Genome-Wide Dissection of Shade Tolerance in Soybean at Seedling Stage

Abstract

Dense planting and intercropping are the main ways to improve soybean production. However, both confront inter- and intra-crop shading stress. This leads to stem elongation, resulting in lodging and yield losses. Most previous studies have focused on the later growth stages for shade tolerance. However, it has been found that the seedling stage is crucial, and understanding the genetic basis of shade tolerance at this stage is pivotal for yield improvement. In this study, 310 soybean accessions were used to evaluate shade tolerance under greenhouse conditions. Plant height (PH), main stem length (MSL), and hypocotyl length (HL) were examined at seedling stage, and their treatment/control ratios (PH_r, MSL_r, HL_r) were used for genetic dissection of shade tolerance. Their overall phenotypic variation and heritability (H²) ranged 22.97–36.85% and 31.66–83.81%, respectively. RTM-GWAS identified 12, 10, and 6 QTLs associated with PH_r, MSL_r, and HL_r, respectively. Among these, Block_17_11907536_11926235 was associated with both PH_r and MSL_r, and Block_1_55630414_55715065 associated with the HL_r trait showed the highest contribution (R² = 10.38%). Additionally, seven promising candidate genes, primarily linked to ethylene-responsive transcription factors, were proposed, supported by their established roles in plant development and stress responses, as evidenced in prior studies. The germplasm, QTLs, and candidate genes identified in this study improve our understanding of shade tolerance and have the potential to accelerate the breeding of shade-resilient soybeans.

1. Introduction

Soybean is one of the most important economic crops and provides about 70% of high-quality plant-sourced protein and 28% of vegetable oil globally [1]. With the growing world population, the demand for soybean is rising continuously. Dense planting and intercropping are effective strategies to enhance soybean production within limited land resources. Dense planting has promoted significant yield increases in wheat, rice, and maize [2,3,4]. Similarly, soybean–maize strip intercropping has also been proved successful in China [5]. However, both dense planting and intercropping can lead to proximity and shading, which result in stem and petiole elongation, resulting in lodging and yield loss, known as shade avoidance response (SAR) [6,7,8].

Shading alters the ambient light environment by reducing the intensity of red light (R) while relatively increasing far-red light (FR) intensity, leading to a decrease in the R:FR ratio [9]. Plant photoreceptors, such as phytochromes (PHYs) [10], cryptochromes (CRYs) [11], phototropins [12], and UVR8 [13], detect these changes in light and initiate the SAR. In addition to light perception, plant hormones play a crucial role in the signaling pathways that mediate SAR. Key hormones involved in SAR include auxin (IAA), ethylene (ETH), gibberellin (GA), cytokinin (CTK), abscisic acid (ABA), and strigolactone (SL) [14,15,16]. These hormones regulate cell growth and division, thereby orchestrating the plant’s response to shading.

Although the mechanisms of the SAR have been well explored in Arabidopsis thaliana, it remains mostly unknown in soybean. Jia et al. [17] assessed the shade tolerance of 264 soybean natural accessions under 30% shading (light reduction) by manually phenotyping six SAR-related traits at maturity stage, such as plant height, branch number, and main stem node number. They identified 733 QTNs and four candidate genes over two years. Su et al. [18] used 246 recombinant inbred lines derived from two contrasting parents in shade tolerance, followed by a restricted two-stage multi-locus genome-wide association study (RTM-GWAS) based on gene/allele sequence markers for shade tolerance index (STI) and relative pitch cell length (RCL) at 50 days after planting. They identified 140 and 146 genes associated with STI and RCL, respectively. In a separate study, Su et al. [19] used 394 soybean accessions and identified 53 main-effect STI genes with 281 alleles via GWAS. These studies demonstrated complex traits and provided important insights into the genetic basis of shade tolerance in soybean. Recent studies have also identified genes conditioning the soybean SAR. Light-activated GmCRY1s increase the abundance of the bZIP transcription factors STF1 and STF2, which directly upregulate the expression of genes encoding GA2 oxidases to deactivate GA1 and repress stem elongation [20]. The soybean gene PH13 on chromosome 13 encodes a WD40 protein. It interacts with GmCOP1s to prevent the accumulation of STF1/2 and increase plant height. Deletion of both PH13 and its paralogue PHP results in decreasing SAR and allows high-density planting [21]. These studies have contributed to the ongoing effort to improve soybean varieties, for better performance under shaded conditions.

The objectives of this study were to dissect the genetic basis of shade tolerance at seedling stage using SAR-related traits like plant height, main stem length, and hypocotyl length, and to identify the superior genetic resources, associated loci, and candidate genes for SAR in soybean.

2. Materials and Methods

2.1. Experimental Materials, Design, and Phenotypic Measurements

A total of 310 soybean accessions, a subset of 424 re-sequenced genotypes [22], were used for the shade tolerance experiment. These materials mostly originated from the Huanghuai region of China, and North China (Supplementary Materials, Table S1). Phenotypic experiments were conducted in two replications using a randomized complete block design under greenhouse conditions. The greenhouse temperature was maintained around 26 °C throughout the experiment. LED lights (model: XM-PART8W36, power: 36 W, size: 1200 × 35 × 35 mm) were used. The light intensity was measured using a photometer (OHSP-350). Soybean materials were exposed with 75% shading (145 μmol·m⁻²·s⁻²) (75-treatment) and with normal illumination (580 μmol·m⁻²·s⁻²) (0-control) under a 12 h light/8 h dark photoperiod. Three seeds were planted in each small pot (10 × 10 × 9 cm) and two with consistent growth after germination were kept for further study.

After 15 days of planting, measurements of the plant height (PH), main stem length (MSL), and hypocotyl length (HL) were taken manually. PH is defined as the distance from the base of the plant to the top of the main stem growth point. MSL is the distance between the cotyledon node and the top of the main stem growth point. HL is the distance between the base of the plant and the cotyledon node (Figure 1A).

2.2. Statistical Analysis of Phenotype Data

Variance components of each trait were calculated using the ‘lme4’ R package (V1.1-35), where a simple linear model with genotype and replication was applied, both as random effects. The following formula was used for heritability calculation:

$${\mathrm{H}}^2={\displaystyle \frac{V_g}{V_g+V_e/t}}$$

(1)

where ‘H²’ is broad-sense heritability, ‘V_g’ is the genetic variance and ‘V_e’ is the residual variance, and ‘t’ shows the number of replications.

2.3. SNP Linkage Disequilibrium (LD), PCA, and Kinship

In this study, 3,579,423 SNP markers for 310 individuals were acquired by data filtering (MAF > 0.05) using PLINK 1.9. A total of 34,207 SNP Linkage Disequilibrium Blocks (SNPLDBs) were built using Haploveiw (V4.2) based on the parameter setting suggested by the RTM-GWAS version 1.1 [23]. PCA gravel maps were plotted using the R ‘ggplot2’ package [24]. The kinship relationship between various materials was calculated using the Centered IBS method in the TASSEL software [25], and the resulting K matrix was utilized to depict the kinship relationship. The heatmap of the association was created in R using the ‘pheatmap’ package [26].

2.4. Genome-Wide Association Analysis and Candidate Genes Identification

Association analysis was conducted using the ratios (treatment/control) of each of the three traits related to shade tolerance with the RTM-GWAS software version 1.1 [23], which also allowed estimation of the individual impact of QTL alleles. The heritability of the traits was used as the upper limit of the model interpretation rate. By default, the significance level of QTL identification was set to 0.01. Putative candidate genes were identified using ±50 genomic regions of SNPLDB using the ZH13.a1 gene model in the Soybase (https://soybase.org/, accessed on 5 August 2024) database, and for comparison and annotation, W82.a2 version in the SoyMD database (https://yanglab.hzau.edu.cn/SoyMD/, accessed on 5 August 2024) was used.

3. Results

3.1. Descriptive Statistics of Shade-Tolerance-Related Traits

The relationship between the control and treatment groups for all three traits is shown in Figure 1B–D. The treatment group generally exhibited higher values, especially for PH and MSL, indicating SAR induced by shade treatment. However, certain genotypes showed flat or non-responsive trends (e.g., horizontal regression lines), indicating no detectable response to shade treatment. This phenotypic variability indicated the differential tolerance to shade among individuals, with certain plants showing less or no change in growth. The descriptive statistics and correlation analysis for the treatment group traits provided valuable insights into their variability, genetic control, and interrelationships (

Table 1. Descriptive statistics of shade-tolerance-related traits under control (_0) and treatment (_75) conditions and ratios (treatment/control) (_r) of traits.

Trait	Min	Max	Mean	1 SD	2 PCV (%)	3 H2 (%)
PH_0	3.32	12.32	7.66	1.76	22.97	83.81
PH_75	8.8	27.32	18.08	4.94	27.33	77.13
PH_r	1.3	5.41	2.43	0.75	30.71	57.49
MSL_0	1.3	8.86	4.56	1.23	26.99	80.06
MSL_75	5.5	24.05	13.18	4.19	31.82	73.29
MSL_r	1.01	8.37	3.01	1.08	35.79	50
HL_0	0.68	5.36	3.10	0.88	28.48	73.88
HL_75	2.22	8.25	4.92	1.56	31.77	67.75
HL_r	0.66	4.73	1.66	0.61	36.85	31.66

¹ Standard deviation, ² Phenotypic coefficient of variance, ³ Broad-sense heritability.

, Figure 2).

The traits under control conditions exhibited the highest heritability, followed by those under treatment condition, with the treatment/control ratio showing the lowest heritability. In contrast, the phenotypic coefficient of variation (PCV) displayed the opposite trend (Table 1). Under control conditions, both HL_0 and PH_0, and MSL_0 and PH_0 were highly correlated, with correlation coefficients of r = 0.78 and r = 0.90, respectively, suggesting a similar genetic basis for these traits in non-stress environments. Conversely, under shade conditions, HL_75 showed no significant correlation with MSL_75 (r = 0.008) and only a weak correlation with PH_75 (r = 0.35) (Figure 2), indicating that shade treatment had a marked yet uneven impact across traits. Nevertheless, similarly to the control conditions, MSL_75 maintained a strong correlation with PH_75 (r = 0.93), evidently indicating that MSL and PH shared a substantial genetic basis.

3.2. LD, PCA, and Kinship

The distance at which the LD coefficient falls to half of its greatest value is known as the LD attenuation distance [27]. In this study, the maximum value of the average LD coefficient r² of 20 chromosomes was 0.88, and the average attenuation distance was 295 kb when it was cut in half to 0.44 (Figure 3A). Studies found that the LD attenuation distance of soybeans was mostly between 200 and 300 kb [28], and the LD attenuation distance in this study was within this range. A total of 3,579,423 SNP markers were used for PCA after the genotype data of 310 materials had been filtered. The first six components were used in the association analysis. They explained 52.04% of the total genetic variance, which declined slowly from PC6 (Figure 3B). The kinship matrix analysis revealed low pairwise kinship coefficients (range: 0.01–0.15), indicating minimal relatedness between accessions. This population structure, with over 95% pairwise kinship values less than 0.1, provided a diverse genetic resource for association mapping (Figure 3C).

3.3. Light Intensity Effects on Soybean Plant Height in Control Group

To investigate if the plant height variation was confounded with the variation in light intensity, we assessed the light intensity in the area of each pot (Figure 3D). Covariance analysis was conducted by taking the light intensity as the covariate, 310 soybean genotypes as the independent variable, and plant height as the dependent variable. The variety type had a significant impact on plant height, p < 0.05, and an effect size of 0.757. However, there was no significant effect on light intensity, p > 0.05, with an effect size of 0.001 (

Table 2. Covariance analysis of light intensity in different regions.

Source of Variation	DF	MSE	F	p	η2
Intercept	1	464.79	73.94	0	0.21
Light	1	1.91	0.31	0.58	0.001
Accession	306	18.22	2.90	0	0.76
Error	284	6.29
Total	592

). This evidently showed that, in a single habitat, the regional light intensity change is not associated with the variation in plant height.

3.4. Finding Superior Shade-Tolerant Soybean Germplasm

A total of 310 materials were screened for shade tolerance, with the ratios (treatment/control) of plant height used as the index. The five most shade-tolerant genotypes with the lowest PH_r values were identified based on the average ratios from two replications of plant height measurements (Table S2). Noticeably, all the shade-tolerant materials were cultivars. The tolerant genotypes originated from different regions: Anhui, Shanxi, and Henan. Their values ranged from 1.66 to 1.75. Similarly, among the five most shade-sensitive genotypes, three of them originated from Guangxi and Jiangxi, with their relative values ranging from 3.72 to 4.28.

3.5. GWAS and Proposed QTLs and Candidate Genes

Association analysis was conducted using 310 individuals and 34,207 SNPLDs with the RTM association model across ratios of three traits. A total of 28 QTLs with seven candidate genes associated with shade tolerance were identified (

Table 3. QTLs and proposed candidate genes.

Trait	QTLs	SNPLDBs	Chr.	Model p Value	Allele No.	SNP No.	QTL Main Effect		Candidate Genes
							−log10(p)	R2 (%)	ZH13 Gene Number	W82.a2 Gene Number	Description
PH_r
	PH_r_shade.1.1	Block_1_11646785_11746784	1	8.23 × 10−5	11	320	3.66	3.78	SoyZH13_01G064100	Glyma.01G068600	Transcription factor bHLH49
	PH_r_shade.3.1	Block_3_3114852_3115149	3	1.38 × 10−11	4	7	7.25	5.76
	PH_r_shade.10.1	Block_10_4801122_4860661	10	0.001718	8	219	2.68	2.39	SoyZH13_10G051100	Glyma.10G053500	Auxin response factor 16
	PH_r_shade.12.1	Block_12_37743951_37744542	12	0.000161	2	4	3.63	1.47
	PH_r_shade.15.1	Block_15_23412759_23416902	15	3.06 × 10−6	5	56	4.52	3.25
	PH_r_shade.16.1	Block_16_33893242_33893951	16	3.29 × 10−18	6	24	10.89	10.18
	PH_r_shade.16.2	Block_16_35747737_35749257	16	4.49 × 10−9	8	12	6.04	5.62
	PH_r_shade.16.3	Chr16_36081826	16	0.000499	2	2	3.3	1.25
	PH_r_shade.17.1	Block_17_7316304_7397881	17	2.52 × 10−5	5	333	3.91	2.77
	PH_r_shade.17.2	Block_17_11907536_11926235	17	2.2 × 10−7	7	134	5.12	4.4	SoyZH13_17G140200	Glyma.17G145300	Ethylene-responsive transcription factor 5
									SoyZH13_17G140300	Glyma.17G145400	Ethylene-responsive transcription factor 1A
	PH_r_shade.17.3	Block_17_11933118_12033106	17	1.04 × 10−10	11	507	6.56	7.37
	PH_r_shade.19.1	Chr19_478140	19	9.47 × 10−5	2	2	3.63	1.58
MSL_r
	MSL_r_shade.3.1	Block_3_3253737_3254065	3	1.38 × 10−10	4	5	7.34	6.36
	MSL_r_shade.4.1	Block_4_45771799_45788317	4	1.10 × 10−12	3	147	8.57	7.21
	MSL_r_shade.8.1	Block_8_15201156_15201636	8	2.33 × 10−5	3	3	4.20	2.71
	MSL_r_shade.11.1	Block_11_4331465_4356051	11	5.16 × 10−9	6	62	6.86	6.12
	MSL_r_shade.12.1	Chr12_1168266	12	9.72 × 10−7	2	2	5.35	3.05
	MSL_r_shade.16.1	Chr16_34243799	16	0.000268	2	2	3.42	1.68
	MSL_r_shade.17.1	Block_17_11907536_11926235	17	5.3 × 10−9	7	134	5.12	4.4	SoyZH13_17G140200	Glyma.17G145300	Ethylene-responsive transcription factor 5
									SoyZH13_17G140300	Glyma.17G145400	Ethylene-responsive transcription factor 1A
	MSL_r_shade.18.1	Chr18_55533676	18	0.000368	2	2	3.35	1.60	SoyZH13_18G217700	Glyma.18G246000	Transcription factor bHLH25
	MSL_r_shade.19.1	Block_19_476384_476514	19	5.79 × 10−5	3	4	3.95	2.47
	MSL_r_shade.20.1	Block_20_1172558_1173696	20	0.00082	7	10	3.09	2.90	SoyZH13_20G012300	Glyma.20G013200	U-box domain-containing protein 10
HL_r
	HL_r_shade.1.1	Block_1_55630414_55715065	1	4.04 × 10−11	11	224	8.09	10.38
	HL_r_shade.7.1	Block_7_6887632_6888597	7	4.32 × 10−5	3	13	4.36	2.86
	HL_r_shade.7.2	Block_7_27001385_27047263	7	6.66 × 10−6	7	44	4.97	4.88
	HL_r_shade.10.1	Block_10_8406436_8478177	10	1.21 × 10−6	9	370	5.10	6.10
	HL_r_shade.13.1	Block_13_35550086_35550267	13	5.67 × 10−6	2	2	4.89	2.93	SoyZH13_13G214700	Glyma.13G236500	Ethylene-responsive transcription factor 9
	HL_r_shade.20.1	Chr20_18734811	20	1.36 × 10−6	2	2	5.07	3.33

) (Figure 4).

For the PH_r trait, the association analysis identified 12 SNPLDBs, with R² ranging from 1.25% to 10.18%, explaining 49.82% of the phenotypic variation. These loci were distributed across chromosomes 1, 3, 10, 12, 15, 16, 17, and 19, with chromosomes 16 and 17 exhibiting the highest distribution (3 loci). The locus with the highest contribution rate was Block_16_33893242_33893951, with an R² value of 10.18%. This genomic block comprised a total of 24 SNPs and contained six distinct alleles. A total of four candidate genes were proposed with the PH_r trait.

Ten SNPLDBs were identified for the MSL_r trait, located on chromosomes 3, 4, 8, 11, 12, 16, 17, 18, 19, and 20. The contribution rates (R²) of these sites ranged from 1.6% to 7.21%, explaining 38.5% of the phenotypic variation. The highest contribution was observed at Block_4_45771799_45788317. This trait resulted in the identification of four candidate genes.

Importantly, for the PH_r and MSL_r traits, GWAS analysis identified a significant genomic region, Block_17_11907536_11926235, located on chromosome 17. This region demonstrated a strong association with both traits (Table 3). The block encompassed a total of 134 SNPs with seven haplotypes. To better understand the phenotypic impact of these alleles, the distribution of PH_r values across the seven haplotypes was visualized using violin plots (Figure 5). Notably, this region harbored two candidate genes, Glyma.17G145300 (SoyZH13_17G140200) and Glyma.17G145400 (SoyZH13_17G140300), located at genomic positions Chr17_11923933 and Chr17_11940242, respectively. Glyma.17G145300 was located approximately 16.40 KB away from the SNPLDB Block_17_11907536_11926235, while Glyma.17G145400 was located approximately 13.81 KB away from the block. These genes encode ethylene-responsive transcription factor 5 and ethylene-responsive transcription factor 1A, both of which are known to play critical roles in plant growth and development. Ethylene-responsive transcription factors are known to play crucial roles in plant growth, suggesting promising candidate regulators of the shade tolerance in soybean.

To dissect the genetic architecture of this region, we analyzed LD patterns across the 59.9 kb span (Chr17:11896252–11956196) encompassing the block. Figure 6 illustrates the LD structure, with the focal block (highlighted in the triangular heatmap) showing strong pairwise correlations among SNPs. Both SoyZH13_17G140200 (positioned ~16.4 kb downstream) and SoyZH13_17G140300 (~13.8 kb upstream) reside within the high-LD zone, suggesting their potential co-inheritance with trait-associated variants. The ERF genes in this locus are compelling candidates, as in Arabidopsis, ethylene signaling modulates hypocotyl elongation and photo-morphogenesis [29].

For the HL_r trait, GWAS analysis identified six significant SNPLDBs across chromosomes 1, 7, 10, 13, and 20. The most significant locus, Block_1_55630414_55715065 (on chromosome 1), had the highest contribution rate (R² = 10.38%) and a strong association (−Lg(p) = 8.09). Other notable loci included Block_10_8406436_8478177 (R² = 6.10%) and Block_7_27001385_27047263 (R² = 4.88%). A key finding was Block_13_35550086_35550267 (chromosome 13), associated with the candidate gene Glyma.13G236500 (SoyZH13_13G214700) located at Chr13_35527024, approximately 23.06 KB away from the SNPLDB Block_13_35550086_35550267, encoding an ethylene-responsive transcription factor 9.

4. Discussion

Most previous studies on soybean shade tolerance have focused on mid to late growth stages. However, recent research has suggested that the seedling stage is crucial in determining soybean shade tolerance. Zhao [30] identified a major QTL, qSAR1, associated with SAR using soybean recombinant inbred lines. Notably, qSAR1 encompasses four genes known as key regulators of the SAR network. The differential expression of these candidate genes between parental lines was confirmed via RNA-seq, and they were suggested to promote the stability of the SAR gene PIF7 [30]. This finding highlights the validity of screening soybean for shade tolerance at seedling stage. The current study investigated the soybean SAR at seedling stage, to enable early and rapid identification of shade-tolerant genotypes under greenhouse condition. Moreover, our approach holds great potential for integrating indoor high-throughput phenotyping pipelines, which could further improve the efficiency and accuracy of shade tolerance assessments.

Shading leads to excessive elongation of soybean plants, often resulting in lodging and yield losses. Optimizing canopy structure and stem characteristics can mitigate the negative effects of shading by reducing the yield loss in intercropping systems [31]. Multiple candidate genes have been identified to be involved in shade tolerance mechanisms. One of those is ATP Phosphoribosyl Transferase (ATP-PRT2), which plays a role in the biosynthesis of histidine, which is crucial for plant growth and development under shade conditions [32]. Phospho-choline Phosphatase (PEPC) is another gene associated with shade tolerance in soybean. It is involved in the metabolism of phospholipids, which are essential for maintaining cell membrane integrity and function under stress conditions such as shading [32]. Similarly, AUXIN-RESPONSIVE PROTEIN (IAA17) is part of the auxin signaling pathway, which is significantly enriched in shade-tolerant soybeans. Auxin is a plant hormone that regulates various aspects of growth and development, including responses to light conditions [32]. Another mechanism is known through GmCRY1s that modulate gibberellin metabolism [20].

To maintain equal distribution of light throughout the experimental group, it was discovered that using a surrounding border was helpful (Figure 3D). Covariance analysis results (Table 2) further demonstrated that target trait identification was not significantly impacted by variations in light intensity. Based on 310 soybean genotypes, this study examined changes in plant-height-related features under low-light treatment by setting up normal light and 75% shade treatments during the seedling stage. Our study showed that soybean plant height, main stem length, and hypocotyl length increased under low-light conditions, which is consistent with previous findings that low light promotes plant height and hypocotyl elongation. In this study, the top five shade-tolerant and five shade-sensitive genotypes were identified. Importantly, all the shade-tolerant materials were cultivated varieties, and all of them were local (originated from China), indicating that long-term breeding has enhanced shade tolerance in soybeans. This adaptation likely relates to tolerance to lodging, as shade-tolerant materials tend to exhibit strong lodging tolerance, a desirable trait in widely cultivated varieties.

With the passage of time, GWAS techniques have continuously undergone refinement to make them more effective. The RTM-GWAS method is one of those refined tools emerging as a valuable technique for QTL mining [33]. The multi-locus RTM model enhances detection efficiency compared to traditional single-point models and adjusts the phenotypic variation interpretation rate to align with heritability, reducing false positives [34]. In this study, RTM-GWAS analysis was performed on 34,207 SNPLDBs across ratios of three traits: PH, MSL, and HL. A total of 28 QTLs were identified, including seven candidate genes. Notably, PH_r and MSL_r shared a common region on chromosome 17, where two candidate genes were identified; Glyma.17G145300 and Glyma.17G145400. Glyma.17G145300 has been previously reported in other studies to be involved in growth signaling and stress responses [35,36]. Similarly, Glyma.17G145400 has been associated with flood tolerance, a trait that may relate to tolerance against plant lodging [37]. These findings suggest that both genes could play crucial roles in shade tolerance, potentially through mechanisms involving stress adaptation and structural stability. Importantly, most of the QTLs identified in our study were novel, with no direct overlapping with previous studies detected. This uniqueness is likely due to the limited availability of comparable studies, the complexity of assessing shade tolerance, and the diversity of research teams and global materials used in these investigations. The results offer a robust foundation for soybean genetic improvement and gene mining, targeting shade tolerance.

5. Conclusions

In conclusion, this study highlights the genetic basis of shade tolerance in soybean at the seedling stage, a critical phase for plant development under shading stress. Using the RTM-GWAS model, we identified 28 QTLs and seven candidate genes associated with key traits, including the ratio of plant height, main stem length, and hypocotyl length. Notably, the genomic region Block_17_11907536_11926235 associated with both PH_r and MSL_r harbored two ethylene-responsive transcription factor genes, Glyma.17G145300 and Glyma.17G145400, which were implicated in growth signaling, stress responses, and flood tolerance. Similarly, the ethylene-responsive transcription factor 9 encoded by Glyma.13G236500 was identified for the Block_13_35550086_35550267. These findings may underscore the importance of ethylene signaling in shade tolerance at the early growth stage of soybean. The promising candidate genes and valuable genetic resources identified in this study are critical for mining shade-tolerant genes and breeding resilient soybean cultivars adapted to shade stress.