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Article

QTL Mapping for Seed Quality Traits under Multiple Environments in Soybean (Glycine max L.)

by
Jiaqi Liu
1,†,
Aohua Jiang
1,†,
Ronghan Ma
1,
Weiran Gao
1,
Pingting Tan
1,
Xi Li
1,
Chengzhang Du
2,
Jijun Zhang
2,
Xiaochun Zhang
2,
Li Zhang
3,
Xiaomei Fang
1,
Zelin Yi
1 and
Jian Zhang
1,*
1
College of Agronomy and Biotechnology, Southwest University, Chongqing 400715, China
2
Institute of Specialty Crop, Chongqing Academy of Agricultural Sciences, Chongqing 402160, China
3
Chongqing Three Gorges Academy of Agricultural Sciences, Chongqing 404100, China
*
Author to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Agronomy 2023, 13(9), 2382; https://doi.org/10.3390/agronomy13092382
Submission received: 25 August 2023 / Revised: 8 September 2023 / Accepted: 12 September 2023 / Published: 13 September 2023
(This article belongs to the Special Issue Soybean Molecular Breeding for Yield, Quality and Resistance Traits)
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Abstract

:
Soybeans are the main source of vegetable protein and edible oil for humans, with an average content of about 40% crude protein and 20% crude fat. Soybean quality traits are mostly quantitative traits controlled by multiple genes. The quantitative trait loci (QTL) for soybean quality traits and mining related candidate genes are of great significance for the molecular breeding of soybean quality traits and understanding the genetic mechanism of protein/fat metabolism. In this study, the F2 population was derived from the high-protein material Changjiang Chun 2 and Jiyu 166. On the basis of a genetic linkage map constructed in our previous study, the QTL of crude protein content, crude oil content and fatty acid fractions were detected using the multiple-QTL model (MQM) mapping method. The results show that a total of 92 QTL were obtained affecting quality traits under three environments, including 14 QTL of crude oil content, 9 QTL of crude protein content, and 20, 20, 11, 10 and 8 QTL for the content of palmitic, stearic, oleic, linoleic and linolenic acids, respectively. Sixteen QTL clusters were identified, among which Loci01.1, Loci06.1 and Loci11.1 were identified as stable QTL clusters with phenotypic contribution rates of 16.5%, 16.4% and 12.1%, respectively, and candidate genes were mined in their regions. A total of 32 candidate genes related to soybean quality were finally screened via GO enrichment and gene annotation. The present study lies the foundations for understanding the genetic mechanism and elite germplasm innovation of seed quality in soybean.

1. Introduction

Soybean (Glycine max L. Merr.) is an annual herbaceous plant of the genus Leguminosae in the family Leguminosae of the order Rosaceae in the class of dicotyledonous plants, which has been cultivated and consumed for about 5000 years [1]. According to data published by FAO (www.fao.org/faostat/en, accessed on 1 July 2023), in 2021, the total global production of soybeans reached 368 million tons, while Chinese soybean production accounted for only 5.3% of global production, and the self-sufficiency rate was low, meaning that China still required a large number of imports. Soybeans contain about 40% protein content and 20% vegetable oil content [2]. Soybeans remain the leading source of vegetable protein and edible oils for human consumption worldwide. Therefore, breeding high-yield and high-quality soybean is an important and urgent task.
Soybeans are rich in protein and are an important source of plant protein in people’s daily diet [3]. As a full-price plant-based high-quality protein, soy protein contains a complete variety of amino acids, and it is easy to digest and absorb [4]. Soybean can supplement the amino acids lacking in grain protein, whose nutritional value is also comparable to animal protein. In addition, soybean protein is slightly inferior to animal protein. It can lower cholesterol, soften blood vessels, and prevent cancer [5]. In the metabolic process of human bones, a portion of calcium will be excreted with urine every day, and we must obtain calcium from food to maintain the balance of calcium in the body. With the increase in calcium intake, the body’s effective absorption rate and amount of calcium will decrease [6]. Therefore, it is very important to minimize calcium loss for bone health. Previous research has shown that the consumption of soybean protein can reduce bone calcium loss compared to animal protein. In this regard, soybean protein has some efficacy in the prevention of osteoporosis [7].
Soybean fat contains more than 90% fatty acids, and the quality of the fatty acids determines the nutritional value of soybeans and their products. Soybean fatty acids consist of five components, namely palmitic acid, stearic acid, oleic acid, linoleic acid, and linolenic acid. Soybean fatty acids are divided into saturated fatty acids and unsaturated fatty acids, with saturated fatty acids including palmitic acid, stearic acid, and unsaturated fatty acids including oleic acid, linoleic acid and linolenic acid [8,9,10]. Soybeans are rich in unsaturated fatty acids, which cannot be synthesized by the human body itself and must be obtained from food. Therefore, the content of unsaturated fatty acids determines the quality and nutritional value of soybeans. The intake of unsaturated fatty acids can reduce the concentration of cholesterol and lipids in human blood, promote the metabolism of saturated fatty acids, soften blood vessels, and reduce the probability of cardiovascular, cerebral vascular and heart diseases. However, there was a significant negative correlation between protein and oil content in soybean seeds [11,12].
With the emergence of molecular marker technology, in order to develop high-protein soybean varieties, the use of molecular marker technology to select high-protein soybean varieties has become an important research direction. Quantitative trait locus (QTL) analysis provides a powerful tool for soybean breeders to search for new sources of variation and investigate the genetic factors underlying quantitatively inherited traits. In 1988, Apuya et al. successfully constructed the first soybean genetic linkage map using F2 as the mapping population, which contained 11 restriction fragment length polymorphism (RFLP) markers, and the experiment demonstrated their distribution across four linkage groups [13]. In 1992, Diers et al. used RFLP markers to construct an F2:3 population with a cross between G. max and G. soja, and then localized nine QTL related to oil and eight QTL related to protein content [14]. Orf et al. utilized Minsoy × Noir1, Minsoy × Archer, Noir1 × Archer’s recombinant inbred line population to detect a total of six QTL of oil content, which were distributed on the A1, C2, L, and C1 linkage groups [15]. Jun et al. localized 11 QTL associated with protein content in a population obtained by crossing high-protein soybean with low-protein varieties, of which nine have been localized by previous authors [16]. In 2015, Han et al. obtained an RIL population using Zhongdou 27 × Jiunong 20, and localized nine QTL for protein content and nine QTL for oil content [17]. In 2020, Yao et al. used ZYD00463 × WDD01514 cross to obtain a recombinant inbred lines (RIL) population containing 181 families as the study material, and detected 24 stable QTL of seed oil content based on high-density genetic map [18]. Bing et al. constructed a high-density single nucleotide polymorphism (SNP) linkage map with Heinong 84 and Kenfeng 17, positioned quantitative trait loci (QTL) for seed oil content in their hybridized RIL populations, and detected five QTL related to seed oil content distributed on five chromosomes [19]. Yao et al. constructed 236 F2-generation plants derived from Jiyu 50 and Jinong 18. The QTL localization results showed that nine microeffect QTLs of protein content and seven microeffect QTLs of fat content were detected [20]. From the soybean database, the results of studies on QTL localization for many traits have been reported. With the latest data from Soybase (http://soybase.ncgr.org, accessed on 1 July 2023), 249 QTL related to seed protein and 325 QTL related to seed oil have been localized. In addition, 45, 37, 49, 63 and 48 QTL related to seed palmitic, stearic, linoleic, inolenic and oleic have been localized, respectively. The present study is aimed at constructing a relatively high-density map and mapping QTL for seed quality traits through a population derived from a cross between ChangJiangChun 2 (CJC2) and JiYu166 (JY166) in three environments. The results are expected to be useful for marker-assisted selection (MAS) and to improve our understanding of the genetic mechanisms underlying seed quality traits in soybean.

2. Materials and Methods

2.1. Plant Materials

Changjiang Chun 2 (CJC2) is a high-protein cultivar approved by the Chongqing Municipality, and Jiyu 166 (JY166) is a widely adaptable high-oil variety introduced from the north. The genetic difference between the two is large, and the genetic relationship is distant. In this study, 186 individual plants of the F2 population produced from the hybridization of CJC2 and JY166 were used as the location population.
The F2 and F2:3 populations were planted in the summer of 2021 and 2022 at the Teaching and Experimental base of Southwest University in Chongqing (21CQ, 22CQ), and the F2:4 family population was planted in the winter of 2022 in Yuanjiang, Yunnan (22YN). The F2 population was sown as a single plant. The F2:3 and F2:4 line populations were sown in single rows, 1 m long, 0.5 m wide and 0.2 m apart. General field management was used. The material was harvested at maturity and used for further testing of soybean quality traits.

2.2. Methods for the Determination of Quality Traits

2.2.1. Determination of Protein and Oil Content

Seeds of uniform size were selected from harvested soybean seeds on the premise of ensuring that the Petri dishes were full, and were used as soybean samples to be tested. The Petri dishes covered with seeds were placed in the FOSS NIR System 5000 (including detector and software) sample tank of the NIR analyzer, and the crude protein and crude oil contents of the two parents and the F2, F2:3 and F2:4 population materials were determined.

2.2.2. Determination of Fatty Acid Content

The five components of soybean fatty acids, including palmitic acid, stearic acid, oleic acid, linoleic acid and linolenic acid, were determined by means of GC-2010 gas chromatography.
The procedure was as follows: we took 0.2 g of seeds, ground them, put them into 5 mL test tubes, added 2 mL of petroleum ether–ether (1:1) solution, shook slightly, and left the mixture it for 40 min. Then, we added 1 mL of potassium hydroxide–methanol (0.4 mol/L) solution and mixed it well, and the methyl esterification time was 30 min. Then, we added distilled water along the wall of the vials, and left the mixture to stand for a while. After layering, 1 mL of the supernatant was aspirated into the autosampling vial. The chromatographic column was DB-WAX (30 mm × 0.246 mm × 0.25 µm), and the stationary phase was polyethylene glycol. The operating conditions of the chromatograph were as follows: the column temperature was 185 °C, the temperature of the vaporization chamber was 250 °C, the temperature of the detection chamber was 250 °C, the flow rate of the carrier gas (nitrogen) was 60 mL/min, the flow rate of the hydrogen was 40 mL/min, the flow rate of the air was 400 mL/min, the retention time of the peaks was 13 min, and the injection volume was 2 µL. The composition of the unknown samples was determined based on the retention time of the standard samples of fatty acid compositions of soybeans. The area normalization method was used to calculate the percentage content of the five fatty acid components. The measurements were repeated 3 times each, and the average value was taken as the content of fatty acid components.

2.3. Phenotypic Statistical Analyses

Microsoft Excel 2019 software was used for basic statistical analysis of parents’ and populations’ phenotypic data in three environments. SPSS 24.0 software was used for correlation and variance analysis, and Origin 2019 was used for visualization mapping.

2.4. QTL Mapping

The genetic map used in this study was constructed by our laboratory in the early stage. The total length of the soybean genetic map was 2799.2 cm, including 455 markers, and the average map distance was 6.15 cm [21]. QTL localization for quality-related traits was analyzed by means of MQM using MapQTL 6.0 software, and phenotypic data were analyzed using 1000 permutation tests with significance p = 0.05 and LOD = 4.0 as the threshold to determine the presence of QTL [22,23]. Additive effects were defined for the CJC2 allele. Thus, positive genetic effects indicate that alleles of CJC 2 increase phenotypic values, and negative genetic effects indicate that alleles of CJC 2 decrease phenotypic values.
The QTL that we found were labeled with the letter q, the trait name, the chromosome number and the sort number. For example, for the QTL denoted as qPRO01.1, q indicates QTL, PRO stands for the trait (Protein content), 01 indicates the chromosome on which the QTL detected, and 01.1 indicates the order of the QTL identified on the chromosome for each trait [24].
The QTL graphic representation of the linkage groups was created using MapChart 2.2 [25].

2.5. QTL Clusters Identification

QTL clusters are densely packed QTL regions on chromosomes that contain multiple QTL associated with different traits [26]. All QTL were sorted with the chromosome as the primary condition and the physical location as the secondary condition. QTL with overlapping physical locations on the same chromosome were grouped into a cluster and identified as a QTL cluster if associated with at least two traits. The QTL clusters that we found were labeled with “Loci”. For example, for the QTL cluster denoted as Loci01.1, Loci indicates a QTL cluster, 01 indicates the chromosome on which the QTL cluster detected, and 01.1 indicates the order of the QTL cluster identified on the chromosome.

2.6. Candidate Gene Prediction

We obtained gene and functional annotations of stable QTL clusters from SoyBase (http://www.soybase.org, accessed on 1 July 2023), enriched the terms of GO (Gene Ontology), and analyzed the families and subfamilies, molecular functions, biological processes and pathways of genes in the identified QTLs. Finally, candidate genes related to seed quality traits were screened.

3. Results

3.1. Trait Phenotype Analysis

The phenotypic data analysis results of the three environments are shown in Table 1. It can be seen that there are differences in seven traits between the two parents, among which the protein content, palmitic acid, oleic acid and linolenic acid contents of CJC2 are higher than those of JY166. At the same time, all seven traits had some degree of separation in the population, and there was superparental separation. The contents of fatty acids in different years are also different, and the coefficient of variation of oleic acid and linoleic acid is higher, which is greatly affected by the environment. The seven traits were roughly continuously distributed in the three environments, which was consistent with the genetic rule of quantitative traits and suitable for QTL mapping (Figure 1).
According to the analysis of variance, protein, oil content and fatty acid composition are complex quantitative traits, which are influenced by both genes and the environment (Table 2). Among them, only protein content is not significantly affected by the environment, while oil content, palmitic acid, stearic acid, oleic acid, linoleic acid and linolenic acid are significantly affected by the environment.
Correlation analysis (Figure 2) shows that there is a certain correlation between protein, oil and fatty acid components. There was a significant negative correlation between protein and oil content. Palmitic acid was negatively correlated with oil and linoleic acid. There was a significant positive correlation between stearic acid and linolenic acid. The relationship between linoleic acid and oleic acid was significantly negative. The correlation coefficient between linoleic acid and oleic acid is the largest, reaching 0.970, indicating that suitable varieties can be selected according to this law in breeding.

3.2. QTL Identified for Seed Quality Traits

A total of 92 QTL related to soybean quality traits were detected in three environments.
For oil content, 14 QTL (Table 3) were identified and mapped on 16 chromosomes, explaining the phenotypic variation from 10.0% to 19.2%. qOIL01.1 and qOIL12.1 were identified in two environments, with the maximum phenotypic variation of 18.40% and 13.40%, respectively. Most of the QTL’s favorable alleles were derived from JY166.
For protein content, nine QTL (Table 3) were identified and mapped on nine chromosomes, explaining between 11.1% and 24.0% of the phenotypic variation. Except for qPRO20.1, the favorable alleles of other QTL were derived from CJC2.
For palmitic acid, 20 QTL (Table 3) were identified and mapped on fifteen chromosomes, explaining between 10.8% and 20.8% of the phenotypic variation. qPA06.1 and qPA19.1 were identified in two environments. The additive effects of qPA01.1, qPA03.1, qPA06.1, qPA07.1, qPA09.1 and qPA11.2 were positive, while the other QTL were negative.
For Stearic acid, 20 QTL (Table 3) were identified and mapped on 18 chromosomes, explaining between 10.7% and 18.5% of the phenotypic variation. qSA14.1 was detected in three environments with a maximum phenotypic contribution of 18.5%. qSA13.2 were identified in two environments. Except for qSA19.1, the favorable alleles of other QTLs were derived from JY166.
For Oleic acid, 11 QTL (Table 3) were identified and mapped on 11 chromosomes, explaining between 10.6% and 18.5% of the phenotypic variation. The favorable alleles of qOA02.1, qOA09.1, qOA11.1, and qOA16.1 were derived from JY166.
For Linoleic acid, 10 QTL (Table 3) were identified and mapped on 10 chromosomes, explaining the phenotypic variation from 9.5% to 20.7% of the phenotypic variation. qLA11.1 were identified in two environments, with the maximum phenotypic variation of 14.2%. The favorable alleles of qLA02.1, qLA03.1, qLA09.1, qLA11.1 and qLA16.1 were derived from CJC2. The favorable alleles of qLA06.1, qLA07.1, qLA08.1, qLA13.1 and qLA18.1 were derived from JY166.
For Linolenic acid, eight QTL (Table 3) were identified and mapped on eight chromosomes, explaining from 9.5% to 17.5% of the phenotypic variation. qLNA02.1 were identified in two environments, with the maximum phenotypic variation of 14.1%. The favorable alleles of qLNA02.1 and qLNA17.1 were derived from JY166.

3.3. Identification and Analysis of QTL Clusters

The QTLs identified were located on a total of 16 QTL clusters, distributed in clusters on 14 chromosomes in this study (Table 4). More QTL clusters contained QTL related to palmitic acid. In terms of the number of controlled traits, one QTL cluster of four traits was Loci16.1, while the seven QTL clusters of three traits were Loci01.1, Loci03.1, Loci06.1, Loci07.1, Loci08.1, Loci09.1, Loci11.1 and Loci18.1. The remaining QTL clusters were all for two traits. Loci3 was the major QTL cluster with the largest explained phenotypic variation in this study, and its physical location was near 39.79–40.61 MB.

3.4. Candidate Gene Prediction within Stable QTL Clusters

Based on the description of QTL clusters above, Loci01.1, Loci06.1 and Loci11.1 stably detected in two environments were selected for further detection of candidate genes. The loci01.1 interval contains 175 genes, the loci06.1 interval contains 397 genes, and the loci11.1 interval contains 395 genes. Gene GO enrichment analysis revealed that most of the genes within these three QTL cluster intervals were involved in cellular component and molecular function. Biological processes are mainly related to cellular protein metabolism. lipid metabolism process; signal transduction; embryonic development; growth; cellular metabolic processes; cell differentiation, etc. (Figure 3 and Figure 4).
Through the above gene GO enrichment analysis, followed by gene function annotation, a total of 32 candidate genes that may be involved in regulating seed size and weight traits were obtained (Table 5). Of these, seven genes are located on chromosome 1, 15 on chromosome 6, and 11 on chromosome 11. The 32 genes, respectively encode the SapB, Motile_Sperm, DHHC, Oxysterol-binding protein, GLTP, Acyl CoA binding protein, Aldehyde dehydrogenase family, Thioredoxin, Peptidase family M20, EamA-like transporter family, DUF3474 and others.

4. Discussion

In this study, multiple QTL for quality traits of F2, F2:3 and F2:4 populations from CJC2 and JY166 were identified by using the complex interval mapping technique. Through comprehensive analysis of QTL results and comparative analysis with previous research results, the reliability of the location results was verified, and the theoretical basis for molecular breeding of soybean quality traits was provided. Phenotypic data analysis in the three environments showed that there were significant differences in seven traits between parents and continuous variation between populations, which was consistent with the genetic rule of quantitative traits, indicating that QTL mapping in this test population was feasible.
The planting environment of the F2:4 population in Yunnan in 2022 is quite different from that in Chongqing. Latitude and precipitation may be the main environmental factors affecting the fatty acid composition of soybean. According to the results, qPA06.1 and qPA19.1 were detected in both environments, and the coefficient of variation was 5.4~6.6%, indicating that palmitic acid was least affected by environmental factors, which was consistent with the results of previous studies, indicating that palmitic acid may be the most stable among the five fatty acids [27]. Saturated fatty acids are the basis for fatty acid dehydrogenase to catalyze most unsaturated fatty acids. In general, the composition of unsaturated fatty acids is more affected by the environment than that of saturated fatty acids. In this study, the coefficient of variation of oleic acid is 7.2~17.3%, that of linoleic acid is 2.0~20.8%, and that of linolenic acid is 9.0~21.2%. Palmitic acid is negatively correlated with oleic acid and linoleic acid, stearic acid is significantly positively correlated with linolenic acid, and the relationship between linoleic acid and oleic acid is significantly negatively correlated. The selection of one component can indirectly achieve the purpose of selecting other components.
A total of 14 QTL related to oil were detected in this study, with phenotypic variation rates ranging from 10.0% to 19.2%, and most of the favorable alleles were from JY166. Among them, qOIL05.1, qOIL08.1 and qOIL15.1 have been reported by previous studies [28,29,30]. A total of nine QTL related to protein were detected, located on nine chromosomes, respectively, among which qPRO02.1 was consistent with Whiting et al. [31] and qPRO03.1 was consistent with Priolli et al. [32]. A total of 20 QTL related to palmitic acid were detected, and most of the favorable alleles were from CJC2. There were 20 QTL associated with stearic acid, of which qSA14.1 was detected in all three environments. There were 11 QTL associated with oleic acid, and the phenotypic variation rate ranged from 10.6% to 18.5%. The additive effect of qOA02.1, qOA09.1, qOA11.1 and qOA16.1 were all negative. There were 10 QTL associated with linoleic acid with phenotypic variation ranging from 9.5% to 20.7%, of which qLA11.1 was detected in two environments. There were eight QTL associated with linolenic acid, which were located on chromosomes 1, 2, 3, 5, 7, 10, 17 and 18, respectively, with phenotypic variation rates ranging from 9.5% to 17.5%. None of the QTL associated with linolenic acid were reported in this study. In summary, 20 QTL were consistent with the results of previous studies, indicating the accuracy of this experiment. In addition, 72 new QTL were located, providing valuable information for improving soybean quality.
We detected overlapping QTL for multiple traits, with 16 QTL clusters located on chromosomes 1, 2, 3, 5, 6, 7, 8, 9, 10, 11, 12, 16, 18, and 20, each associated with two or more traits related to oil content, protein, and fatty acids. QTL clusters may represent gene/QTL linkage or pleiotropic effects of a single QTL within the same genomic region. These QTL clusters can lay a foundation for further exploration of target genes controlling seed quality traits. Based on the obtained QTL clusters, there are two QTL clusters detected in the environment in the Loci01.1, Loci06.1 and Loci11.1 intervals. Therefore, these three QTL clusters are identified as stable QTL clusters, and a total of 967 genes are obtained through gene mining in these intervals. Through the initial gene screening of lipid metabolism, amino acid synthesis and endoplasmic reticulum in GO enrichment, 32 candidate genes related to crude protein and oil metabolism were obtained after the corresponding gene annotation.
Among the discovered candidate genes, Glyma.01g142300 is involved in the regulation of synthetic DHHC enzyme, mediating palmitoylation and acyl protein thioesterase reduction, and then involved in four cellular processes: endocytosis, reproduction and cell growth, fat and sugar homeostasis, and synaptic signal transduction [33]. Glyma.06g131200 is involved in the regulation of acyl-CoA binding protein (ACBP), which plays an important “endoprotective” role in lipid metabolism by maintaining the acyl-CoA library in cells. ACBP is involved in lipid biosynthesis and transport, gene expression and membrane biogenesis. ACBP is also involved in acyl-CoA transport in phloem and biosynthesis of structural and storage lipids [34]. Glyma.01g130100, Glyma.01g130200, Glyma.01g132500 and Glyma.01g132600 regulate antiprotease to regulate protein accumulation during development to support the growth of developing plants [35]. Glyma.11g190600 induced GDSL-like lipase/acylhydrolase to catalyze TAG in the glycolipid metabolic pathway [36], thereby regulating lipid metabolism. Glyma.06g114300 is involved in the synthesis of ALDH, which may be related to the increase in oil content in seeds by scavenging active aldehydes, fatty acid free radicals and other alcohol derivatives [37]. Glyma.06g138400, Glyma.06g138600, Glyma.06g138700, Glyma.06g140900, Glyma.11g158500, Glyma.11g188900 and Glyma.11 g192500 encodes the ribosomal proteins RPS and RPL, which are involved in ribosome synthesis and increased transcription levels of ribosomal protein genes, resulting in efficient protein turnover and protein accumulation [38].
In summary, 32 genes related to oil, crude protein and fatty acid synthesis were identified through QTL interval localization, GO enrichment analysis and gene function screening, laying a certain foundation for the subsequent improvement of soybean seed quality traits and providing valuable resources for the molecular breeding of soybean quality.

Author Contributions

J.Z. (Jian Zhang), as the co-corresponding author, designed, and supervised the study and wrote the manuscript. J.L. and A.J. performed the study, analyzed data, and were involved in the writing of the manuscript. W.G., R.M., P.T. and X.L. were involved in data analyses and helped in writing the manuscript. J.Z. (Jijun Zhang) and X.Z. were involved in sample collection and preparation, and helped in writing the manuscript. C.D. and Z.Y. helped to perform the analysis. L.Z. and X.F. provided constructive discussions. J.L. and A.J. contributed equally to this work. All authors have read and agreed to the published version of the manuscript.

Funding

This study was supported by Chongqing Technology Innovation and Application Development Special Key Project (cstc2021jscx-gksbX0011); Collection, Utilization and Innovation of Germplasm Resources by Research Institutes and Enterprises of Chongqing (cqnyncw-kqlhtxm).

Data Availability Statement

The datasets used and/or analyzed during the current study are available from the corresponding author upon reasonable request.

Conflicts of Interest

The authors declare no conflict of interest.

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Agronomy 13 02382 g001
Figure 1. (A) Distribution of seed protein and oil content in F2, F2:3 and F2:4 locus populations in three environments. (B) Distribution of seed fatty acid fraction content in F2, F2:3 and F2:4 locus populations in three environments.
Figure 1. (A) Distribution of seed protein and oil content in F2, F2:3 and F2:4 locus populations in three environments. (B) Distribution of seed fatty acid fraction content in F2, F2:3 and F2:4 locus populations in three environments.
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Figure 2. Correlation analysis of soybean protein, oil and fatty acid components (* and ** represent significance at the 0.05 and 0.01 probability levels. The data in this table are the average results of three environments).
Figure 2. Correlation analysis of soybean protein, oil and fatty acid components (* and ** represent significance at the 0.05 and 0.01 probability levels. The data in this table are the average results of three environments).
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Agronomy 13 02382 g003aAgronomy 13 02382 g003bAgronomy 13 02382 g003c
Figure 3. QTL for seed quality traits derived from the (CJC2 × JY166) population.
Figure 3. QTL for seed quality traits derived from the (CJC2 × JY166) population.
Agronomy 13 02382 g003aAgronomy 13 02382 g003bAgronomy 13 02382 g003c
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Figure 4. GO term enrichment analysis of the genes located within the three QTL clusters: (A) Loci01.1; (B) Loci06.1; (C) Loci11.1.
Figure 4. GO term enrichment analysis of the genes located within the three QTL clusters: (A) Loci01.1; (B) Loci06.1; (C) Loci11.1.
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Table 1. Characteristics of seed quality trait in the F2 population obtained by crossing CJC 2 with JY166 in three environments.
Table 1. Characteristics of seed quality trait in the F2 population obtained by crossing CJC 2 with JY166 in three environments.
Trait (%)Env.ParentPopulation
CJC2JY166MinMaxMeanSDVarianceCV (%)SkewnessKurtosis
Oil 21CQ18.9522.0116.8523.4320.002.014.0310.00−0.09−1.17
22CQ19.4322.1214.9823.8119.101.853.429.700.16−0.17
22YN18.55 21.5614.8424.7019.192.285.2011.900.06−0.78
Protein 21CQ42.9837.9032.4444.1538.312.787.757.300.290.64
22CQ43.1037.3026.6943.9337.154.3418.8411.70−0.59−0.38
22YN42.08 39.2035.4845.2541.241.923.704.70−0.26−0.08
Palmitic acid21CQ11.6710.238.9814.4811.210.740.556.600.514.05
22CQ11.3210.409.8213.3711.700.630.405.40−0.340.76
22YN11.6210.7810.4013.1711.830.640.415.400.06−0.65
Stearic acid21CQ4.092.982.204.403.390.430.1912.80−0.170.09
22CQ3.272.792.073.662.640.270.0710.100.842.10
22YN4.313.352.885.393.790.480.2312.700.761.44
Oleic acid21CQ32.5630.9823.1655.3240.807.0449.5817.30−0.08−0.47
22CQ33.2631.3822.1046.5834.095.6031.3516.400.30−0.42
22YN21.4919.8717.6424.5121.031.522.317.200.12−0.56
Linoleic acid21CQ49.5652.3043.3360.0150.078.3569.7420.800.04−0.55
22CQ48.9751.9742.1162.0649.726.5042.2213.60−0.38−0.24
22YN53.1655.2352.0857.2154.311.081.172.000.240.16
Linolenic acid21CQ4.713.342.937.524.430.940.8821.200.900.74
22CQ4.683.452.925.553.800.550.3014.401.251.44
22YN9.428.237.4910.639.040.810.669.000.02−0.99
21CQ, 22CQ and 22YN indicate from 2021 to 2022 in Chongqing and the winter of 2022 in Yunnan, respectively.
Table 2. Variance analysis of soybean quality traits.
Table 2. Variance analysis of soybean quality traits.
TraitItemSSdfMSF-Value
OilGen.18.8718518.872.14 **
Env.279.472279.4729.41 **
ProteinGen.6.351856.353.21 **
Env.0.0920.090.03
Palmitic acidGen.0.631850.633.27 **
Env.0.4320.431.27 **
Stearic acidGen.0.241850.240.59
Env.26.78226.78207.94 **
Oleic acidGen.23.3018523.300.46
Env.3521.8423521.84267.75 **
Linoleic acidGen.29.1018529.101.34 **
Env.907.732907.7353.60 **
Linolenic acidGen.0.751850.750.11
Env.557.502557.501398.83 **
** represent significance at the 0.01 probability levels, Gen. and Env. represent genotype and environment.
Table 3. QTL identified for seed quality traits in three environments.
Table 3. QTL identified for seed quality traits in three environments.
QTLEnv. aChr.Nearest MakerInterval (cm)LODPVE (%) bAdditiveDominance
qOIL01.122CQ1SWU0111094.90–98.908.0418.40.641.38
22YN1SWU0111094.90–98.905.5514.4−0.930.65
qOIL02.122YN2GMES0080.59–4.597.0818.00.461.95
qOIL03.122YN3SWU0311580.89–86.594.5512.0−0.920.57
qOIL04.122YN4SWU0412164.13–66.444.4011.6−0.421.65
qOIL05.122YN5satt211152.69–155.325.7114.8−2.01−0.54
qOIL06.122YN6SWU0604625.73–30.087.5019.0−1.080.94
qOIL07.122YN7SWU0704979.47–87.737.5719.2−1.47−0.52
qOIL08.122YN8SWU08015123.74–126.856.6817.1−1.250.06
qOIL09.122CQ9GMES05498.02–109.024.4010.5−0.24−1.28
qOIL11.122YN11SWU1112585.35–101.514.8312.7−1.190.76
qOIL12.122CQ12satt2791.00–2.214.1510.00.69−0.63
22YN12satt2791.00–3.215.1313.41.060.67
qOIL13.122YN13sat2980–11.744.5712.0−0.980.66
qOIL15.122YN15satt72016.00–32.724.4711.8−0.830.80
qOIL16.122CQ16satt21545.39–49.314.3310.40.35−1.13
qPRO01.122CQ1SWU01118117.14–124.187.7720.52.082.07
qPRO02.121CQ2SWU0213434.65–41.646.2425.00.05−4.47
qPRO03.122YN3SWU0310559.59–74.896.3616.40.990.41
qPRO06.122CQ6sat40286.84–100.864.7413.12.06−1.91
qPRO07.122CQ7SWU0704660.77–66.774.6412.81.681.15
qPRO14.122CQ14CSSR02346.81–50.439.2824.03.995.21
qPRO16.122YN16GMES09858.09–59.254.1811.10.850.14
qPRO17.122CQ17satt25613.27–19.396.8218.2−0.15−3.89
qPRO20.122CQ20sat26856.14–71.474.3312.02.120.23
qPA01.122YN1SWU0110358.72–69.888.1220.4−0.300.49
qPA02.122YN2SWU0212423.43–29.528.6021.50.400.41
qPA03.122YN3SWU0311580.89–86.598.3020.8−0.320.25
qPA06.122CQ6SWU0604736.08–37.365.0211.7−0.08−0.45
22YN6SWU0604625.73–37.367.7419.5−0.290.31
qPA07.122YN7SWU0704877.60–82.737.8419.8−0.350.14
qPA08.122CQ8SWU0805557.06–63.596.7415.40.470.36
qPA08.222YN8sat406132.85–139.095.0313.20.010.54
qPA09.122YN9SWU0905623.98–29.015.0113.1−0.10−0.52
qPA10.122YN10SWU100200–13.824.5412.00.250.16
qPA10.222CQ10SWU1005317.82–24.995.1812.0−0.370.24
qPA11.122YN11GMES06913–21.475.8815.20.320.10
qPA11.222CQ11SSR06228.47–42.374.6911.0−0.130.37
qPA12.122YN12SWU1202813.81–31.917.8919.90.360.07
qPA12.222YN12sat20653.79–65.347.0317.90.410.17
qPA14.122YN14SWU140063.31–6.385.2213.60.150.42
qPA16.122YN16CSSR01048.31–53.354.9012.80.280.07
qPA18.122CQ18GMES114211.53–217.377.5517.00.410.37
qPA19.121CQ19sat34012.45–21.785.3212.70.290.29
22CQ19sat34012.45–21.784.6310.80.340.42
qPA20.122YN20satt23914.38–27.265.3113.80.33−0.08
qPA20.222CQ20CSSR04130.26–44.705.7013.20.340.23
qSA01.122CQ1satt198133.48–139.376.6415.2−0.140.05
qSA02.122CQ2SWU0203827.01–32.194.9811.6−0.070.19
qSA03.122CQ3SWU0311575.89–87.597.7517.5−0.150.03
qSA04.122CQ4GMES0176.89–9.815.8813.5−0.110.16
qSA05.122CQ5sat171101.42–108.775.9213.6−0.080.13
qSA05.221CQ5SWU05120149.69–159.907.9518.4−0.28−0.21
qSA06.122CQ6SWU0604937.36–47.516.3714.6−0.150.03
qSA07.122CQ7SWU0703842.64–49.107.0316.0−0.120.02
qSA08.122CQ8CSSR05430.72–40.156.4914.8−0.130.00
qSA10.121CQ10sat1900–4.004.4210.7−0.13−0.34
qSA11.121CQ11SWU1112891.66–100.836.1014.5−0.220.30
qSA12.122YN12satt29339.07–54.794.5011.9−0.29−0.34
qSA13.122YN13satt14537.00–43.565.6714.7−0.18−0.27
qSA13.221CQ13sat417106.37–121.525.7813.7−0.17−0.32
22CQ13GMES088106.37–121.525.0211.7−0.070.13
qSA14.121CQ14GMES09033.87–39.816.6515.7−0.26−0.16
22CQ14GMES09033.87–39.818.2418.5−0.150.06
22YN14SSR05831.29–39.194.0910.8−0.21−0.11
qSA16.121CQ16satt2850–8.317.5017.5−0.11−0.40
qSA17.121CQ17satt54341.79–52.734.6111.1−0.190.28
qSA18.122YN18sat094146.61–147.764.4211.7−0.21−0.13
qSA19.122YN19GMES11852.76–56.895.0013.10.15−0.24
qSA20.122YN20sat10520.98–35.264.8712.8−0.28−0.32
qOA01.122YN1SWU01114114.86–116.144.3311.50.381.04
qOA02.122CQ2SWU0213645.64–51.638.2418.5−2.920.07
qOA06.122YN6CSSR071186.36–189.725.7715.00.780.05
qOA07.122YN7satt175106.52–110.874.2011.10.68−0.48
qOA08.122YN8CSSR05431.72–39.155.3213.90.73−0.11
qOA09.121CQ9satt41728.01–44.114.5010.6−0.683.57
qOA11.122CQ11SSR06226.47–36.605.7413.2−1.193.62
qOA13.122YN13satt33580.03–92.836.1715.90.930.30
qOA14.122YN14SSR01916.10–19.704.0710.80.55−0.35
qOA16.122CQ16sat16531.31–46.394.8811.4−1.963.18
qOA18.122CQ18GMES114213.53–217.376.5114.93.110.03
qLA02.122CQ2SWU0213645.64–52.407.4916.93.22−0.30
qLA03.122CQ3SWU0310563.59–65.894.039.52.35−1.83
qLA06.122YN6satt202177.43–189.725.5914.5−0.550.07
qLA07.122YN7CSSR0186.90–8.745.9915.5−0.320.69
qLA08.122YN8CSSR05433.72–42.154.4211.7−0.47−0.09
qLA09.122CQ9satt41735.32–44.114.7711.10.89−4.22
qLA11.121CQ11satt58335.60–43.374.079.94.43−2.05
22CQ11SSR06226.47–37.606.2014.21.43−4.37
qLA13.122YN13sat12046.45–57.228.2620.7−0.74−0.20
qLA16.122CQ16sat16531.31–45.394.9211.52.09−3.89
qLA18.122CQ18GMES114213.53–217.376.5114.9−3.70−0.38
qLNA01.122YN1SWU01114113.86–116.146.8517.50.08−0.70
qLNA02.122CQ2SWU0211514.44–14.514.039.5−0.18−0.21
22YN2SWU0211513.11–15.515.4214.1−0.15−0.58
qLNA03.122CQ3satt549100.08–100.904.3810.30.130.46
qLNA05.122YN5CSSR01143.73–48.945.2713.80.13−0.84
qLNA07.122YN7SWU0704981.47–86.735.2213.60.370.35
qLNA10.122CQ10GMES06311.00–11.604.209.90.010.37
qLNA17.122CQ17satt6722.00–6.855.3312.4−0.230.11
qLNA18.122CQ18satt501142.54–143.405.3812.50.010.46
a 21CQ, 22CQ and 22YN indicate from 2021 to 2022 in Chongqing and the winter of 2022 in Yunnan, respectively. b PVE: phenotypic variance explained; PA: palmitic acid; SA: stearic acid; OA: oleic acid; LA; linoleic acid; LNA: linolenic acid.
Table 4. QTL clusters associated with quality traits in soybean.
Table 4. QTL clusters associated with quality traits in soybean.
ClustersChr.IntervalPosition (bp)QTLsPVE (%)
Loci01.11SWU01109-SWU0112344466210–48412121qPro01.1
qOA01.1
qLNA01.1		17.5–20.5
Loci02.12SWU02139-satt70341717826–42580900qOA02.1
qLA02.1		16.9–18.5
Loci03.13SWU03105-satt5492786413–37342918qOIL03.1
qPA03.1
qSA03.1		12.0–20.8
Loci05.15SWU05127-SWU0512040080446–41448032qOIL05.1
qSA05.2		14.8–18.4
Loci06.16SWU06046-sat2138505693–11903768qOIL06.1
qPA06.1
qSA06.1		14.6–19.5
Loci06.26satt708-GMES03240461921–47934390qOA06.1
qLA06.1		14.5–15.0
Loci07.17SWU07047-SWU070498992988–9382826qOIL07.1
qPA07.1
qLNA07.1		13.6–19.2
Loci08.18satt233-satt42410633908–17232172qSA08.1
qOA08.1
qLA08.1		11.7–14.8
Loci09.19satt544-sat28111261508–16046181qPA09.1
qOA09.1
qLA09.1		10.6–13.1
Loci10.110SWU10024-SWU100533532259–7394838qPA10.1
qSA10.1		10.7–12.0
Loci11.111GMES069-sat34812781503–26865200qPA11.2
qOA11.1
qLA11.1		11.0–14.2
Loci11.211SWU11123-SSR05729910416–31543053qOIL11.1
qSA11.1		12.7–14.5
Loci12.112satt302-sat21835082852–37556872qPA12.2
qSA12.1		11.9–17.9
Loci16.116sat165-sat36627158682–30404834qOIL16.1
qPA16.1
qOA16.1
qLA16.1		11.1–17.5
Loci18.118GMES107-GMES11451907621–54872565qPA18.1
qOA18.1
qLA18.1		14.9–17.0
Loci20.120SWU20050-satt27023513366–35362794qPA20.1
qSA20.1		12.8–13.8
Table 5. Candidate genes identified in the three QTL clusters.
Table 5. Candidate genes identified in the three QTL clusters.
GO IDGeneGene Functional Annotation
GO:0005576Glyma.01g130100Tryp_alpha_amyl
GO:0005576Glyma.01g130200Tryp_alpha_amyl
GO:0005576Glyma.01g132500Tryp_alpha_amyl
GO:0005576Glyma.01g132600Tryp_alpha_amyl
GO:0005829Glyma.01g131400SapB_1; SapB_2
GO:0005886Glyma.01g136000Motile_Sperm
GO:0005794Glyma.01g142300DHHC (Aspartate-histidine-histidine-cysteine)
GO:0016020Glyma.06g109500Oxysterol-binding protein; PH domain
GO:0016020Glyma.06g124000Glycolipid transfer protein (GLTP)
GO:0005886Glyma.06g131200Ankyrin repeat; Acyl CoA binding protein
GO:0016020Glyma.06g111400emp24/gp25L/p24 family/GOLD
GO:0016020Glyma.06g114300Aldehyde dehydrogenase family
GO:0016020Glyma.06g114700Thioredoxin
GO:0005794Glyma.06g114800Thioredoxin
GO:0005576Glyma.06g115100Peptidase dimerisation domain; Peptidase family M20/M25/M40
GO:0005783Glyma.06g116700Keratinocyte-associated protein 2
GO:0005634Glyma.06g118500Chalcone and stilbene synthases, C-terminal domain
GO:0005634Glyma.06g118600Chalcone and stilbene synthases, N-terminal domain
GO:0016020Glyma.06g122200Sugar efflux transporter for intercellular exchange
GO:0016020Glyma.06g123200EamA-like transporter family
GO:0016020Glyma.06g123300EamA-like transporter family
GO:0016020Glyma.06g133300Cytochrome b5-like Heme/Steroid binding domain
GO:0005840Glyma.06g138400Ribosomal protein L6
GO:0005840Glyma.06g138600Ribosomal protein L6
GO:0005840Glyma.06g138700Ribosomal protein L6
GO:0005840Glyma.06g140900Ribosomal protein L16p/L10e
GO:0005840Glyma.06g144400Ribosomal protein S26e
GO:0005840Glyma.11g158500Ribosomal protein S19e
GO:0005783Glyma.11g160100Sybindin-like family
GO:0005886Glyma.11g174100Domain of unknown function (DUF3474); Fatty acid desaturase
GO:0006629Glyma.11g190400Lecithin:cholesterol acyltransferase
GO:0005576Glyma.11g190600GDSL-like Lipase/Acylhydrolase
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MDPI and ACS Style

Liu, J.; Jiang, A.; Ma, R.; Gao, W.; Tan, P.; Li, X.; Du, C.; Zhang, J.; Zhang, X.; Zhang, L.; et al. QTL Mapping for Seed Quality Traits under Multiple Environments in Soybean (Glycine max L.). Agronomy 2023, 13, 2382. https://doi.org/10.3390/agronomy13092382

AMA Style

Liu J, Jiang A, Ma R, Gao W, Tan P, Li X, Du C, Zhang J, Zhang X, Zhang L, et al. QTL Mapping for Seed Quality Traits under Multiple Environments in Soybean (Glycine max L.). Agronomy. 2023; 13(9):2382. https://doi.org/10.3390/agronomy13092382

Chicago/Turabian Style

Liu, Jiaqi, Aohua Jiang, Ronghan Ma, Weiran Gao, Pingting Tan, Xi Li, Chengzhang Du, Jijun Zhang, Xiaochun Zhang, Li Zhang, and et al. 2023. "QTL Mapping for Seed Quality Traits under Multiple Environments in Soybean (Glycine max L.)" Agronomy 13, no. 9: 2382. https://doi.org/10.3390/agronomy13092382

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