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Identification of quantitative trait loci for lodging and related agronomic traits in soybean (Glycine max [L.] Merr.)

BMC Genomics volume 25, Article number: 900 (2024) Cite this article

Abstract

Background

Lodging, a crucial agronomic trait linked to soybean yield, poses a significant challenge in soybean production. Nevertheless, there has been less research on soybean lodging compared to other important agronomic traits, hindering progress in breeding high-yield soybeans. Our goals were to investigate lodging, pinpoint quantitative trait loci (QTL) linked to lodging, and forecast potential candidate genes linked to this trait. To achieve this, we employed a recombinant inbred line (RIL) population derived from a cross between Guizao 1 and B13 (GB) across various environments.

Results

The lodging score of the RIL population was found to be significantly positively correlated with flowering time, maturity time, plant height, number of main stem nodes, stem diameter, and internode length, with correlation coefficients ranging from 0.457 to 0.783. A total of 84 QTLs associated with soybean lodging and related traits were identified using the GB population. The contribution of phenotypic variance ranged from 1.26 to 66.87%, with LOD scores ranging from 2.52 to 69.22. Additionally, within these QTLs, a stable major QTL associated with lodging was newly discovered in the GB population. Out of the ten major QTLs associated with other related traits, nine of them were situated within the qLD-4-1 interval of the major lodging score locus, displaying phenotypic variations ranging from 12.10 to 66.87%. Specific alterations in gene expression were revealed through the analysis of resequencing data from the two parental lines, potentially indicating their significant roles in lodging. Subsequently, it was determined through qRT-PCR that four genes are likely to be the major genes controlling soybean lodging.

Conclusions

This study’s findings offer valuable insights into the genetic underpinnings of soybean lodging resistance traits. By comprehending the potential genetic factors associated with lodging, this research lays the groundwork for breeding high-yield soybeans with improved lodging resistance.

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Background

Soybean (Glycine max [L.] Merr.) is a significant oil and protein crop with origins dating back approximately 5,000 years to ancient China [1, 2]. Given the rising global population and improving living standards, there is an expectation that soybean production will need to meet consumer demands by 2050 [3]. Consequently, the aim of crop breeding is to develop agricultural plants with desirable traits [4].

In recent decades, numerous studies have employed molecular markers and QTL analysis to investigate and identify desired agronomic traits in crops [5,6,7,8]. The rapid advancement of molecular biology technology has facilitated the widespread adoption of DNA molecular marker techniques in various domains, including gene mapping and identification [9,10,11]. Conventional molecular markers include restriction fragment length polymorphism (RFLP), random amplified polymorphic DNA (RAPD), amplified fragment length polymorphism (AFLP), and simple sequence repeat (SSR) markers [12,13,14,15,16]. With the ongoing advancement of molecular biology technology, a novel molecular marker known as SNP has emerged, demonstrating rich and stable genetic variations within the genome [17, 18]. The creation of high-density genetic linkage maps through high-throughput SNP genotyping platforms is of paramount importance for precise QTL mapping and the exploration of essential agricultural traits [19, 20]. Subsequently, many scholars have identified a total of 27 QTLs using RFLP and SSR molecular markers.

Crop lodging represents an agronomic trait influenced by multiple factors, including genetics, cultivation practices, climate, and ecology. It is closely linked to plant height, stem thickness, stem strength, root weight, root length, and root volume [21,22,23,24]. Numerous QTLs associated with soybean lodging have been identified in prior research. Mansur et al. [25] detected a QTL on chromosome 19 closely linked to RFLP markers using an F2:5 population resulting from a cross between PI27890 × PI27890. Subsequently, Lee et al. [26] successfully identified 18 lodging QTLs employing F2 populations and RFLP molecular markers. Orf et al. [27] discovered two QTLs on chromosome 6 tightly linked to RFLP markers using a recombinant inbred line (RIL) population derived from a cross between Minsoy × Archer. Specht et al. [28] found five QTLs on chromosomes 6, 12, and 19, using SSR and RFLP markers in an F7:11 population from a Minsoy × Noir1 cross. Chapman et al. [29] uncovered a QTL on chromosome 11 closely linked to SSR markers in an F4:6 population from an Essex × Essex cross. Wang et al. [30] identified a QTL on chromosome 9 closely linked to SSR markers in a BC2F4 population from an IA2008 × PI468916 cross. Zhang et al. [31] identified eight QTLs on chromosomes 6 and 13, using SSR and RFLP markers in an F2:7:11 population from a Kefeng No.1 × Nannong 1138-2 cross. Subsequently, SSR molecular markers became widely adopted by scholars for detecting 15 lodging QTLs on various chromosomes [32,33,34,35,36]. Additionally, some scholars successfully detected lodging QTLs using SNP molecular markers [37]. Lee et al. [22] identified five QTLs on chromosomes 4, 6, and 19 closely linked to SNPs in an F7 population from crosses between Wyandot × PI567301B. In summary, previous research has identified 20 chromosomes containing QTLs associated with soybean lodging, utilizing various molecular markers.

This study aims to utilize the GB populations and high-density genetic linkage maps to pinpoint the locus associated with lodging across multiple environments and to identify stable major-effect QTLs related to soybean lodging. Furthermore, we aim to identify candidate genes responsible for regulating soybean lodging. The lodging QTLs and genes identified in this study may contribute to a deeper understanding of the genetic mechanisms underlying lodging and provide valuable guidance for future efforts to improve soybean yields.

Materials and methods

Plant materials

The RIL populations were created through a cross between two distinct soybean accessions, Guizao 1 × B13, and comprised 248 F8:11 lines [38]. Guizao 1 belongs to the early-maturing soybean varieties of southern China, selected by the Guangxi Zhuang Autonomous Region Academy of Agricultural Sciences. B13 is an imported variety from Brazil. Both the parental lines and the 248 RILs were cultivated in three replicates in July of 2020, 2021, and 2022 and in March of 2021 at the Teaching and Research Base of Zengcheng (23°14’N, 113°38’E), and also in July 2020 at the Experimental Teaching Base of Guangzhou (23°10’N, 113°22’E), denoted as 20ZC, 21ZC-2, 22ZC, 21ZC-1, and 20GZ, respectively. The experimental planting followed a completely randomized block design, with one row allocated per line, a row length of 1.5 m, a row spacing of 0.5 m, and a plant spacing of 0.1 m. Field management adhered to conventional practices, and no occurrences of pests or diseases were observed during the growth period.

Phenotypic statistics and analysis

The improvement in lodging score is categorized into five levels, and during the soybean R8 phase, it is required that a minimum of 80% of the plants display a tilted angle [22, 25]. These levels are defined as follows: Level 1: Nearly all plants are upright. Level 2: The angle of plant inclination does not exceed 15°. Level 3: The tilt angle ranges from 15° to 45°. Level 4: The tilt angle ranges from 45° to 85°. Level 5: The tilt angle exceeds 85°. Other key traits measured include: Flowering time: The presence of an open flower on any section of the main stem. Maturity time: When 95% of the pods achieve maturity based on pod color. Plant height: The distance from the cotyledon to the top of the plant stem, excluding the inflorescence. Number of main stem nodes: The count of nodes starting above the cotyledon node, excluding the apical inflorescence and cotyledon nodes. Stem diameter: The diameter of the first internode of the stem. Internode length: The ratio of plant height to the number of main stem nodes. Grain weight per plant: The weight of seeds produced per plant. Among these traits, plant height, stem diameter, and the number of main stem nodes were assessed using two plants for each repetition [32, 33, 39, 40].

The mean of the phenotypic data collected from the GB RIL population across five natural environments was computed to represent the phenotypic data of the combined environment. These data encompassed lodging score, flowering time, maturity time, plant height, number of main stem nodes, stem diameter, internode length, and grain weight per plant across various natural environments. Statistical parameters for lodging and related traits were computed between the parental lines and their respective RIL individuals in each environment and for the combined environment using IBM SPSS Statistics 26.0 (SPSS, Inc., Chicago, IL, USA). Descriptive statistics of phenotype data were obtained through analysis of variance (ANOVA), including measures such as maximum, minimum, mean, range of variation, standard deviation, coefficient of variation (CV), kurtosis, and skewness for the two RIL populations.

Analysis of variance (ANOVA) and the estimation of broad-sense heritability (h2) were performed on the phenotypic data collected from the five natural environments utilizing Genstat 12th Edition software. The formulae [41] were as follows:

h2 = σ2g =(σ2g + σ2ge /n + σ2e /nr).

where σ2g denotes genotype variance, σ2ge denotes genotype × environment interaction variance, and σ2e denotes variance error. “n” denotes the number of environments, and “r” denotes the number of replicates per environment.

Linkage map construction and QTL mapping

The construction of genetic maps for the RIL populations in this study followed previously described methods [38]. These maps encompassed 3,748 bin markers identified in the 248 individuals, covering a total of 3,031.93 cM. The average genetic distance between adjacent bin markers on each chromosome within the GB RIL population ranged from 0.60 to 1.03 cM. These high-density linkage maps provided enhanced resolution compared to traditional maps. To identify the positions of QTLs, we employed the Composite Interval Mapping (CIM) method using WinQTLCart 2.5 software. The threshold for LOD (logarithm of odds) scores for different traits was set at 2.5, corresponding to the 5% significance level. LOD scores exceeding 2.5 were considered indicative of the presence of QTLs. The distribution of all identified QTLs on the linkage map was visualized using MapChart software. Each detected QTL was denoted by a combination of one or more letters representing trait abbreviations and chromosome numbers [42]. Specifically, “q” represented QTL, LD represented lodging score, FT represented flowering time, MT represented maturity time, PH represented plant height, SD represented stem diameter, NMSN represented the number of main stem nodes, IL represented internode length, and GWPP represented grain weight per plant. The number immediately following indicated the chromosome number, followed by the sequence number [43].

Prediction of candidate genes in the major stable QTL interval

For this analysis, information regarding the Glyma.Wm82.a2.v1 gene model was sourced from SoyBase (http://www.soybase.org), focusing on the physical intervals of both stable and novel QTLs. Functional gene annotation information for candidate genes within these QTLs was assigned based on the Glyma.Wm82.a2.v1 reference genome sequence available at (https://phytozome.jgi.doe.gov) and SoyBase. To further elucidate the roles of these candidate genes, an online data analysis platform and cloud-based tools (https://www.omicstudio.cn/tool/22) were utilized to conduct Gene Ontology (GO) annotation, categorizing the functions associated with all genes found within the stable and novel QTLs. In addition to functional annotation, reference gene expression data were obtained from the ePlant database (https://bar.utoronto.ca/eplant_soybean/), which includes RNA-seq data from various soybean samples, specifically from Shoot Apex, Hypocotyl, and Stem, all located within the major and stable QTL interval. To provide insights into tissue-specific expression patterns, an online resource (https://www.omicstudio.cn/tool/59) was employed to generate heat maps depicting the expression patterns of candidate genes [44]. Moreover, an assessment of genetic variation among candidate genes between the parental lines was conducted based on resequencing data of the parental lines. Whole-genome sequencing of Guizao 1 and B13 was performed using the Illumina HiSeq X Ten platform, with an average sequencing depth of 8× [45]. High-quality sequencing data from these parental lines were analyzed to predict structural variations in the genes.

Quantitative real-time PCR (qRT-PCR) analysis

Root and stem tissues from the two parental lines (Guizao 1 and B13) were preserved at -80 °C. Total RNA extraction from the roots and stems was performed using the Plant Total RNA Purification Kit (Promega (Beijing) Biotech Co., Ltd). Subsequently, one microgram of RNA underwent genomic DNA removal and reverse transcription using the TransScript One-Step gDNA Removal and cDNA Synthesis SuperMix kit (Novoprotein). Gene expression levels were assessed through qRT-PCR analysis conducted with the CFX96 Real-Time System (Bio-Rad). The PCR cycling conditions were as follows: an initial denaturation step at 95 °C for 1 min, followed by 40 cycles of denaturation at 95 °C for 10 s, annealing at 55.0–60.0 °C (depending on the gene) for 10 s, and extension at 72 °C for 30 s [46]. The soybean ACT3 gene [47] was amplified as a reference control. The resulting expression data were calculated using the comparative cycle threshold method (2−△△ct), and the experiments were independently repeated three times [48]. Specific primer sequences for each gene used in this analysis can be found in Table S1.

Results

Correlation analysis of lodging score and related traits

The box plots displaying phenotypic data for various environmental traits (Fig. 1) indicate minimal variation in the data across the years. Consequently, the mean values of the data for each trait were employed in the correlation analysis (Table 1). In the GB population, the lodging score displays highly significant positive correlations with several traits, including flowering time, maturity time, plant height, number of main stem nodes, stem diameter, and internode length. The correlation coefficients for these relationships range from 0.457 to 0.783. However, there is no significant correlation observed between lodging score and grain weight per plant. The correlation coefficients between lodging score and each of the correlated traits are as follows: Flowering time: 0.698 Maturity time: 0.483 Plant height: Highest correlation at 0.783 Number of main stem nodes: 0.749 Stem diameter: 0.457 Internode length: 0.564 In contrast, grain weight per plant exhibits no significant correlation, with a correlation coefficient of 0.050. These findings indicate that the correlations between soybean lodging and other agronomic traits can serve as valuable references for selecting high-yield varieties in field-based breeding programs.

Table 1 Correlation Analysis between Lodging Score and Related Traits in the GB Population
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Fig. 1
figure 1

Box Plot of Lodging and Related Traits. (A) Lodging Score, (B) Flowering Time, (C) Maturity Time, (D) Plant Height, (E) Number of Main Stem Nodes, (F) Stem Diameter, (G) Internode Length, (H) Grain Weight per Plant. Environments: Guangzhou 2020 (20GZ), Zengcheng 2020 (20ZC), Zengcheng Spring 2021 (21ZC-1), Zengcheng Summer 2021 (21ZC-2), Zengcheng 2022 (22ZC), Combined Environment (CE)

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Fig. 2
figure 2

Frequency Distribution of Lodging-Related Traits in the GB Population Across Multiple Environments. Environments: Guangzhou 2020 (20GZ), Zengcheng 2020 (20ZC), Zengcheng Spring 2021 (21ZC-1), Zengcheng Summer 2021 (21ZC-2), Zengcheng 2022 (22ZC), Combined Environment (CE). “G” represents the female parent Guizao1, and “B” represents the male parent B13. Figures A to H indicate the lodging score, flowering time, maturity time, plant height, number of main stem nodes, stem diameter, internode length and grain weight per plant, respectively

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Descriptive statistical analysis of soybean lodging related traits and estimates of broad-sense heritability

Descriptive statistical analysis was conducted on the phenotypic data (Table 2). The results revealed that both parental lines exhibited mild lodging scores. However, the RIL populations displayed a wide range of family variation, with coefficients of variation ranging from 28.44 to 51.94%. This wide variation encompasses extreme traits, providing a solid foundation for QTL mapping of lodging. Similar patterns were observed in other lodging-related traits within the RIL population, indicating parental segregation and laying the groundwork for QTL mapping. The coefficients of variation for various traits are as follows: 11.34–13.00% for flowering time, 5.99–7.00% for maturity time, 20.20–28.51% for plant height, 13.51–21.35% for the number of main stem nodes, 10.80–14.45% for stem diameter, 13.31–17.17% for internode length, and 18.25–24.42% for grain weight per plant.

Table 2 Descriptive statistical table for lodging and related traits of parents and the GB Population in various environments
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Several traits, including lodging score, flowering time, maturity time, and plant height, exhibit kurtosis and skewness values greater than 1. These values indicate the presence of numerous influential factors influencing these traits. The segregation of these traits in the RILs is governed by multiple genes, aligning with the characteristics of quantitative genetic traits. Conversely, the absolute values of kurtosis and skewness for the number of main stem nodes, stem diameter, internode length, and grain weight per plant are all less than 1, signifying that these traits follow a normal or approximately normal distribution and are consistent with quantitative genetic traits. Additionally, the frequency distribution map (Fig. 2) vividly illustrates the continuous variations in the phenotypic data of lodging score and its related traits. These findings collectively suggest that the lodging score and associated traits within the GB population conform to a normal or partially normal distribution, aligning with the characteristics of the RIL population and categorizing as quantitative genetic traits. In summary, the results indicate that lodging and related traits in the GB population adhere to a normal distribution pattern, consistent with the characteristics of the RIL population and indicating their classification as quantitative genetic traits.

ANOVA results for the lodging score of the GB RIL population across five natural environments demonstrate significant effects of genotype, environment, and the interaction between genotype and environment on lodging and related traits of the GB population (Table 3). The lodging trait in the GB population exhibited a substantially high heritability estimate (h2) of 93.18%, indicating that the lodging phenotype in soybean is primarily influenced by genotype.

Table 3 Analysis of Variance and Broad-Sense Heritability for the GB Population across Five Natural Environments
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Identification of QTLs for lodging score and related traits

In total, 84 QTL loci were identified, accounting for phenotypic variation ranging from 1.26 to 66.87% across six environments. These QTLs were distributed across various traits, with 20, 11, 11, 12, 9, 10, 6, and 5 QTLs detected for lodging score, flowering time, maturity time, plant height, number of main stem nodes, stem diameter, internode length, and grain weight per plant, respectively (Fig. 3; Table S2). All of these QTLs displayed LOD values exceeding 2.5. The major and stable QTL locus, named qLD-4-1, associated with lodging score was identified. It is positioned within the physical interval of 3,513,907–5,769,624 bp on chromosome 4, spanning between bin15 to bin39 markers. This QTL was consistently detected in all six environments and exhibited phenotypic variation ranging from 15.38 to 38.68%, with LOD values ranging from 10.36 to 34.70. Additionally, nine out of the ten primary QTLs for other related traits (qFT-4, qMT-4-1, qMT-4-2, qPH-4, qPH-19-2, qNMSN-4, qSD-4-1, qSD-4-2, qIL-4-1, qIL-4-2) were found within the physical region of the primary QTL, qLD-4-1, for lodging score (Table 4). These QTLs exhibited phenotypic variation ranging from 55.93 to 66.87% for flowering time, 31.58–48.70% for maturity time, 41.41–51.32% for plant height, 20.46–48.39% for the number of main stem nodes, 12.10–29.12% for stem diameter, and 13.60–30.07% for internode length. The stable QTLs mentioned above provide valuable insights for the exploration of genes that regulate soybean lodging and related agronomic traits.

Fig. 3
figure 3

Distribution of QTLs for Lodging and Related Traits on Soybean Chromosomes The left ruler indicates the physical distance between markers in cM. The right side of the chromosome shows the position of QTLs for lodging-related traits. The eight colors in the upper right corner of all chromosomes represent different traits

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Table 4 Key QTLs Associated with Lodging and related traits in the GB Population across different environmental conditions
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Candidate gene prediction within stable and major QTL interval

To identify potential genes associated with lodging within the QTL region (qLD-4-1), a search was conducted for 271 gene models located within this interval. Subsequently, 225 gene functions linked to qLD-4-1 underwent GO annotation and were categorized through GO annotation analysis, providing functional annotations spanning cellular composition, biological processes, and molecular function (Fig. 4). The majority of genes within qLD-4-1 were found to be involved in processes such as the regulation of DNA-templated transcription, plasma membrane, chloroplast, membrane, and ATP binding. In order to narrow down the list of candidate genes associated with lodging, a comparison was made of differentially expressed genes across various soybean tissues (Fig. 5) during three specific periods: Shoot Apex, Hypocotyl, and Stem. Through gene GO annotation analysis, followed by gene expression screening and functional annotation, a total of 13 candidate genes were identified (Table 5), indicating their potential roles in critical processes governing soybean lodging. In the high-quality resequencing data, seven out of the 13 genes exhibited structural variation between the parental lines of the RIL population (Guizao 1 and B13). These genes are Glyma.04g050200, Glyma.04g050800, Glyma.04g051300, Glyma.04g052100, Glyma.04g053600, Glyma.04g056200, and Glyma.04g063800 (Table 6).

Fig. 4
figure 4

Gene Ontology Annotation Analysis of Main and Stable QTL (qLD-4-1) Interval Genes

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Fig. 5
figure 5

Expression of Candidate Genes in the qLD-4-1 Region in Different Soybean Tissues The heat map displays the expression levels of 13 candidate genes in the qLD-4-1 region across various soybean tissues, as analyzed using ePlant

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Table 5 Candidate genes in the qLD-4-1 region of the Population
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Table 6 Information on candidate genes in qLD-4-1 in the Population
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Expression for the identification of candidate genes

This study conducted a comprehensive analysis of the expression levels of candidate genes in the root and stem tissues of the two parental lines. The genes exhibited differential expression in the stems and leaves of the two parents, as determined by qRT-PCR analysis (Fig. 6). Among these genes, Glyma.04g051300, Glyma.04g053600, Glyma.04g056200, and Glyma.04g063800 demonstrated significant differences in expression between Guizao 1 and B13 in both root and stem tissues, and these differences were highly significant. These findings strongly suggest that these four genes are the primary candidates responsible for regulating soybean lodging.

Fig. 6
figure 6

Expression of Seven Candidate Genes in Root and Stem Tissues of the Two Parent Plants Detected by qRT-PCR. The y-axis represents the relative expression levels of candidate genes compared to the expression in the roots of Guizao 1. A to G denote the relative expression of different candidate genes, respectively. The female parent of the GB RIL population is Guizao 1, and the male parent is B13. Error bars indicate standard deviation (n = 3). Asterisks indicate significant differences determined by Student’s t test (*, P < 0.05; **, P < 0.01; ***, P < 0.001; ****, P < 0.0001)

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Discussion

Improvement of evaluation method for lodging score

The grain weight measurement conducted by our research institute specifically applies to seeds with smooth surfaces. However, due to the prolonged maturity of the populations studied, lodging plants experienced seed mold and rot issues. This led to a significant decrease in the data related to grain weight per plant, resulting in data errors that rendered the results less meaningful. The recombinant inbred lines exhibited diverse phenotypic variations, and relying solely on a single indicator has certain limitations. This approach may overlook the influence of other traits on lodging. To comprehensively evaluate the lodging phenomenon, it is essential to detect specific morphological and physiological indicators in plants. These indicators often involve assessing traits like stem bending resistance and stem composition [49, 50]. Currently, the most commonly employed method for lodging assessment is intuitive observation [22, 25]. This method assesses the degree of lodging based on actual conditions, capturing the combined effects of various factors. It is a straightforward approach, providing authentic and easily interpretable results, all while avoiding damage to the plants. To address the potential differences resulting from various degrees of edge lodging, adjustments can be made to enhance the suitability of this method for the studied population. Therefore, in this study, lodging scores were determined based on the intuitive method and the inclination of the main stem. Additionally, the proportion of lodging plants was calculated in relation to the total number of plants. These modifications aimed to facilitate data analysis and improve the visualization of lodging distribution within the population.

Comparisons of QTLs localization with previous results

The primary objective of crop breeding is to develop agricultural plants with desirable traits, such as increased grain yield, improved nutritional quality, and enhanced adaptability to changing environmental conditions, with grain yield being a predominant factor [4, 51]. Lodging significantly impacts crop yield, making it essential to study lodging characteristics in soybeans to reduce production costs and enhance yield. In this study, a total of 20 QTLs were identified for lodging score, including a previously unreported lodging QTL (qLD-4-1). Comparing the results of this study with previous research, it was discovered that the Glyma.04G050200 gene within the major QTL (qLD-4-1) for lodging score has been previously associated with controlling the growth period. Its physical location corresponds to a range of 4,075,901 to 4,081,260 bp, suggesting a potential link between genes regulating lodging and those governing the growth period [52,53,54]. Furthermore, QTLs for three lodging scores (qLD-4-2, qLD-7-2, and qLD-19-2) overlap or coincide with loci related to growth stage traits from previous studies [39, 55, 56]. Additionally, qLD-7-2, qLD-11-1, and qLD-19-2 are associated with QTLs for grain weight per plant, number of pods per plant, stem diameter, and plant height, as reported in previous research [21, 57, 58]. Comparing the 64 QTLs related to other traits with previous studies (Table 3), it was observed that 25 of them overlap or fall within the range of previously identified QTL locations. Notably, the primary QTLs for these traits consistently align with the main QTL for lodging score (qLD-4-1). In summary, many QTLs associated with lodging identified in this study show correlations with prior research results [21, 22, 30, 35, 37, 52,53,54,55,56, 59,60,61,62,63,64,65,66,67,68,69,70]. The presence of shared QTL loci for various traits, such as growth period, seed type, grain weight per plant, number of pods per plant, stem diameter, and plant height, suggests the reliability of the QTL mapping in this study. It is plausible that soybean lodging may be associated with genes controlling the growth period, a trait influenced by multiple genes.

Putative genes for the lodging resistant trait in soybean

Lodging resistance in soybean is influenced by various factors. One key factor is wheat lignin production, which plays a critical role in countering lodging [71, 72]. Wheat modifies the structure of its cell walls through the regulation of pathways related to hormones, reactive oxygen species, and nitrogen assimilation. This process limits cell wall loosening, restricts cell elongation, and enhances lodging resistance [73]. Wheat that is sensitive to gibberellin exhibits improved lodging resistance due to its semi-dwarf stature [74]. The mechanical strength of sorghum stems may be linked to the production of secondary cell wall cellulose [75]. In corn, an increase in ethylene content leads to reduced plant height, thereby strengthening lodging resistance [76, 77]. Additionally, various plant hormones, including auxin, abscisic acid, jasmonic acid, and salicylic acid, may play roles in maize lodging resistance [78]. Rice employs dwarfing breeding techniques to reduce plant height and enhance lodging resistance [79]. Mutations at the CESA4 site in rice impact cell wall characteristics, particularly cellulose structure, resulting in improved biomass digestion and lodging resistance [80]. In Arabidopsis, gibberellin (GA) biosynthesis may also be involved in lodging control [81]. Furthermore, the accumulation of lignin and cellulose in soybeans can inhibit lodging [50].

We identified gene models within the qLD-4-1 interval using publicly available data from Soybase. Within the physical interval of qLD-4-1, we found a total of 271 genes. Using high-quality resequencing data, we identified structural variations in 7 out of the 13 candidate genes among the parents in the RIL population. These seven candidate genes are as follows: (1) Glyma.04g050200, which is homologous to Arabidopsis ELF3 (AT2G25930.1), and control of soybean flowering genes and gibberellin biosynthesis [82, 83]; (2) Glyma.04g050800, which is homologous to Arabidopsis ATH1 (AT4G32980.1), and inhibiting stem growth and affecting lodging [84]; (3) Glyma.04g051300, which is homologous to Arabidopsis CGA1 (AT4G26150.1), and involved in influencing internal plant hormones, including cytokinins and gibberellin (GA) [85]; (4) Glyma.04g052100, which is homologous to Arabidopsis CYP711A1 (AT2G26170.1), and inhibition of axillary bud growth may be achieved by regulating flavonoid dependent auxin retention in buds and stems [86]; (5) Glyma.04g053600, which is homologous to Arabidopsis AGY1 (AT4G01800.2), and AtcpSecA plays a crucial role in the biogenesis of chloroplasts, as its deletion triggers retrograde signaling, ultimately leading to chloroplast weight programming and mitochondrial gene expression [87]; (6) Glyma.04g056200, which is homologous to Arabidopsis ER (AT2G26330.1), and the important role of soybean native genes (GmER and GmERL) in soybean growth and stress response, and the truncation of Arabidopsis ERECTA gene can be used to regulate the growth and stress response of different crop varieties [88, 89]; (7) Glyma.04g063800, which is homologous to Arabidopsis IRX1 (AT4G18780.1), and Arabidopsis xylem is the CesA gene synthesized by cellulose in the secondary wall of cotton fibers, which is used in different ways to construct specific specialized cell walls [90]. Further analysis is necessary to validate the specific functions of these 7 genes. Subsequently, it was determined through qRT-PCR that four genes are likely to be the major genes controlling soybean lodging. The findings of this study will provide valuable insights for future research. To determine the true controlling gene of lodging, this study consulted the expression information of different genes in public databases and conducted qRT-PCR experiments on the presumed candidate genes. The relative expression level of Glyma.04g063800 is the highest. Therefore, we believe that this gene is most likely the gene controlling lodging.

Conclusions

In conclusion, this study employed high-density genetic linkage maps for QTL mapping and conducted correlation analysis between lodging traits and other traits across six different environments. The lodging score displayed a strong and significant correlation with various traits, except for single plant grain weight. A total of 84 QTLs were identified, comprising 20 QTLs associated with lodging score, 11 with flowering time, 11 with maturity time, 12 with plant height, 9 with the number of main stem nodes, 10 with stem diameter, 6 with internode length, and 5 with grain weight per plant. These QTLs contributed to a phenotypic variation ranging from 1.26 to 66.87%, with LOD scores ranging from 2.52 to 69.22. Furthermore, this study placed particular emphasis on the consistent detection of the QTL qLD-4-1 across all six environments and identified seven new candidate genes related to lodging. Subsequent qRT-PCR analysis revealed that four of these genes are likely to play a major role in controlling soybean lodging. The findings from this research provide valuable insights for a better understanding of lodging in soybeans and hold promise for future investigations aimed at enhancing soybean yield and lodging resistance through breeding efforts.

Data availability

The data that support the findings of this study are available from the Genome Sequence Archive database at the National Genomics Data Center, Beijing Institute of Genomics (BIG), Chinese Academy of Sciences, with accession number CRA004753 (https://bigd.big.ac.cn/gsa/browse/CRA004753) and CRA004754 (https://bigd.big.ac.cn/gsa/browse/CRA004754). The phenotype dataset used during the current study is provided in the Supplementary table: Table S3.

Abbreviations

QTL:

Quantitative trait loci

RIL:

Recombinant inbred line

LOD:

Logarithm of odds

GO:

Gene Ontology

SNP:

Single nucleotide polymorphism

References

  1. Dong L, Fang C, Cheng Q, Su T, Kou K, Kong L, Zhang C, Li H, Hou Z, Zhang Y, et al. Genetic basis and adaptation trajectory of soybean from its temperate origin to tropics. NAT COMMUN. 2021;12(1):5445.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Zhou Z, Jiang Y, Wang Z, Gou Z, Lyu J, Li W, Yu Y, Shu L, Zhao Y, Ma Y, et al. Resequencing 302 wild and cultivated accessions identifies genes related to domestication and improvement in soybean. NAT BIOTECHNOL. 2015;33(4):408–14.

    Article  CAS  PubMed  Google Scholar 

  3. Ray DK, Mueller ND, West PC, Foley JA. Yield trends are insufficient to double global crop production by 2050. PLoS ONE. 2013;8(6):e66428.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Liu S, Zhang M, Feng F, Tian Z. Toward a Green Revolution for soybean. MOL PLANT. 2020;13(5):688–97.

    Article  CAS  PubMed  Google Scholar 

  5. Arachis hypogaea L.). THEOR APPL GENET 2023, 136(5):105.

  6. Kumar J, Gupta DS, Gupta S, Dubey S, Gupta P, Kumar S. Quantitative trait loci from identification to exploitation for crop improvement. PLANT CELL REP. 2017;36(8):1187–213.

    Article  CAS  PubMed  Google Scholar 

  7. Des Marais DL, Razzaque S, Hernandez KM, Garvin DF, Juenger TE. Quantitative trait loci associated with natural diversity in water-use efficiency and response to soil drying in Brachypodium distachyon. PLANT SCI. 2016;251:2–11.

    Article  CAS  PubMed  Google Scholar 

  8. THEOR APPL GENET 1990, 79(4):465–469.

  9. Kushanov FN, Turaev OS, Ernazarova DK, Gapparov BM, Oripova BB, Kudratova MK, Rafieva FU, Khalikov KK, Erjigitov DS, Khidirov MT, et al. Genetic diversity, QTL mapping, and marker-assisted Selection Technology in Cotton (Gossypium spp). FRONT PLANT SCI. 2021;12:779386.

    Article  PubMed  PubMed Central  Google Scholar 

  10. Sahu BB, Sumit R, Srivastava SK, Bhattacharyya MK. Sequence based polymorphic (SBP) marker technology for targeted genomic regions: its application in generating a molecular map of the Arabidopsis thaliana genome. BMC Genomics. 2012;13:20.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Kumar LS. DNA markers in plant improvement: an overview. BIOTECHNOL ADV. 1999;17(2–3):143–82.

    Article  CAS  PubMed  Google Scholar 

  12. Kantety RV, La Rota M, Matthews DE, Sorrells ME. Data mining for simple sequence repeats in expressed sequence tags from barley, maize, rice, sorghum and wheat. PLANT MOL BIOL. 2002;48(5–6):501–10.

    Article  CAS  PubMed  Google Scholar 

  13. Karp A, Edwards KJ, Bruford M, Funk S, Vosman B, Morgante M, Seberg O, Kremer A, Boursot P, Arctander P, et al. Molecular technologies for biodiversity evaluation: opportunities and challenges. NAT BIOTECHNOL. 1997;15(7):625–8.

    Article  CAS  PubMed  Google Scholar 

  14. Vos P, Hogers R, Bleeker M, Reijans M, van de Lee T, Hornes M, Frijters A, Pot J, Peleman J, Kuiper M, et al. AFLP: a new technique for DNA fingerprinting. NUCLEIC ACIDS RES. 1995;23(21):4407–14.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. THEOR APPL GENET 1989, 77(3):353–359.

  16. Botstein D, White RL, Skolnick M, Davis RW. Construction of a genetic linkage map in man using restriction fragment length polymorphisms. AM J HUM GENET. 1980;32(3):314–31.

    CAS  PubMed  PubMed Central  Google Scholar 

  17. Stolting KN, Nipper R, Lindtke D, Caseys C, Waeber S, Castiglione S, Lexer C. Genomic scan for single nucleotide polymorphisms reveals patterns of divergence and gene flow between ecologically divergent species. MOL ECOL. 2013;22(3):842–55.

    Article  PubMed  Google Scholar 

  18. Ganal MW, Altmann T, Roder MS. SNP identification in crop plants. CURR OPIN PLANT BIOL. 2009;12(2):211–7.

    Article  CAS  PubMed  Google Scholar 

  19. Berraies S, Cuthbert R, Knox R, Singh A, DePauw R, Ruan Y, Bokore F, Henriquez MA, Kumar S, Burt A, et al. High-density genetic mapping of Fusarium head blight resistance and agronomic traits in spring wheat. FRONT PLANT SCI. 2023;14:1134132.

    Article  PubMed  PubMed Central  Google Scholar 

  20. Gao F, Wen W, Liu J, Rasheed A, Yin G, Xia X, Wu X, He Z. Genome-wide linkage mapping of QTL for Yield Components, Plant Height and Yield-related physiological traits in the Chinese Wheat Cross Zhou 8425B/Chinese Spring. FRONT PLANT SCI. 2015;6:1099.

    Article  PubMed  PubMed Central  Google Scholar 

  21. Sun CY, Yang YM, Jia L, Liu XQ, Xu HQ, Lv HY, Huang ZW, Zhang D. QTL mapping of the genetic basis of stem diameter in soybean. Planta. 2021;253(5):109.

    Article  CAS  PubMed  Google Scholar 

  22. Lee S, Jun TH, Michel AP, Rouf Mian MA. SNP markers linked to QTL conditioning plant height, lodging, and maturity in soybean. Euphytica. 2015;203(3):521–32.

    Article  Google Scholar 

  23. Lolium multiflorum Lam.). THEOR APPL GENET 2004, 109(8):1576–1585.

  24. Tar’An B, Warkentin T, Somers DJ, Miranda D, Vandenberg A, Blade S, Woods S, Bing D, Xue A, DeKoeyer D et al: Quantitative trait loci for lodging resistance, plant height and partial resistance to mycosphaerella blight in field pea (Pisum sativum L.). THEOR APPL GENET 2003, 107(8):1482-1491.

  25. Glycine max L. Merr. THEOR APPL GENET. 1993;86(8):914–8.

    Article  Google Scholar 

  26. THEOR APPL GENET 1996, 92(5):516–523.

  27. Orf JH, Chase K, Adler FR, Mansur LM, Lark KG. Genetics of soybean agronomic traits: II. Interactions between Yield Quantitative Trait Loci in soybean. CROP SCI. 1999;39(6):1652–7.

    Article  Google Scholar 

  28. Specht JE, Chase K, Macrander M, Graef GL, Chung J, Markwell JP, Germann M, Orf JH, Lark KG. Soybean response to Water: a QTL analysis of Drought Tolerance. CROP SCI. 2001;41(2):493–509.

    Article  CAS  Google Scholar 

  29. Chapman A, Pantalone VR, Ustun A, Allen FL, Landau-Ellis D, Trigiano RN, Gresshoff PM. Quantitative trait loci for agronomic and seed quality traits in an F2 and F4:6 soybean population. Euphytica. 2003;129(3):387–93.

    Article  CAS  Google Scholar 

  30. THEOR APPL GENET 2004, 108(3):458–467.

  31. Glycine max L. Merr.) genetic map and their association with EST markers. THEOR APPL GENET 2004, 108(6):1131–1139.

  32. THEOR APPL GENET 2013, 126(7):1809–1823.

  33. × high-yielding exotic soybean lines. THEOR APPL GENET 2009, 119(3):429–436.

  34. Guzman PS, Diers BW, Neece DJ, St. Martin SK, LeRoy AR, Grau CR, Hughes TJ, Nelson RL. QTL Associated with yield in three backcross-derived populations of soybean. CROP SCI. 2007;47(1):111–22.

    Article  CAS  Google Scholar 

  35. Reinprecht Y, Poysa VW, Yu K, Rajcan I, Ablett GR, Pauls KP. Seed and agronomic QTL in low linolenic acid, lipoxygenase-free soybean (Glycine max (L.) Merrill) germplasm. GENOME. 2006;49(12):1510–27.

    Article  CAS  PubMed  Google Scholar 

  36. THEOR APPL GENET 2010, 120(5):997–1003.

  37. THEOR APPL GENET 2012, 125(6):1353–1369.

  38. THEOR APPL GENET 2019, 132(6):1833–1845.

  39. Kong L, Lu S, Wang Y, Fang C, Wang F, Nan H, Su T, Li S, Zhang F, Li X, et al. Quantitative trait locus mapping of flowering time and Maturity in soybean using next-generation sequencing-based analysis. FRONT PLANT SCI. 2018;9:995.

    Article  PubMed  PubMed Central  Google Scholar 

  40. Chen H, Shan Z, Sha A, Wu B, Yang Z, Chen S, Zhou R, Zhou X. Quantitative trait loci analysis of stem strength and related traits in soybean. Euphytica. 2011;179(3):485–97.

    Article  Google Scholar 

  41. Luo S, Jia J, Liu R, Wei R, Guo Z, Cai Z, Chen B, Liang F, Xia Q, Nian H, et al. Identification of major QTLs for soybean seed size and seed weight traits using a RIL population in different environments. FRONT PLANT SCI. 2022;13:1094112.

    Article  PubMed  Google Scholar 

  42. Asekova S, Kulkarni KP, Kim M, Kim JH, Song JT, Shannon JG, Lee JD. Novel quantitative trait loci for Forage Quality traits in a Cross between PI 483463 and ‘Hutcheson’ in soybean. CROP SCI. 2016;56(5):2600–11.

    Article  Google Scholar 

  43. THEOR APPL GENET 2014, 127(3):659–675.

  44. JIA J, WANG H, CAI Z, WEI R, HUANG J, XIA Q, XIAO X, NIAN MAQ, CHENG H. Identification and validation of stable and novel quantitative trait loci for pod shattering in soybean [Glycinemax (L.) Merr]. J INTEGR AGR. 2022;21(11):3169–84.

    Article  CAS  Google Scholar 

  45. Xian P, Cai Z, Jiang B, Xia Q, Cheng Y, Yang Y, Zhou Q, Lian T, Ma Q, Wang Y, et al. GmRmd1 encodes a TIR-NBS-BSP protein and confers resistance to powdery mildew in soybean. PLANT COMMUN. 2022;3(6):100418.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. THEOR APPL GENET 2018, 131(8):1715–1728.

  47. Wang Y, Yu K, Poysa V, Shi C, Zhou Y. Selection of reference genes for normalization of qRT-PCR analysis of differentially expressed genes in soybean exposed to cadmium. MOL BIOL REP. 2012;39(2):1585–94.

    Article  CAS  PubMed  Google Scholar 

  48. Xian P, Cai Z, Cheng Y, Lin R, Lian T, Ma Q, Nian H. Wild Soybean Oxalyl-CoA Synthetase Degrades Oxalate and Affects the Tolerance to Cadmium and Aluminum Stresses. INT J MOL SCI 2020, 21(22).

  49. Wang H, Lu H, Yang Z, Zhang Z, Li M, Zhang Z, Dai W, Song X, Olsen KM, Qiang S. Characterization of lodging variation of weedy rice. J EXP BOT. 2023;74(5):1403–19.

    Article  CAS  PubMed  Google Scholar 

  50. Hussain S, Shuxian L, Mumtaz M, Shafiq I, Iqbal N, Brestic M, Shoaib M, Sisi Q, Li W, Mei X, et al. Foliar application of silicon improves stem strength under low light stress by regulating lignin biosynthesis genes in soybean (Glycine max (L.) Merr). J HAZARD MATER. 2021;401:123256.

    Article  CAS  PubMed  Google Scholar 

  51. Mehrabi Z, Ramankutty N. Synchronized failure of global crop production. NAT ECOL EVOL. 2019;3(5):780–6.

    Article  PubMed  Google Scholar 

  52. THEOR APPL GENET 2021, 134(4):1007–1014.

  53. Fang T, Bai Y, Huang W, Wu Y, Yuan Z, Luan X, Liu X, Sun L. Identification of potential Gene Regulatory pathways affecting the ratio of four-seed pod in soybean. FRONT GENET 2021, 12.

  54. Yue Y, Liu N, Jiang B, Li M, Wang H, Jiang Z, Pan H, Xia Q, Ma Q, Han T, et al. A single nucleotide deletion in J encoding GmELF3 confers long juvenility and is Associated with adaption of tropic soybean. MOL PLANT. 2017;10(4):656–8.

    Article  CAS  PubMed  Google Scholar 

  55. Zimmer G, Miller MJ, Steketee CJ, Jackson SA, de Tunes L, Li Z. Genetic control and allele variation among soybean maturity groups 000 through IX. PLANT GENOME-US. 2021;14(3):e20146.

    Article  CAS  Google Scholar 

  56. Liu B, Watanabe S, Uchiyama T, Kong F, Kanazawa A, Xia Z, Nagamatsu A, Arai M, Yamada T, Kitamura K, et al. The soybean stem growth habit gene Dt1 is an ortholog of Arabidopsis TERMINAL FLOWER1. PLANT PHYSIOL. 2010;153(1):198–210.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Yao D, Liu ZZ, Zhang J, Liu SY, Qu J, Guan SY, Pan LD, Wang D, Liu JW, Wang PW. Analysis of quantitative trait loci for main plant traits in soybean. GENET MOL RES. 2015;14(2):6101–9.

    Article  CAS  PubMed  Google Scholar 

  58. Glycine max L. Merr. THEOR APPL GENET. 2006;112(4):665–73.

    Article  Google Scholar 

  59. Lu Y, Zhang J, Guo X, Chen J, Chang R, Guan R, Qiu L. Identification of Genomic Regions Associated with Vine Growth and Plant Height of soybean. INT J MOL SCI 2022, 23(10).

  60. Hu B, Li Y, Wu H, Zhai H, Xu K, Gao Y, Zhu J, Li Y, Xia Z. Identification of quantitative trait loci underlying five major agronomic traits of soybean in three biparental populations by specific length amplified fragment sequencing (SLAF-seq). PEERJ. 2021;9:e12416.

    Article  PubMed  PubMed Central  Google Scholar 

  61. THEOR APPL GENET 2020, 133(3):1039–1054.

  62. Fang Y, Liu S, Dong Q, Zhang K, Tian Z, Li X, Li W, Qi Z, Wang Y, Tian X, et al. Linkage Analysis and Multi-locus genome-wide Association studies identify QTNs Controlling soybean plant height. FRONT PLANT SCI. 2020;11:9.

    Article  PubMed  PubMed Central  Google Scholar 

  63. Ogiso-Tanaka E, Shimizu T, Hajika M, Kaga A, Ishimoto M. Highly multiplexed AmpliSeq technology identifies novel variation of flowering time-related genes in soybean (Glycine max). DNA RES. 2019;26(3):243–60.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  64. Assefa T, Otyama PI, Brown AV, Kalberer SR, Kulkarni RS, Cannon SB. Genome-wide associations and epistatic interactions for internode number, plant height, seed weight and seed yield in soybean. BMC Genomics. 2019;20(1):527.

    Article  PubMed  PubMed Central  Google Scholar 

  65. Li R, Jiang H, Zhang Z, Zhao Y, Xie J, Wang Q, Zheng H, Hou L, Xiong X, Xin D et al. Combined linkage mapping and BSA to identify QTL and candidate genes for Plant Height and the number of nodes on the main stem in soybean. INT J MOL SCI 2019, 21(1).

  66. Cao Y, Li S, Chen G, Wang Y, Bhat JA, Karikari B, Kong J, Gai J, Zhao T. Deciphering the Genetic Architecture of Plant Height in soybean using two RIL populations sharing a common M8206 parent. PLANTS-BASEL 2019, 8(10).

  67. Oki N, Sayama T, Ishimoto M, Yokota I, Kaga A, Takahashi M, Takahashi M. Quantitative trait loci associated with short inter-node length in soybean. Breed SCI. 2018;68(5):554–60.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  68. Zhang X, Wang W, Guo N, Zhang Y, Bu Y, Zhao J, Xing H. Combining QTL-seq and linkage mapping to fine map a wild soybean allele characteristic of greater plant height. BMC Genomics. 2018;19(1):226.

    Article  PubMed  PubMed Central  Google Scholar 

  69. THEOR APPL GENET 2017, 130(11):2297–2314.

  70. Watanabe S, Xia Z, Hideshima R, Tsubokura Y, Sato S, Yamanaka N, Takahashi R, Anai T, Tabata S, Kitamura K, et al. A map-based cloning strategy employing a residual heterozygous line reveals that the GIGANTEA gene is involved in soybean maturity and flowering. Genetics. 2011;188(2):395–407.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  71. Muszynska A, Guendel A, Melzer M, Tandron Moya YA, Röder MS, Rolletschek H, Rutten T, Munz E, Melz G, Ortleb S, et al. A mechanistic view on lodging resistance in rye and wheat: a multiscale comparative study. PLANT BIOTECHNOL J. 2021;19(12):2646–61.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  72. Ma QH. Functional analysis of a cinnamyl alcohol dehydrogenase involved in lignin biosynthesis in wheat. J EXP BOT. 2010;61(10):2735–44.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  73. Chai S, Yao Q, Zhang X, Xiao X, Fan X, Zeng J, Sha L, Kang H, Zhang H, Li J et al. The semi-dwarfing gene rht-dp from dwarf Polish wheat (Triticum polonicum L.) is the Green Revolution gene Rht-B1b. BMC Genomics 2021, 22(1).

  74. Ford BA, Foo E, Sharwood R, Karafiatova M, Vrana J, MacMillan C, Nichols DS, Steuernagel B, Uauy C, Dolezel J, et al. Rht18 semidwarfism in wheat is due to increased GA 2-oxidaseA9 expression and reduced GA Content. PLANT PHYSIOL. 2018;177(1):168–80.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  75. Li P, Liu Y, Tan W, Chen J, Zhu M, Lv Y, Liu Y, Yu S, Zhang W, Cai H. Brittle culm 1 encodes a COBRA-Like protein involved in secondary cell Wall Cellulose Biosynthesis in Sorghum. PLANT CELL PHYSIOL. 2019;60(4):788–801.

    Article  CAS  PubMed  Google Scholar 

  76. Li H, Wang L, Liu M, Dong Z, Li Q, Fei S, Xiang H, Liu B, Jin W. Maize Plant Architecture is regulated by the Ethylene Biosynthetic Gene ZmACS7. PLANT PHYSIOL. 2020;183(3):1184–99.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  77. Shi J, Drummond BJ, Habben JE, Brugire N, Weers BP, Hakimi SM, Lafitte HR, Schussler JR, Mo H, Beatty M, et al. Ectopic expression of ARGOS8 reveals a role for ethylene in root-lodging resistance in maize. Plant J. 2019;97(2):378–90.

    Article  CAS  PubMed  Google Scholar 

  78. Liu L, Liu S, Lu H, Tian Z, Zhao H, Wei D, Wang S, Huang Z. Integration of transcriptome and metabolome analyses reveals Ke lodging-resistance-related genes and metabolic pathways in maize. FRONT GENET. 2022;13:1001195.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  79. Ke S, Luan X, Liang J, Hung Y, Hsieh T, Zhang X. Rice OsPEX1, an extensin-like protein, affects lignin biosynthesis and plant growth. PLANT MOL BIOL. 2019;100(1–2):151–61.

    Article  CAS  PubMed  Google Scholar 

  80. Li F, Liu S, Xu H, Xu Q. A novel FC17/CESA4 mutation causes increased biomass saccharification and lodging resistance by remodeling cell wall in rice. BIOTECHNOL BIOFUELS 2018, 11(1).

  81. Guo S, Zhang X, Bai Q, Zhao W, Fang Y, Zhou S, Zhao B, He L, Chen J. Cloning and Functional Analysis of Dwarf Gene Mini Plant 1 (MNP1) in Medicago truncatula. INT J MOL SCI 2020, 21(14).

  82. Liew LC, Singh MB, Bhalla PL. A novel role of the soybean clock gene LUX ARRHYTHMO in male reproductive development. SCI REP-UK. 2017;7(1):10605.

    Article  Google Scholar 

  83. Lu S, Zhao X, Hu Y, Liu S, Nan H, Li X, Fang C, Cao D, Shi X, Kong L, et al. Natural variation at the soybean J locus improves adaptation to the tropics and enhances yield. NAT GENET. 2017;49(5):773–9.

    Article  CAS  PubMed  Google Scholar 

  84. Gomez-Mena C, Sablowski R. ARABIDOPSIS THALIANA HOMEOBOX GENE1 establishes the basal boundaries of shoot organs and controls stem growth. PLANT CELL. 2008;20(8):2059–72.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  85. Hudson D, Guevara D, Yaish MW, Hannam C, Long N, Clarke JD, Bi YM, Rothstein SJ. GNC and CGA1 modulate chlorophyll biosynthesis and glutamate synthase (GLU1/Fd-GOGAT) expression in Arabidopsis. PLoS ONE. 2011;6(11):e26765.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  86. Lazar G, Goodman HM. MAX1, a regulator of the flavonoid pathway, controls vegetative axillary bud outgrowth in Arabidopsis. P NATL ACAD SCI USA. 2006;103(2):472–6.

    Article  CAS  Google Scholar 

  87. Liu D, Gong Q, Ma Y, Li P, Li J, Yang S, Yuan L, Yu Y, Pan D, Xu F, et al. cpSecA, a thylakoid protein translocase subunit, is essential for photosynthetic development in Arabidopsis. J EXP BOT. 2010;61(6):1655–69.

    Article  CAS  PubMed  Google Scholar 

  88. Shanmugam S, Zhao S, Nandy S, Srivastava V, Khodakovskaya M. Modification of soybean growth and abiotic stress tolerance by expression of truncated ERECTA protein from Arabidopsis thaliana. PLoS ONE. 2020;15(5):e233383.

    Article  Google Scholar 

  89. Villagarcia H, Morin AC, Shpak ED, Khodakovskaya MV. Modification of tomato growth by expression of truncated ERECTA protein from Arabidopsis thaliana. J EXP BOT. 2012;63(18):6493–504.

    Article  CAS  PubMed  Google Scholar 

  90. Betancur L, Singh B, Rapp RA, Wendel JF, Marks MD, Roberts AW, Haigler CH: Phylogenetically distinct cellulose synthase genes support secondary wall thickening in arabidopsis shoot trichomes and cotton fiber. J Integr Plant Biol 2010, 52(2):205–220.

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Funding

This work was supported by the China Agriculture Research System (CARS-04-PS12), the Key-Areas Research and Development Program of Guangdong Province (2020B02022008), and Guangdong Agricultural Research System (2023KJ136-03).

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  1. Bo Chen, Cheng Chai and Mingming Duan are contributed equally to this work.

Authors and Affiliations

  1. The Key Laboratory of Plant Molecular Breeding of Guangdong Province, College of Agriculture, South China Agricultural University, Guangzhou, Guangdong, 510642, People’s Republic of China

    Bo Chen, Cheng Chai, Mingming Duan, Ximeng Yang, Zhandong Cai, Jia Jia, Shilin Luo, Lu Yin, Yunxia Li, Nianen Huang, Qibin Ma, Hai Nian & Yanbo Cheng

  2. The State Key Laboratory for Conservation and Utilization of Subtropical Agro-bioresources, South China Agricultural University, Guangzhou, Guangdong, 510642, People’s Republic of China

    Bo Chen, Cheng Chai, Mingming Duan, Ximeng Yang, Zhandong Cai, Jia Jia, Shilin Luo, Lu Yin, Yunxia Li, Nianen Huang, Qibin Ma, Hai Nian & Yanbo Cheng

  3. Guangdong Provincial Laboratory of Lingnan Modern Agricultural Science and Technology, South China Agricultural University, Guangzhou, Guangdong, 510642, People’s Republic of China

    Bo Chen, Cheng Chai, Mingming Duan, Ximeng Yang, Zhandong Cai, Jia Jia, Shilin Luo, Lu Yin, Yunxia Li, Nianen Huang, Qibin Ma, Hai Nian & Yanbo Cheng

  4. Rice Molecular Breeding Institute, Granlux Associated Grains, Shenzhen, Guangdong, 518023, People’s Republic of China

    Qiuju Xia

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  1. Bo Chen
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Contributions

B. C., C. C., M. D., X. Y., J. J., S. L., L. Y., Y. L., and N. H. collected the plant materials used in this study. Q. X. performed QTL mapping. B. C. and Y. C. prepared the first draft of the manuscript. B. C. and S. L. contributed to data analysis. Y. C., Q. M., Z. C., and H. N. planned, supervised, and financed this work, as well as edited the manuscript. All authors have read and approved the final version of the manuscript.

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Correspondence to Hai Nian or Yanbo Cheng.

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Chen, B., Chai, C., Duan, M. et al. Identification of quantitative trait loci for lodging and related agronomic traits in soybean (Glycine max [L.] Merr.). BMC Genomics 25, 900 (2024). https://doi.org/10.1186/s12864-024-10794-1

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BMC Genomics

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