QTL Mapping for Seed Tocopherol Content in Soybean

Abstract

Tocopherol plays an important role as a powerful antioxidant in human beings and in plants. This study investigated the genetic basis of tocopherol content in soybean. A RIL population of 192 lines derived from 2 cultivars, ZH13 and ZH35, was evaluated for tocopherol content across 3 environments. QTL mapping identified 38 QTL for tocopherol, with stable QTL identified on Chromosomes 5 and 12. Ninety polymorphic genes were identified from these regions. Further SNP variation of a natural population identified 47 SNPs, with missense variants in 19 genes, including the heat shock transcription factor gene (GmHSFA8) and gamma-tocopherol methyltransferase (GmVTE4), potentially related to tocopherol accumulation in soybean. Haplotype analysis revealed significant variations in these missense variants in the natural population. This study provides insights into the genetic mechanisms underlying tocopherol content in soybean, which is important for breeding high tocopherol soybean cultivars.

1. Introduction

Tocopherols are a group of lipid-soluble compounds that function as antioxidants in plant and animal tissues. They play a crucial role in protecting cell membranes from oxidative damage caused by reactive oxygen species, which can result in a range of deleterious effects on cellular metabolism. Among the different forms of tocopherols, alpha-tocopherol is the most biologically active form and is the primary form of tocopherol found in human diets [1].

Soybean (Glycine max L. Merrill) is a major source of edible oil and protein for humans and livestock, and it is also an important source of tocopherols [2]. However, the tocopherol content in soybean varies widely depending on genetic and environmental factors, which can affect the nutritional and functional properties of the crop [3]. Tocopherol content in soybean is also influenced by agronomic factors, such as the type of soil, fertilizer application, and growing conditions [4]. Therefore, identifying the genetic factors that control tocopherol content in soybean is of great interest to plant breeders and nutritionists. Understanding the genetic control of tocopherol content in soybean will enable the development of new soybean cultivars with enhanced tocopherol content and can be used to guide breeding efforts for producing soybean varieties with improved nutritional value and functional properties.

Quantitative trait loci (QTL) analysis is a powerful tool for identifying the genetic basis of complex traits, including tocopherol content, in plants. QTL are genomic regions that contain one or more genes that contribute to the variation in a particular trait [5]. By identifying QTL associated with tocopherol content in soybean, we can gain insights into the molecular mechanisms that regulate this trait and develop genetic markers for marker-assisted selection in breeding programs. In recent years, several studies have reported the identification of QTL for the tocopherol content of soybeans using different genetic mapping approaches, such as linkage mapping, association mapping, and genome-wide association studies (GWAS) [6,7,8,9,10,11,12,13]. These studies have identified several candidate genes involved in tocopherol biosynthesis and metabolism and have provided valuable information for improving the tocopherol content in soybean.

However, despite these advances, there is still much to be learned about the genetic and molecular factors that control tocopherol content in soybean. In particular, the genetic basis of variation in different tocopherol forms remains poorly understood. The different forms of tocopherols have different biological activities and are selectively retained and metabolized by different tissues in the body, making it important to understand the genetic control of the individual tocopherol forms.

In this study, we aim to identify QTL for tocopherol content and its different forms in a RIL population evaluated across three environments. Our findings will provide new insights into the genetic architecture of tocopherol content in soybean and will help to guide future efforts to improve the nutritional and functional properties of this important crop.

2. Materials and Methods

2.1. Plant Materials

In this study, a mapping population comprising 192 F_7:8 RILs was used. The RILs were obtained by crossing two cultivars: Zhonghuang 13 (ZH13), which has low tocopherol content, and Zhonghuang 35 (ZH35), which has high tocopherol content. The experiments were conducted at three different locations in Beijing during the 2020 and 2021 growing seasons. The locations were the Nankou Experimental Station (40°13′ N, 116°06′ E) in 2020 (2020NK), the Changping Experimental Station (40°13′ N and 116°12′ E) in 2021 (2021CP), and the Shunyi Experimental Station (40°13′ N and 116°34′ E) in 2021 (2021SY). The planting was carried out in rows that were 2.00 m long, with an intra-row spacing of 0.10 m and an inter-row spacing of 0.50 m. The planting locations were employed as three replications. The seeds were sown in June and harvested in October. The planting and post-planting operations were conducted according to recommended agronomic practices. After maturity, the seeds of each RIL and their parental lines were harvested and air-dried. Notably, 100 g of dried seeds from each RIL and parental line were taken for analysis. The seed tocopherol content of the RIL lines and their parents was evaluated across multiple environments.

2.2. Tocopherol Extraction and Quantification

Soybean tocopherol extraction and quantification were performed in duplicate measurements as reported in our recent study [3]. Individual tocopherols, including delta, alpha, and gamma tocopherols, were analyzed.

Specifically, about 20 g of mature seeds per sample were added to the rotary kiln (IKA, A10 basic, Rheinische, Staufen, Germany) and crushed. Then, 50 mg sample powder was weighed by an electronic analytical balance (Accuracy is 0.001 g, Sartorius BS124S, Gottingen, Niedersachsen, Germany) and added into a 2 mL centrifuge tube (Axygen, Hangzhou, China). After adding 1 mL ethanol, the powder was swirled at 3000 rpm for 20 s. The mixture was placed in the ultrasonic water bath (Ningbo SCIENTZ Biotechnology Company Ltd., Ningbo, China) for 20 min at room temperature. During ultrasonic extraction, the mixture was shaken by hand twice for 10 s. Centrifugation at 13,000 rpm (4 °C) for 20 min. Finally, the supernatant was filtered into HPLC vials (ASONE, Shanghai, China) by sterile syringe (Jiangsu Zhiyu Medical Equipment Co., Ltd., Changzhou, China) and The YMC duo-filter (YMC Co., Kyoto, Japan) and stored at 4 °C for determination. Tocopherol was determined by C18 reverse-phase column (YMC ODS AM-303, 250 mm × 4.6 mm, S-5 μm, YMC Co., Kyoto, Japan) with Agilent 1260 HPLC device (Agilent, Santa Clara, CA, USA). The mobile phase was 100% methanol. The solvent flow rate was 1.5 mL min⁻¹, and the injection volume was set to 10 μL. The UV detector wavelength was 295 nm and the column temperature was 40 °C. The separation was performed for a total of 8 min under equidistant conditions. Individual tocopherol was calculated according to the correction curve.

2.3. Molecular Genotyping and QTL Mapping

To perform molecular genotyping of the RIL population, five young leaves for each line of F_2:7 RILs were randomly selected as mixed samples. Genomic DNA (gDNA) was extracted by the CTAB method [14] and sent to Beijing Novogene Bioinformation Technology Co., Ltd. for sequencing. Briefly, the DNA library was prepared by enzyme digestion, adding sequencing joint, purification, and PCR amplification of qualified DNA samples. After qualification, the library was sequenced by means of Illumina platform. With Williams 82 genome sequence (https://phytozome-next.jgi.doe.gov/info/Gmax_Wm82_a2_v1, accessed on 10 January 2023) as a reference genome sequence, the sequencing data of 2 parents and 192 RILs were compared with the reference genome, and then SNP detection was performed for 2 parents and 192 progeny, and the information on SNP was considered. High-quality SNP markers were screened by parental genotyping [15]. After genotyping, SNPs with a high missing data rate (>10%) or low p-value (<10⁻⁶) were removed based on chi-square tests of segregation distortion. The remaining SNPs were binned according to their segregation pattern using the BIN function of IciMapping V4.1 software (http://www.isbreeding.net/, accessed on 15 January 2023) [16]. The linkage groups (LGs) were identified using Joinmap v4.0 software, and the maximum likelihood algorithm was applied to determine loci order, with Kosambi’s mapping function used as the mapping function [17].

The QTL for tocopherol content was mapped using the Inclusive Composite Interval Mapping (ICIM) strategy (IciMapping V4.1 (http://www.isbreeding.net, accessed on 15 January 2023)) with a logarithm of odds (LOD) threshold based on 1000 permutation tests at a 95% confidence level [18]. The ICIM additive (ICIM-ADD) method was used [19], and QTL identified in two or more environments with a phenotypic variation that explained (R²) of >10% were considered stable and major QTL [20]. QTL located within a 10 cM distance from the same chromosome in the mapping population were considered a single QTL cluster [21,22]. If the same QTL cluster was detected in at least two environments, it was considered a consistent QTL across environments [23]. QTL names followed the nomenclature “q” for quantitative locus, the trait name, followed by the chromosome number, and the number of QTL detected on each chromosome for each trait.

2.4. In Silico Identification and Annotation of Candidate Genes

The physical position of the major QTL cluster was used to identify all model genes within the interval by utilizing the online platforms Phytozome and SoyBase. To annotate all the genes in the QTL cluster region, Gene Ontology (GO) annotation was used. Candidate genes were selected based on their gene annotations and the reported functions of genes involved in these traits. To identify potential regulators of the QTL within the QTL clusters, we utilized the Plant Transcription Factor Database (http://planttfdb.gao-lab.org/, accessed on 5 February 2023) to search for putative transcription factors (TFs).

2.5. Identification of Genes with Significant Loci Associated with Tocopherol Content in a Natural Population

The study utilized a natural population consisting of 1151 Chinese accessions that were re-sequenced using a genome-wide approach to investigate genome variations [24]. The 1151 accessions originated from 3 major soybean planting ecoregions in China: the northern region (NR), the Huang-Huai-hai Basin region (HR), and the southern region (SR). The information on the accessions has been described in detail previously [3]. The genotype data for candidate genes were retrieved and downloaded from the Soybean Functional Genomics & Breeding database (SoyFGB v2.0) (https://sfgb.rmbreeding.cn/, accessed on 15 January 2023). To determine the effects of candidate genes on tocopherol content, SNPs of candidate genes were analyzed using ANOVA in the natural population. The phenotypes were evaluated in five environments across different regions, including Beijing, Hainan, and Anhui, as well as an overall mean across all environments [3]. To filter the SNPs of accessions, reference (REF) and alternate (ALT) sequences were retained, and heterozygous and missing SNPs were removed. Important loci (p < 0.05) in multiple environments for both traits were identified, and genes associated with these loci were selected for further analysis. The Snpeff variant annotation tool was utilized to predict the annotation and effects of SNPs in exonic regions of significant genes [25]. The variants were categorized according to their impact or deleteriousness using SnpEff analysis, including high (frameshift, stop gain, and stop lost), moderate (missense and splice region), modifier (intronic and non-coding), and low (synonymous) variants.

2.6. Haplotype Analysis of Missense Variants in Candidate Gene

The gene structure of the candidate gene was illustrated using GSDS 2.0 [26]. For missense variants in the candidate gene, haplotype analysis was conducted using DNASP software [27]. Based on their haplotypes, the genotypes were categorized into specific groups. To evaluate the impact of haplotypes on tocopherol contents, an analysis of variance was performed on the tocopherol contents of these specific haplotype groups using the natural soybean population. Additionally, haplotype analysis based on the three major ecoregions in China (i.e., NR, HR, and SR) were also analyzed due to the significantly different latitudes and photoperiodic and climatic conditions which may have significant effects on the content of tocopherol [3].

2.7. Statistical Analysis

To estimate the total tocopherol content on a fresh weight (FW) basis, the individual tocopherol components (alpha, delta, and gamma-tocopherol) were added up. All phenotypic data were subjected to ANOVA using the agricolae package (https://cran.r-project.org/web/packages/agricolae, accessed on 3 January 2023) in R 3.4.5 (R Foundation for Statistical Computing, Vienna, Austria). Post-hoc mean separation was carried out using Tukey’s HSD at p < 0.05. The frequency distribution and correlation analyses were performed using the ggplot2 package (https://rdocumentation.org/packages/ggplot2/versions/3.3.6, accessed on 3 January 2023) in R, while the skewness and kurtosis were computed using the e1071 package (https://www.rdocumentation.org/packages/e1071/versions/1.7-9, accessed on 3 January 2023) in R. The broad-sense heritability (h²) was computed using Microsoft Excel 2016 following the method outlined in Owusu et al. (2021) [28].

3. Results

3.1. Phenotypic Variation of Soybean Tocopherol Content

The descriptive statistics, h², and ANOVA for the tocopherol components (alpha-, gamma-, and delta-tocopherol) of the 192 RILs and their parental lines across 3 environments are presented in

Table 1. Summary statistics for soybean tocopherol content in the parental cultivars and the RIL population across three environments.

Trait	Environment	Parent		Population							p Values from ANOVA		h2 (%)
		ZH13	ZH35	Min	Max	Range	Mean ± SD	CV (%)	Kurtosis	Skewness	VG	VE
Delta-TP	2020NK	81.17	86.99	49.13	123.69	74.56	82.86 ± 12.25	14.79	0.58	0.03	3.4 × 10−14 ***	<2 × 10−16 ***	61.04
	2021CP	89.55	108.91	65.76	135.24	69.48	96.97 ± 12.27	12.65	−0.13	0.17
	2021SY	91.55	101.10	62.82	121.27	58.45	90.57 ± 10.81	11.93	−0.14	−0.04
	Mean	87.42	99.00	59.24	126.73	67.50	90.13 ± 11.78	13.07	0.20	0.03
Gamma-TP	2020NK	134.65	144.59	86.33	225.59	139.26	152.21 ± 23.17	15.22	0.59	0.35	<2 × 10−16 ***	<2 × 10−16 ***	69.74
	2021CP	118.77	165.10	106.10	198.80	92.70	144.75 ± 17.86	12.34	−0.11	0.41
	2021SY	102.87	159.57	93.93	167.99	74.06	130.62 ± 14.95	11.44	−0.59	0.02
	Mean	118.76	156.42	95.45	197.46	102.01	142.53 ± 18.66	13.09	0.71	0.57
Alpha-TP	2020NK	8.38	8.96	1.38	22.67	21.29	10.27 ± 2.68	26.06	3.92	1.09	<2 × 10−16 ***	<2 × 10−16 ***	66.24
	2021CP	8.13	8.65	5.31	14.51	9.20	8.67 ± 1.68	19.33	0.03	0.43
	2021SY	6.75	7.96	4.17	12.10	7.93	7.65 ± 1.38	17.97	0.15	0.16
	Mean	7.75	8.52	3.62	16.43	12.81	8.86 ± 1.91	21.54	4.87	1.32
Total TP	2020NK	224.19	240.55	149.08	331.26	182.18	245.34 ± 30.37	12.38	0.69	0.22	<2 × 10−16 ***	<2 × 10−16 ***	65.03
	2021CP	216.45	282.67	192.64	343.70	151.06	250.39 ± 26.93	10.75	−0.03	0.39
	2021SY	201.17	268.63	166.86	287.81	120.95	228.84 ± 23.02	10.06	−0.34	0.00
	Mean	213.94	263.95	169.53	320.92	151.40	241.52 ± 26.77	11.08	0.38	0.33

TP—tocopherol; Mean ± SD—average value ± standard deviation; CV—coefficient of variation; V_G—genotype variance; V_E—environmental variance; ***—significance at 0.001 level; h²—heritability.

. For the parental lines, the average total tocopherol contents across the three environments differed significantly at 213.94 and 263.95 μg/g (p < 0.05) for ZH13 and ZH35, respectively. Across the three environments, total tocopherol contents ranged from 201.17 to 224.19 μg/g (average 213.94 μg/g) and 240.55 to 282.67 μg/g (average 263.95 μg/g) for ZH13 and ZH35, respectively. The total tocopherol content of the RIL population ranged from 149.08 to 331.26 μg/g in 2020NK, 192.64 to 343.70 μg/g in 2021CP, and 166.86 to 287.81 μg/g in 2021SY (Table 1), showing transgressive segregation among the RILs. The most dominant tocopherol component was gamma-tocopherol with average contents ranging from 95.45 to 197.46 μg/g, followed by delta with average contents ranging from 59.24 to 126.73 μg/g. Alpha-tocopherol contents ranged from 3.62 to 16.43 μg/g. The coefficient of variation of tocopherols ranged from 10.06% to 26.06% for all tocopherol components and total tocopherol. The h² estimates were 61.04%, 69.74%, 66.24%, and 65.03% for delta-tocopherol, gamma-tocopherol, alpha-tocopherol, and total tocopherol, respectively, across all three environments. The distribution of total tocopherol and other tocopherol components was continuous (Figure 1). Kurtosis and skewness measured for all traits also confirmed that the distribution of most tocopherol components was normal.

Correlation analysis was conducted for each component of tocopherol. As shown in Table S1, almost all components of tocopherol showed a significant positive correlation between environments, indicating that tocopherol content was stable in different environments. Pearson’s correlation analysis revealed significant correlations among the various tocopherol components (Figure 2). The highest positive correlations were observed between total tocopherol and gamma-tocopherol (r = 0.91 ***) and between total tocopherol and delta-tocopherol (r = 0.69 ***). Alpha-tocopherol also positively correlated with total tocopherol (r = 0.29 ***). The analysis of variance revealed that tocopherol contents were significantly influenced by the genotype and environment (p < 0.001) (Table 1).

3.2. Molecular Genotyping and QTL Mapping for Seed Tocopherol

In a previous study by Liu et al. (2023), whole genome sequencing for the RIL population was used to construct a high-density genetic map. The alignment rates of the two parents were above 93% and the average 1× coverage was 95.23%, indicating good sequencing data uniformity. Based on the comparison with the reference genome, SNP detection, SNP marker development, genotyping, and SNP marker screening were performed on the parents and 192 progeny, and a total of 1,034,092 effective markers were obtained. For each soybean chromosome, JoinMap 4.0 was used to sort the bin markers of each chromosome, the chromosomes were sorted by the maximum likelihood method, and the genetic distance was calculated by Kosambi’s mapping function [17]. Finally, a high-density genetic map containing 4879 bin markers was constructed, the average genetic distance was 0.77 cM, covering 20 chromosomes of the whole soybean genome (Figure S1) [15]. In the current study, this genetic map was used to map the QTL underlying seed tocopherol.

A total of 38 QTL distributed on 14 chromosomes were detected for tocopherol in the RIL population in all 3 environments (

Table 2. QTL for soybean tocopherol components in RIL population across three environments.

Trait	QTL	Chr	Position (Mb)	Left Marker	Right Marker	Position Start	Position End	LOD	R2 (%)	Add	Interval (Kb)	Environment
Delta-TP	qDelt_01_1	1	55.60	bin187	bin186	50,649,088	50,353,810	4.12	6.89	2.63	295.28	2021CP
	qDelt_04_1	4	95.80	bin923	bin922	13,1243,05	12,932,515	5.05	8.47	3.60	191.79	2021CP
	qDelt_07_1	7	104.80	bin1665	bin1664	16,631,707	16,230,613	4.59	7.94	3.47	401.09	2021CP
	qDelt_15_1	15	61.50	bin3518	bin3519	7,421,920	7,641,634	6.02	10.33	−3.24	219.71	2021SY
	qDelt_16_1	16	12.70	bin3882	bin3883	1,647,416	1,906,898	4.45	7.46	−3.38	259.48	2021CP
	qDelt_17_1	17	159.40	bin4262	bin4264	38,228,281	38,543,372	4.32	7.35	−2.73	315.09	2021SY
	qDelt_19_1	19	2.50	bin4563	bin4564	219,123	425,982	4.83	8.05	3.51	206.86	2021CP
	qDelt_19_2	19	8.50	bin4567	bin4568	617,202	835,503	6.06	10.37	3.24	218.30	2021SY
Gamma-TP	qGam_02_1	2	0.00	bin515	bin514	47,425,382	48,577,505	6.10	5.33	−3.51	1152.12	2021SY
	qGam_02_2	2	42.30	bin460	bin459	41,531,073	41,700,825	3.89	3.35	2.77	169.75	2021SY
	qGam_03_1	3	14.40	bin801	bin800	43,336,292	43,538,584	4.26	3.67	2.90	202.29	2021SY
	qGam_04_1	4	145.40	bin989	bin990	46,120,695	46,344,218	10.95	11.00	6.31	223.52	2021CP
	qGam_04_2	4	162.00	bin1004	bin1005	47,618,685	47,827,478	5.54	5.15	−4.31	208.79	2021CP
	qGam_05_1	5	0.20	bin1250	bin1253	41,845,348	42,234,498	22.88	26.65	9.89	389.15	2021CP
	qGam_05_2	5	0.60	bin1253	bin1252	42,234,498	41,990,175	4.90	10.95	8.61	244.32	2020NK
	qGam_05_3	5	3.00	bin1248	bin1247	41,398,689	41,687,940	31.40	38.37	9.50	289.25	2021SY
	qGam_07_1	7	103.80	bin1666	bin1665	16,534,777	16,725,609	8.89	8.70	5.62	190.83	2021CP
	qGam_08_1	8	16.80	bin2051	bin2050	45,332,254	45,490,876	6.17	5.41	−3.55	158.62	2021SY
	qGam_15_1	15	106.20	bin3566	bin3565	12,610,613	12,817,890	4.27	3.88	−3.76	207.28	2021CP
	qGam_18_1	18	19.70	bin4317	bin4318	2,447,058	2,6204,33	3.77	3.21	−2.71	173.38	2021SY
	qGam_19_1	19	88.00	bin4755	bin4756	38,153,383	38,469,713	4.56	4.16	−3.87	316.33	2021CP
Alpha-TP	qAlp_04_1	4	202.60	bin1046	bin1047	51,800,189	52,030,983	4.81	7.62	−0.35	230.79	2021SY
	qAlp_07_1	7	114.20	bin1656	bin1655	15,122,604	15,320,506	6.52	9.23	0.50	197.90	2021CP
	qAlp_11_1	11	81.20	bin2652	bin2651	11,385,920	11,913,632	6.16	9.91	0.40	527.71	2021SY
	qAlp_11_2	11	82.40	bin2650	bin2649	11,236,413	11,385,919	5.06	7.02	0.43	149.51	2021CP
	qAlp_12_1	12	5.80	bin2774	bin2775	941,365	1,141,110	13.05	22.94	0.60	199.75	2021SY
	qAlp_12_2	12	6.20	bin2774	bin2775	941,365	1,141,110	16.28	26.11	0.83	199.75	2021CP
	qAlp_12_3	12	9.00	bin2782	bin2783	1,853,370	2,246,403	3.97	12.48	0.94	393.03	2020NK
	qAlp_16_1	16	62.20	bin3927	bin3926	6,341,451	6,501,570	3.57	4.84	−0.36	160.12	2021CP
Total TP	qTTP_01_1	1	55.60	bin187	bin186	50,353,810	50,649,088	4.51	4.48	5.09	295.28	2021SY
	qTTP_05_1	5	2.60	bin1249	bin1248	41,617,164	41,845,347	20.60	25.15	12.17	228.18	2021SY
	qTTP_05_2	5	7.90	bin1245	bin1244	41,131,528	41,306,224	13.16	22.10	11.82	174.70	2021CP
	qTTP_07_1	7	104.70	bin1665	bin1664	16,230,613	16,631,707	6.45	10.58	8.08	401.09	2021CP
	qTTP_08_1	8	1.10	bin2068	bin2067	47,235,789	47,429,487	4.17	4.13	−4.94	193.70	2021SY
	qTTP_15_1	15	93.10	bin3551	bin3552	11,243,904	11,428,680	5.00	7.75	−6.91	184.78	2021CP
	qTTP_15_2	15	109.50	bin3570	bin3571	13,149,896	13,276,812	4.68	4.81	−5.29	126.92	2021SY
	qTTP_18_1	18	14.20	bin4311	bin4312	1,826,024	2,019,417	7.34	7.60	−6.62	193.39	2021SY
	qTTP_19_1	19	2.90	bin4563	bin4564	219,123	425,982	4.33	4.34	5.03	206.86	2021SY

TP—tocopherol, QTL naming followed nomenclature “q” for quantitative locus, trait name (tocopherol components and total tocopherol), followed by chromosome number, and the number of QTL detected on each chromosome for each trait. R²—phenotypic variance; Add—additive effect.

, Figure 3). LOD values for the QTL ranged from 3.57 to 31.40 and they explained 3.21 to 38.37 % of phenotypic variation. In detail, eight QTL were mapped on seven chromosomes for delta-tocopherol with LOD from 4.12 to 6.06 and R² from 6.89 to 10.37%. Two QTL spanning an interval of 616.38 kb were detected in two environments on Chromosome 19, were in close proximity, and were classified as one stable QTL. Notably, 13 QTL were identified for gamma-tocopherol in 9 chromosomes with LOD from 3.77 to 31.40 and R² from 3.21 to 38.37%, with a stable interval of 835.81 kb on Chromosome 5 detected across all 3 environments. The highest R² value (38.37%) was identified in this interval. There were eight QTL mapped for alpha-tocopherol on five chromosomes, with two different intervals on Chromosomes 11 and 12 being stable across two environments, respectively. For total tocopherol, nine QTL were mapped on seven chromosomes with LOD values from 4.33 to 20.60 and PVE from 4.13 to 25.15%, of which a stable interval of 713.82 kb was identified in chromosome 5.

In this study, out of the 38 QTL identified in this study, we identified stable regions for different tocopherol traits across 2 or more locations. Major QTL with R² values greater than 10% were identified among the stable QTL. Thus, for further studies, stable and major QTL were selected. This included the interval for alpha-tocopherol on Chromosome 12 which spans an interval of 199.75 kb and with PVE from 22.94 to 26.11%. Additionally, we identified an overlapping interval for total tocopherol and gamma-tocopherol on Chromosome 5 within bin1247 and bin1248 which harbored QTL with a high R² value of 38.37%. This indicated that these regions are highly linked to the traits of interest and can be used to mine candidate genes.

3.3. In Silico Identification and Annotation of Candidate Genes

The gene models and annotations of genes in the physical intervals of selected intervals were downloaded from the Soybase database to predict putative genes. In total, there were 92 genes, of which 90 were polymorphic between the parental cultivars (Table S2). In detail, the stable interval on Chromosome 5 consisted of 61 polymorphic genes, and Chromosome 12 contained 29 polymorphic genes. These intervals contained four transcription factor families (Table S3), which included G2-like, ARR-B, HSF, and GRF transcription factors.

3.4. SNP Variation Analysis and Variant Annotation

To narrow down the list of genes in the candidate regions, we conducted an SNP variation analysis to identify those with the strongest functional relevance and strongest correlation with the traits in the study. The candidate region consisted of 22,367 SNPs. Using a natural soybean population evaluated across five environments and a mean of all environments, we identified that the SNPs for the genes in the candidate region were all significant across all multiple environments, which reflected the importance of this candidate region. We performed SNP variant annotation on all the SNPs of the genes in the candidate region. From the annotation, 47 missense SNP variants from 19 genes predicted to have a moderate impact were identified (

Table 3. Significant SNPs of candidate genes and their variant annotations.

Trait	Gene	Chromosome	Position	REF/ALT	AA Conversion	Variant Type	Impact
Alpha-TP	Glyma.12G013400	Chr12	970,529	T/A	Leu/Ile	Missense variant	MODERATE
	Glyma.12G013400	Chr12	970,593	C/A	Pro/His	Missense variant	MODERATE
	Glyma.12G013500	Chr12	977,607	G/C	Gly/Arg	Missense variant	MODERATE
	Glyma.12G013900	Chr12	1,006,325	C/T	Thr/Met	Missense variant	MODERATE
	Glyma.12G013900	Chr12	1,006,457	A/G	Asp/Gly	Missense variant	MODERATE
	Glyma.12G013900	Chr12	1,006,452	C/G	Asp/Glu	Missense variant	MODERATE
	Glyma.12G013900	Chr12	1,006,226	C/G	Ser/Cys	Missense variant	MODERATE
	Glyma.12G013900	Chr12	1,006,949	A/T	Asn/Ile	Missense variant	MODERATE
	Glyma.12G013900	Chr12	1,006,817	C/A	Ala/Glu	Missense variant	MODERATE
	Glyma.12G013900	Chr12	1,006,561	C/T	Leu/Phe	Missense variant	MODERATE
	Glyma.12G013900	Chr12	1,006,958	A/C	Gln/Pro	Missense variant	MODERATE
	Glyma.12G013900	Chr12	1,006,693	T/G	Cys/Gly	Missense variant	MODERATE
	Glyma.12G013900	Chr12	1,006,595	G/T	Ser/Ile	Missense variant	MODERATE
	Glyma.12G013900	Chr12	1,006,943	G/A	Ser/Asn	Missense variant	MODERATE
	Glyma.12G014300	Chr12	1,035,576	C/T	Thr/Ile	Missense variant	MODERATE
	Glyma.12G014300	Chr12	1,035,190	T/A	Ser/Thr	Missense variant	MODERATE
	Glyma.12G014500	Chr12	1,044,076	G/C	Ala/Pro	Missense variant	MODERATE
	Glyma.12G014600	Chr12	1,056,949	C/T	Ser/Leu	Missense variant	MODERATE
	Glyma.12G014600	Chr12	1,049,391	G/A	Arg/Lys	Missense variant and splice region variant	MODERATE
	Glyma.12G014900	Chr12	1,088,444	A/G	Asn/Ser	Missense variant	MODERATE
	Glyma.12G014900	Chr12	1,082,240	A/G	Asn/Asp	Missense variant	MODERATE
	Glyma.12G015300	Chr12	1,103,145	C/T	Pro/Ser	Missense variant	MODERATE
	Glyma.12G015800	Chr12	1,126,756	A/G	Thr/Ala	Missense variant	MODERATE
	Glyma.12G015800	Chr12	1,132,027	T/G	Ser/Arg	Missense variant	MODERATE
	Glyma.12G015800	Chr12	1,129,809	G/A	Ala/Thr	Missense variant	MODERATE
	Glyma.12G015800	Chr12	1,127,746	C/G	Asn/Lys	Missense variant	MODERATE
	Glyma.12G015800	Chr12	1,132,157	A/G	Asn/Ser	Missense variant	MODERATE
	Glyma.12G015800	Chr12	1,127,187	G/C	Ser/Thr	Missense variant	MODERATE
	Glyma.12G015800	Chr12	1,130,492	G/C	Arg/Thr	Missense variant	MODERATE
	Glyma.12G015800	Chr12	1,131,857	G/C	Arg/Thr	Missense variant	MODERATE
Total TP and Gamma-TP	Glyma.05G240500	Chr05	41,564,800	A/G	Ile/Val	Missense variant	MODERATE
	Glyma.05G241600	Chr05	41,657,652	T/G	Val/Gly	Missense variant and splice region variant	MODERATE
	Glyma.05G242800	Chr05	41,767,973	C/G	Gln/Glu	Missense variant	MODERATE
	Glyma.05G242900	Chr05	41,777,763	T/G	Phe/Leu	Missense variant	MODERATE
	Glyma.05G243000	Chr05	41,785,292	G/A	Arg/His	Missense variant	MODERATE
	Glyma.05G243000	Chr05	41,785,204	G/C	Gln/His	Missense variant	MODERATE
	Glyma.05G243000	Chr05	41,785,307	T/C	Leu/Pro	Missense variant	MODERATE
	Glyma.05G243000	Chr05	41,785,044	T/A	Asp/Glu	Missense variant	MODERATE
	Glyma.05G243200	Chr05	41,792,012	T/A	Leu/Met	Missense variant	MODERATE
	Glyma.05G243200	Chr05	41,790,959	G/A	Ala/Thr	Missense variant	MODERATE
	Glyma.05G243200	Chr05	41,791,118	G/A	Val/Ile	Missense variant	MODERATE
	Glyma.05G243300	Chr05	41,799,220	T/C	Val/Ala	Missense variant	MODERATE
	Glyma.05G243400	Chr05	41,807,338	A/C	Gln/Pro	Missense variant	MODERATE
	Glyma.05G243400	Chr05	41,807,339	C/A	Gln/Lys	Missense variant	MODERATE
	Glyma.05G243700	Chr05	41,827,957	A/G	Glu/Gly	Missense variant	MODERATE
	Glyma.05G243100	Chr05	41,783,659	G/C	Gly/Ala	Missense variant	MODERATE
	Glyma.05G243100	Chr05	41,783,805	A/G	Ile/Val	Missense variant	MODERATE

TP—tocopherol.

). Of the 19 genes, 14 were annotated, including, Glyma.05G240500 a heat shock factor transcription factor protein (GmHSFA8), and a key enzyme of the tocopherol biosynthesis pathway, Glyma.12G014300, tocopherol o-methyltransferase or gamma-tocopherol methyltransferase (GmVTE4) (

Table 4. Functional annotation of candidate genes.

Gene	Annotation
Glyma.05G240500	Heat stress transcription factor A-8
Glyma.05G241600	Histidine kinase 4
Glyma.05G242800	ATP-dependent RNA helicase DHX36
Glyma.05G242900	Protein O-linked-mannose beta-1,4-N-acetylglucosaminyltransferase 2
Glyma.05G243000	Cyclin-dependent kinase D-1
Glyma.05G243100	NA
Glyma.05G243200	NA
Glyma.05G243300	NA
Glyma.05G243400	Eukaryotic peptide chain release factor GTP-binding subunit ERF3A
Glyma.05G243700	Peroxisome biogenesis protein 3-2
Glyma.12G013400	Maltose excess protein 1, chloroplastic
Glyma.12G013500	DNA topoisomerase 1
Glyma.12G013900	NA
Glyma.12G014300	Probable tocopherol O-methyltransferase, chloroplastic
Glyma.12G014500	NA
Glyma.12G014600	Origin of replication complex subunit 3
Glyma.12G014900	Bromodomain adjacent to zinc finger domain protein 2B
Glyma.12G015300	Alcohol dehydrogenase-like 2
Glyma.12G015800	FRIGIDA-like protein 5

NA—unannotated.

). Glyma.12G014300, tocopherol O-methyltransferase (GmVTE4), was selected as a candidate gene due to its direct involvement in the tocopherol biosynthesis pathway. Meanwhile, Glyma.05G240500, a heat shock factor transcription factor protein (GmHSFA8), was selected as a candidate gene based on its potential regulatory role in the expression of genes related to tocopherol biosynthesis. GmHSFA8 is 2856 bp long with two exons and an intron (Figure 4A). One missense variant was predicted for GmHSFA8 to be present on the second exon, which resulted in an A to G substitution, changing isoleucine to valine. GmVTE4 is 3904 bp spanning six exons and five introns (Figure 4B). Two missense variants were predicted for VTE4 on the fourth and fifth exons, resulting in the C to T and T to A substitutions which caused changes in the amino acids of the proteins. For further studies, we focused on the missense variants in these two genes as studies have reported their relationships in influencing tocopherol contents in plants.

3.5. Haplotype Variation for Missense Variants for GmHSFA8 and GmVTE4

In this study, we studied the haplotype variations in the missense variants for the GmHSFA8 transcription factor and GmVTE4 for tocopherol content. Since the GmHSFA8 was identified in a significant interval for total and gamma-tocopherol, we analyzed the haplotypes of the natural population for these phenotypes. Similarly, we analyzed the haplotypes of GmVTE4 for alpha-tocopherol content. Natural variations were identified for the missense variants in GmHSFA8 and GmVTE4 for tocopherol content in the natural population (Figure 5). For VTE4, the TC haplotype was the reference genotype, and the AT was the mutant genotype (Figure 5A,B). Overall, TC had 55% frequency in the population, while the AT haplotype was 45% in the natural population (Figure 5B). Alpha-tocopherol content was significantly higher in the AT haplotypes at 12.53 μg/g, while the TC haplotype contained 8.33 μg/g of alpha-tocopherol (Figure 5A). The frequencies of the TC haplotypes in the NR, HR, and SR regions were 23%, 37%, and 82%, respectively, whereas the frequencies of the AT haplotypes were 77%, 63%, and 18%, respectively. Consistent with the overall alpha-tocopherol content, AT haplotypes contained higher amounts of alpha-tocopherol content. In the natural population, the frequency of the reference allele A for GmHSFA8 was 26%, while the variant allele was 74% (Figure 5C,D). Total and gamma-tocopherol contents were significantly higher (p < 0.001) in the A genotypes than in the G genotypes (Figure 5C). In detail, the A genotypes had total and gamma-tocopherol amounts of 234.61 μg/g and 148.97 μg/g, respectively, while the G genotypes had total and gamma-tocopherol contents of 221.28 μg/g and 133.46 μg/g, respectively. The regional distribution also showed the G haplotypes had frequencies of 39%, 80%, and 86% in the NR, HR, and SR regions, respectively. On the other hand, A haplotypes had frequencies of 61%, 20%, and 14% in the NR, HR, and SR regions, respectively. With regard to the total and gamma-tocopherol contents, the G haplotypes contained significantly higher (p < 0.001) amounts than the A haplotypes.

4. Discussion

In this study, the contents of individual tocopherol and total tocopherol of RIL population in the three environments were significantly different. Alpha-tocopherol had the highest mean coefficient of variation, indicating that the natural variation of alpha-tocopherol was more abundant than that of other tocopherol isomers, which was consistent with previous research [3,29]. ANOVA showed that individual tocopherol and total tocopherol contents were significantly affected by genotype and environment. The estimated heritability of all tocopherol contents was higher than 60%, indicating that genetic effects had greater influence than environmental factors on the variation of individual tocopherol and total tocopherol contents in the population, which was supported by an earlier study [30].

The present study aimed to identify QTL associated with tocopherol content in soybean and to identify candidate genes underlying these QTL. Through the use of a biparental population and QTL mapping, we were able to identify 38 QTL associated with tocopherol content in soybean. These QTL were distributed across 14 chromosomes, with some QTL showing stability across multiple environments (Table 1, Figure 1). Of particular interest were the stable and major QTL (i.e., qDelt_19, qGam_05, qAlp_11, qAlp_12, and qTTP_05) identified in this study (Table 2, Figure 3). Especially, two QTL (qAlp_12 for alpha-tocopherol on Chromosome 12 and a locus for both gamma- and total tocopherol (qGam_05 and qTTP_05) on Chromosome 5) were focused on due to their high number of explanations for phenotypic variances. The qAlp_12 spans a genomic region of 199.75 Kb, accounting for 22.94% and 26.11% of the phenotypic variance of alpha-tocopherol content in two environments. Recent studies have also reported stable regions on Chromosome 12, confirming their importance in regulating tocopherol content [6]. Noticeably, the QTL on Chromosome 5 was a novel locus for tocopherol, contributing to both gamma- and total tocopherol, and explained an average of 25.3% for gamma-tocopherol, and 23.6% for total tocopherol. In addition, some other QTL explained more than 10% of phenotypic variation for tocopherol in a single environment (Table 2). Some of these loci, such as qGam_02_2 and qGam_15_1 had also been detected in previous studies [12,13]. The instability of these loci may be due to the sensitivity to specific environments. Considering the importance of the QTL on Chromosomes 12 and 5, these two intervals were further studied to explore candidate genes.

To identify candidate genes underlying these QTL, we conducted an in silico identification and annotation of genes within the physical intervals of the selected QTL. A total of 92 genes were identified, with 90 being polymorphic between the parental cultivars (Table S2). Four transcription factor families were identified in the candidate genes, including G2-like, ARR-B, HSF, and GRF transcription factors (Table S3). To narrow down the list of candidate genes, SNP variation analysis and annotation were conducted to identify those with the strongest functional relevance and correlation with the traits in the study. A total of 47 missense SNP variants from 19 genes were identified, with Glyma.05G240500 (a heat shock factor transcription factor protein) referred to as GmHSFA8 and Glyma.12G014300 (tocopherol o-methyltransferase) also known as GmVTE4 being of particular interest due to their reported relationships in influencing tocopherol contents in plants (Table 2).

We selected GmHSFA8 and GmVTE4 as candidate genes for investigation based on their known functions in plants and their potential roles in the regulation of tocopherol biosynthesis. GmHSFA8 encodes a heat shock transcription factor that has been implicated in the response to various abiotic stresses, such as heat, drought, and salinity, as well as in the regulation of plant growth and development [31]. Studies suggest that heat shock factors may also play a critical role in the regulation of tocopherol biosynthesis. In Arabidopsis, heat shock treatment induced the expression of tocopherol biosynthesis genes, including HPPD, VTE1, VTE2, VTE3, and VTE4 [32]. This upregulation of tocopherol biosynthesis leads to the accumulation of tocopherols, particularly alpha-tocopherol, which protects photosystems and maintains the stability of chloroplasts under high light and heat stress [33]. Moreover, tocopherol accumulation under heat stress promotes the biogenesis of microRNAs, including miR398, which enhances plant heat tolerance by inhibiting the nuclear exoribonucleases (XRN) through PAP production [34]. Therefore, heat shock factors not only regulate the expression of protective proteins but also contribute to the regulation of tocopherol biosynthesis and accumulation, highlighting their important role in plant adaptation to abiotic stresses.

GmVTE4 encodes a protein involved in the conversion of gamma-tocopherol to alpha-tocopherol, the most biologically active form of tocopherol. Previous research has highlighted the significance of VTE4 in the production of tocopherol in plants. In soybean seeds, overexpressing VTE4 resulted in an increase in alpha-tocopherol levels up to 75% of total tocopherol while co-expressing VTE4 with VTE3 led to an even greater increase in alpha-tocopherol levels to more than 95% of total tocopherol [35]. Furthermore, expression levels of gamma-TMT3, an isoform of VTE4 identified on Chromosome 9 by QTL mapping, have been shown to correlate with higher levels of alpha-tocopherol in soybeans [36]. Additionally, the expression of the barley HGGT gene alone or with soybean VTE4 resulted in up to a 10-fold increase in vitamin E content [37]. In our study, we identified a different isoform of VTE4 on Chromosome 12 through QTL mapping. This suggests that the VTE4 genes in soybeans may play a role in tocopherol biosynthesis, but may function differently across cultivars, or have varying expression patterns, substrate specificity, or enzymatic activities. Further research is needed to understand the functions and interactions of different isoforms of VTE4 in soybean and their contribution to tocopherol accumulation and composition.

The identification of moderate impact missense SNP variants in GmHSFA8 and GmVTE4 suggests that these genes may be potential candidates for further investigation and manipulation to improve the tocopherol levels in soybean. Our study revealed significant associations between the SNPs in both GmHSFA8 and GmVTE4 and tocopherol content in soybean seeds. Specifically, the missense variant in GmHSFA8 was significantly associated with decreased total and gamma-tocopherol contents, and the proportion of the missense variant increased from north to south in Chinese soybean accessions. The two missense variants in GmVTE4 were significantly associated with increased alpha-tocopherol content, and the proportion of missense variants decreased from north to south in the natural soybean population. This is consistent with our previous study that alpha- and gamma-tocopherol content is positively correlated with latitude [3]. These findings suggest that both GmHSFA8 and GmVTE4 may play important roles in regulating tocopherol biosynthesis in soybean seeds.

Overall, our study provides new insights into the genetic regulation of tocopherol content in soybean seeds and identifies GmHSFA8 and GmVTE4 as promising targets for future efforts to improve the nutritional quality of soybean through breeding and biotechnology.