated amino acid of the protein in the parental lines is conserved among its homologs in the analyzed plants. Additionally, RT-qPCR analysis demonstrated that the Glyma.10g154400 gene exhibited higher expression levels in varieties with lower stachyose content, whereas Glyma.11g136200 showed increased expression in lines with elevated sucrose levels. These findings enhance our understanding of soluble sugar accumulation in soybean seeds, and the identified candidate genes will be instrumental in breeding soybean varieties with increased sugar content.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/agronomy15040972/s1, Table S1 Analysis of variance (ANOVA) of soybean soluble sugars and their fractions. Table S2. AIC value of each genetic model of soluble sugar in soybean. Table S3 Test of goodness-of-fit on genetic models for soybean soluble sugar components. Table S4 Statistical table of SNP marker information in genetic map. Table S5 Gene annotation of candidate genes in the 72.23-87.74 cM candidate interval on chromosome 10. Table S6 Gene annotation of candidate genes in the 96.88 cM-105.5 cM candidate interval on chromosome 11. Table S7 Primer sequences. Figure S1 Analysis of evolutionary relationships and conserved structural domains of the candidate gene Glyma.10g154400. Figure S2 Analysis of evolutionary relationships and conserved structural domains of the candidate gene Glyma.11g136200.

Author Contributions

Data curation, C.H., Y.W. and C.L.; Investigation, C.H., Y.W. and Q.Y.; Validation, C.H., C.L. and Y.Y.; Visualization, C.H., Q.Y., A.Y. and J.N.; Writing—original draft, C.H., A.Y., J.N. and M.W.; Formal analysis, Y.Y. and J.N.; Supervision, A.Y., L.W. and W.D.; Software, J.N. and M.W.; Project administration, L.W. and M.W.; Writing—review and editing, L.W., W.D. and M.W.; Funding acquisition, W.D. and M.W.; Conceptualization, M.W.; Methodology, M.W. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Technology Innovation 2030 Major Program (2023ZD040350404), Shanxi Provincial Agricultural Key Core Technology Tackling Sub-projects (NYGG27-04), Construction of Modern Agricultural Industrial Technology System in Shanxi Province Project 2024 (CYJSTX05), Shanxi Agricultural University Breeding Engineering Program (YZGC096), Shanxi Agricultural University, College of Agriculture, Key Cultivation Specialties in Breeding Engineering (YZ2021-05).

Data Availability Statement

The Gene annotation data used in this study were downloaded from the Phytozome database (https://phytozome-next.jgi.doe.gov/ (accessed on 10 November 2024)), and Sequence data from this article can be found in the Glycine max Wm82.a2.v1 database. The data that support the findings of this study are available from the corresponding author, M.W., upon reasonable request.

Conflicts of Interest

The authors declare no conflict of interest.

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Figure 1. The frequency distribution of various soluble sugar components in soybean seeds from the RIL population is presented for the years 2017 (A), 2018 (B), and 2019 (C). The y-axis represents frequency, while the x-axis indicates the values of soluble sugar content.
Figure 1. The frequency distribution of various soluble sugar components in soybean seeds from the RIL population is presented for the years 2017 (A), 2018 (B), and 2019 (C). The y-axis represents frequency, while the x-axis indicates the values of soluble sugar content.
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Figure 2. A high-density genetic map of the RIL population was constructed using SLAP and SSR markers. The genetic map is represented by black bars. The x-axis denotes the 20 linkage groups, while the y-axis indicates genetic distance.
Figure 2. A high-density genetic map of the RIL population was constructed using SLAP and SSR markers. The genetic map is represented by black bars. The x-axis denotes the 20 linkage groups, while the y-axis indicates genetic distance.
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Figure 3. Distribution of QTLs for soluble sugars and their components in soybean seeds. Blue font indicates chromosome locations where QTLs were identified in 2017, red font indicates chromosome locations for QTLs identified in 2018, and purple font indicates chromosome locations for QTLs identified in 2019.
Figure 3. Distribution of QTLs for soluble sugars and their components in soybean seeds. Blue font indicates chromosome locations where QTLs were identified in 2017, red font indicates chromosome locations for QTLs identified in 2018, and purple font indicates chromosome locations for QTLs identified in 2019.
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Figure 4. Expression pattern of candidate genes associated with soybean soluble sugar components. Relative transcript levels of Glyma.10g154400 in parental PN, parental JD53, low stachyose (G161), and high stachyose lines (G169) (A); Relative transcript levels of Glyma.11g136200 in parental PN, parental JD 53, low sucrose (G136), and high sucrose lines (G81) (B).
Figure 4. Expression pattern of candidate genes associated with soybean soluble sugar components. Relative transcript levels of Glyma.10g154400 in parental PN, parental JD53, low stachyose (G161), and high stachyose lines (G169) (A); Relative transcript levels of Glyma.11g136200 in parental PN, parental JD 53, low sucrose (G136), and high sucrose lines (G81) (B).
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Figure 5. Candidate genes associated with seed soluble sugar of soybean. The schematic representation of the Glyma.10g154400 in JD53 and PN (A); Schematic of the Glyma.11g136200 in JD53 and PN (B); left, predicted tertiary structure of Glyma.10g154400 in JD53, right, predicted the tertiary structure of Glyma.10g154400 in PN (C); left, predicted tertiary structure of Glyma.11g136200 in JD53, right, predicted tertiary structure of Glyma.11g136200 in PN (D); Amino acid alignment of Glyma.10g154400 with its Homologous (E); Amino acid alignment of Glyma.11g136200 with its Homologous (F).
Figure 5. Candidate genes associated with seed soluble sugar of soybean. The schematic representation of the Glyma.10g154400 in JD53 and PN (A); Schematic of the Glyma.11g136200 in JD53 and PN (B); left, predicted tertiary structure of Glyma.10g154400 in JD53, right, predicted the tertiary structure of Glyma.10g154400 in PN (C); left, predicted tertiary structure of Glyma.11g136200 in JD53, right, predicted tertiary structure of Glyma.11g136200 in PN (D); Amino acid alignment of Glyma.10g154400 with its Homologous (E); Amino acid alignment of Glyma.11g136200 with its Homologous (F).
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Table 1. Phenotypic variation of soluble sugar composition content of the RIL population.
Table 1. Phenotypic variation of soluble sugar composition content of the RIL population.
TraitYearParentsRIL Population
JD53
/mg·g−1		Ping Nan
/mg·g−1		Mean
/mg·g−1		Range
/mg·g−1		Coefficient of Variation/%KurtosisSkewness
Glucose20173.72 a3.13 a7.882.98~23.5242.047.312.34
20184.52 ab5.96 ab5.503.16~10.4924.141.551.19
201921.60 d18.78 e19.1811.31~39.6517.629.661.74
Sucrose201751.06 f29.31 j35.4513.19~49.4720.240.41−0.42
201835.34 de20.21 g35.1919.46~53.4217.34−0.080.27
201945.43 f30.12 i46.6025.64~74.818.011.020.65
Raffinose20175.14 ab4.49 ab5.401.32~12.4233.741.210.67
20183.60 b6.68 a5.483.16~8.6920.92−0.280.35
201914.75 c12.28 c13.570.02~32.2947.02−0.190.62
Stachyose201737.18 g37.48 g31.348.83~49.4920.860.61−0.23
201829.57 g37.86 f36.1418.06~55.216.390.530.38
201935.57 h41.14 g37.0324.31~59.216.362.520.92
Total sugar201797.11 l74.40 m80.0649.62~106.8213.74−0.02−0.33
201873.03 k70.71 l82.3160.67~108.6410.320.250.05
2019117.36 n102.32 o116.3780.8~167.7112.890.890.68
Groups sharing the same lowercase letter (e.g., a) indicate no significant difference (p > 0.05). Groups labeled with adjacent lowercase letters (e.g., ab) show no significant difference with either group a or b (p > 0.05), but a significant difference exists between groups a and b (p < 0.05). Groups with different lowercase letters (e.g., a, b) represent statistically significant differences (p < 0.05).
Table 2. Correlation analysis of soluble sugar and their components in RIL population in 2017.
Table 2. Correlation analysis of soluble sugar and their components in RIL population in 2017.
GlucoseSucroseRaffinoseStachyose
Sucrose−0.234
Raffinose0.1170.280 **
Stachyose−0.247 **0.197 *0.299 **
Total sugar0.0210.745 **0.562 **0.698 **
* indicates significant difference (p < 0.05), and ** indicates highly significant difference (p < 0.01).
Table 3. Correlation analysis of soluble sugar and its components in the RIL population in 2018.
Table 3. Correlation analysis of soluble sugar and its components in the RIL population in 2018.
GlucoseSucroseRaffinoseStachyose
Sucrose0.135
Raffinose0.073−0.09
Stachyose−0.185 *−0.0650.236 **
Total sugar0.1340.682 **0.246 **0.654 **
* indicates significant difference (p < 0.05), and ** indicates highly significant difference (p < 0.01).
Table 4. Correlation analysis of soluble sugar and its components in the RIL population in 2019.
Table 4. Correlation analysis of soluble sugar and its components in the RIL population in 2019.
GlucoseSucroseRaffinoseStachyose
Sucrose0.09
Raffinose0.340 **0.239 **
Stachyose−0.1290.246 **0.011
Total sugar0.368 **0.781 **0.640 **0.517 **
** indicates highly significant difference (p < 0.01).
Table 5. Estimation of genetic parameters for modeling the components of soybean soluble sugars.
Table 5. Estimation of genetic parameters for modeling the components of soybean soluble sugars.
YearTraitsModelSingle EffectInteraction EffectMajor Gene VarHeritability (Major Gene) (%)
2017Glucose4MG-AI2.35371.534910.565595.5996
Sucrose0MG-
Raffinose2MG-CE 1.21761.094632.7648
Stachyose0MG
2018Glucose2MG-CE-1.35641.099561.8282
Sucrose2MG-CE-4.415215.086640.1834
Raffinose2MG-CE 0.8860.620446.8152
Stachyose2MG-CE 4.236713.614938.5054
2019Glucose0MG
Sucrose2MG-CE 6.077226.112836.791
Raffinose3MG-AI4.25272.007532.411978.9812
Stachyose4MG-AI3.7659−0.110336.166897.7313
Table 6. Summary of the high-density genetic map.
Table 6. Summary of the high-density genetic map.
Linkage
Group ID		Marker NumberGenetic
Distance (cM)		Average
Distance (cM)		Maximum
Gap (cM)		Gaps < 5 cM
Chr0157152.352.7216.240.88
Chr02577208.450.369.131.00
Chr0368121.271.819.170.94
Chr04528132.430.259.450.99
Chr05499164.720.333.341.00
Chr06404163.350.419.441.00
Chr07366208.220.577.310.98
Chr08580176.810.319.870.99
Chr094141460.3517.920.99
Chr10608144.330.2423.121.00
Chr11105119.151.158.120.97
Chr12545149.870.2825.261.00
Chr13274138.980.5115.380.98
Chr14120174.331.4616.630.96
Chr15616149.640.248.191.00
Chr162892.513.4316.150.85
Chr17214193.280.916.870.99
Chr18740196.480.2727.090.99
Chr19534106.30.29.760.99
Chr20668209.990.3128.450.98
Total79453148.460.8128.450.97
Table 7. QTL analysis for soluble sugar and its component traits in soybean seeds.
Table 7. QTL analysis for soluble sugar and its component traits in soybean seeds.
YearChrPositionSupport Interval (cM)LODPVE (%)AddPhysical Interval (bp)
Glucose201812111110.808–111.6142.78666.8040.373433506546–33603529
201820118117.986–122.3354.380310.9411−0.473634542250–34901234
20182187178.418–187.5463.47548.8828−0.433151433069–51433264
201914036.795–40.0823.261210.00561.072751571946–51900610
2019171413.755–14.5522.6578.0716−0.96162353429–2581250
Sucrose20173107106.212–110.6583.651310.516−2.358545430793–46055299
2017138988.201–89.2422.7947.685−2.048535116865–35117695
2018137271.623–76.7034.591614.565−2.3166-
2019119796.881–97.5293.255312.89232.778410267062–10446348
Raffinose20182142140.059–142.5622.93258.9174−0.334444725034–44896642
201837674.183–77.5762.97139.29410.341840135633–41057737
20186139138.925–139.4343.642510.98170.371148922807–49064512
201942625.918–29.7062.82469.76331.96478511946–8567964
Stachyose2017107574.631–84.0224.095413.75782.464337101443–38298307
2018108584.966–87.744.579713.87272.230338681635–39134900
2019107372.231–73.0255.537511.66852.543136697685–36697916
Total sugar20171200–0.2342.5038.60883.229218170525–18191808
201813433.309–36.7953.33719.33532.638151900398–52608650
20188103102.474–103.1053.874210.4852.789418611345–18810842
2018177675.01–76.0293.16878.47852.527713684749–14010829
Note: LOD stands for the association between QTL and soluble sugars; the larger the value, the stronger the association. PVE stands for phenotypic variance explained rate.
Table 8. Candidate QTL intervals for soluble sugars and their components in soybean seeds.
Table 8. Candidate QTL intervals for soluble sugars and their components in soybean seeds.
Marker intervalChrGenetic Interval (cM)Traits of Mapping
S10_37101443–S10_382983071074.631–84.0222017-stachyose
S10_38681635–S10_391349001084.966–87.7402018-stachyose
S10_36697685–S10_366979161072.231–73.0252019-stachyose
S11_10267062–S11_104463481196.881–97.5292019-sucrose
Table 9. Locating genes and annotations.
Table 9. Locating genes and annotations.
NameLocationDatabase IDAnnotation TypeAnnotation Description
Glyma.10g154400.Wm82.a2.v1Gm10: 38941372–38947198GO:0005975GO-bpcarbohydrate metabolic process
Glyma.11g136200.Wm82.a2.v1Gm11: 10352301–10356496IPR003663InterProsugar/inositol transporter
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He, C.; Wang, Y.; Li, C.; Yang, Y.; You, Q.; Yue, A.; Niu, J.; Wang, L.; Du, W.; Wang, M. QTLs Mapping and Identification of Candidate Genes Associated with Stachyose and Sucrose in Soybean (Glycine max L.). Agronomy 2025, 15, 972. https://doi.org/10.3390/agronomy15040972

AMA Style

He C, Wang Y, Li C, Yang Y, You Q, Yue A, Niu J, Wang L, Du W, Wang M. QTLs Mapping and Identification of Candidate Genes Associated with Stachyose and Sucrose in Soybean (Glycine max L.). Agronomy. 2025; 15(4):972. https://doi.org/10.3390/agronomy15040972

Chicago/Turabian Style

He, Chuanrong, Yipu Wang, Changning Li, Yue Yang, Qian You, Aiqin Yue, Jingping Niu, Lixiang Wang, Weijun Du, and Min Wang. 2025. "QTLs Mapping and Identification of Candidate Genes Associated with Stachyose and Sucrose in Soybean (Glycine max L.)" Agronomy 15, no. 4: 972. https://doi.org/10.3390/agronomy15040972

APA Style

He, C., Wang, Y., Li, C., Yang, Y., You, Q., Yue, A., Niu, J., Wang, L., Du, W., & Wang, M. (2025). QTLs Mapping and Identification of Candidate Genes Associated with Stachyose and Sucrose in Soybean (Glycine max L.). Agronomy, 15(4), 972. https://doi.org/10.3390/agronomy15040972

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