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

doi:10.21203/rs.3.rs-3981888/v1

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Research Article

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

https://doi.org/10.21203/rs.3.rs-3981888/v1

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published 30 Sep, 2024

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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 10 major QTLs associated with other related traits, nine of them were situated within the qLD-4-1interval 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 three 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.

soybean

lodging

lodging related traits

quantitative trait loci

candidate genes

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 [5–8]. 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 [9–11]. 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 [12–16]. 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 [21–24]. 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 F_2:5 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 F_7:11 population from a Minsoy × Noir1 cross. Chapman et al. [29] uncovered a QTL on chromosome 11 closely linked to SSR markers in an F_4:6 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 F_2:7:11 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 [32–36]. 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 × PI 567301B. 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.

Plant materials

The RIL populations were created through a cross between two distinct soybean accessions, Guizao 1 × B13, and comprised 248 F_8:11 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 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, 22ZC, and 20GZ, respectively. The experimental planting followed a completely randomized block design, with one row allocated per line, a row length of 1.5 meters, a row spacing of 0.5 meters, and a plant spacing of 0.1 meters. 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 = σ²_g =(σ²_g + σ²_ge /n + σ²_e /nr)

where σ²_g denotes genotype variance, σ²_ge denotes genotype × environment interaction variance, and σ²_e 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 minute, followed by 40 cycles of denaturation at 95°C for 10 seconds, annealing at 55.0–60.0°C (depending on the gene) for 10 seconds, and extension at 72°C for 30 seconds [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.

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.

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% to 13.00% for flowering time, 5.99% to 7.00% for maturity time, 20.20% to 28.51% for plant height, 13.51% to 21.35% for the number of main stem nodes, 10.80% to 14.45% for stem diameter, 13.31% to 17.17% for internode length, and 18.25% to 24.42% for grain weight per plant.

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 93.14%, indicating that the lodging phenotype in soybean is primarily influenced by genotype.

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 3513907-5769624 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% to 48.70% for maturity time, 41.41% to 51.32% for plant height, 20.46% to 48.39% for the number of main stem nodes, 12.10% to 29.12% for stem diameter, and 13.60% to 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.

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 enrichment 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 enrichment 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).

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.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 three genes are the primary candidates responsible for regulating soybean lodging.

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 4075901 to 4081260 bp, suggesting a potential link between genes regulating lodging and those governing the growth period [52-54]. 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, 52-56, 59-70]. 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. The findings of this study will provide valuable insights for future research.

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 1.26–66.87%, 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 three 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.

QTL: Quantitative trait loci; RIL: Recombinant inbred line; LOD: Logarithm of odds; GO: Gene Ontology; SNP: Single nucleotide polymorphism.

Author contributions

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.

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).

Availability of data and materials

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.

Ethics approval and consent to participate

This study complies with relevant institutional, national, and international guidelines.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Author details

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

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

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

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

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

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