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Communication

Development of a Set of Polymorphic DNA Markers for Soybean (Glycine max L.) Applications

by
Man-Wah Li
1,2,3,†,
Xin Wang
1,2,4,†,
Ching-Ching Sze
1,2,
Wai-Shing Yung
1,2,3,
Fuk-Ling Wong
1,2,
Guohong Zhang
5,
Gyuhwa Chung
6,
Ting-Fung Chan
1,2,3 and
Hon-Ming Lam
1,2,3,*
1
Centre for Soybean Research of the State Key Laboratory of Agrobiotechnology, The Chinese University of Hong Kong, Hong Kong, China
2
School of Life Sciences, The Chinese University of Hong Kong, Hong Kong, China
3
Shenzhen Research Institute, The Chinese University of Hong Kong, Shenzhen 518000, China
4
Guangdong Provincial Key Laboratory of Plant Adaptation and Molecular Design, Innovative Center of Molecular Genetics and Evolution, School of Life Sciences, Guangzhou University, Guangzhou 510006, China
5
Institute of Dryland Agriculture, Gansu Academy of Agricultural Sciences, Lanzhou 730030, China
6
Department of Biotechnology, Chonnam National University, Yeosu 59626, Republic of Korea
*
Author to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Agronomy 2023, 13(11), 2708; https://doi.org/10.3390/agronomy13112708
Submission received: 9 October 2023 / Revised: 21 October 2023 / Accepted: 25 October 2023 / Published: 27 October 2023
(This article belongs to the Special Issue Functional Genomics and Molecular Breeding of Soybeans)
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Abstract

:
Soybean (Glycine max L.) is gaining in importance due to its many uses, including as a food crop and a source of industrial products, among others. Increasing efforts are made to accelerate soybean research and develop new soybean varieties to meet global demands. Soybean research, breeding, identification, and variety protection all rely on precise genomic information. While DNA markers are invaluable tools for these purposes, the older generations, especially those developed before the advent of genome sequencing, lack precision and specificity. Thankfully, advancements in genome sequencing technologies have generated vast amounts of sequence data over the past decade, allowing precise and high-resolution analyses. However, making sense of the genomic information requires a certain level of professional training and computational power, which are not universally available to researchers. To address this, we generated a set of PCR-based DNA markers out of the existing genomic data from 228 popular soybean varieties that offer precise, unambiguous genomic information and can be easily adapted in various applications. A standard operating procedure (SOP) was also designed for these markers and validated on diverse soybean varieties to ensure their reproducibility. This user-friendly universal panel of DNA markers, along with the SOP, will facilitate soybean research and breeding programs through simple applications.

1. Introduction

Soybean (Glycine max L.) is a widely planted crop globally and serves as the most important plant-based source of oil and protein. Improving soybean production is crucial for ensuring food security. Recently, substantial resources have been invested in soybean research, generating large volumes of genomic data. For example, large-scale population analyses have been conducted to delineate the diversity of soybean collections [1,2,3,4,5,6] and identify key genetic components related to important agronomic traits. However, these genomic approaches are both capital- and technology-intensive and require specialized skills and equipment in data analysis, making them impractical for most field-based geneticists and breeders. Therefore, the development of a user-friendly toolkit, drawn from the vast volume of existing genomic data, would have great practical applications.
While the density of genomic variations captured by sequencing is high, the information contained by neighboring variations is redundant, and actually informative variations are limited. One potential enhancement is to collapse the current genomic data into specific markers with non-redundant information. Polymerase chain reaction (PCR)-based markers are well established and well recognized for this purpose.
A few traditional PCR-based markers have been widely used in soybean genetic research. One of them is the amplified fragment length polymorphism (AFLP), which detects DNA polymorphisms through the amplification of restriction enzyme digested DNA, creating polymorphic PCR band pattern between samples [7]. Such a technique, theoretically, requires no prior knowledge of the DNA sequence. Thus, it was a versatile marker type before the popularization of genome sequencing. It has been used in various early soybean genetic studies, such as studying the diversity of soybean [8] and the mapping of the maturity locus E4 [9].
The simple sequence repeat (SSR) marker was one of the most adopted DNA markers for early soybean genetic research. Most of these markers were developed in the early 1990s [10,11,12,13]. For example, seven SSR markers were developed by the Agricultural Research Service of the U.S. Department of Agriculture for genotype identification [13]. Each of these markers can detect 11–26 alleles within the testing population of 96 soybean genotypes [13]. After that, the same group led the development of over a thousand soybean SSR markers, which are now available in the Soybase [11,14]. In the genomic era, a survey of the soybean genome identified over 200,000 potential SSR loci in the soybean genome [15]. Among them, 33,000 markers were developed. Although SSR markers are widely used and are technically simple to detect, there are a few drawbacks. SSR markers are highly polymorphic, and thus each of them is information intensive. However, amplified bands of SSR markers with differences in a few repeating units cannot be easily distinguished from each other by simple agarose gel electrophoresis. The actual size of the amplified band can also be obscured as a result. Furthermore, most well-adapted SSR markers of soybean were designed in the pre-genomic era. Thus, the precise physical locations of these markers in the soybean genome were largely unknown. Mapping these SSR markers to the soybean reference genomes may result in multiple hits in different locations of the genome or mismatches in the priming sites. The former could complicate the interpretation of the results, while the latter could result in inefficient amplification. Through genetic mapping, SSRs associated with phenotypic traits have been identified. Nevertheless, these trait-associated SSRs are often germplasm-specific in terms of the amplicon size, and they are usually weakly associated with the traits as they are located at a distance away from the causal gene of the phenotype.
Supported by genome sequencing, small insertion/deletion (INDEL) of 5–50 bp has become a rising type of DNA marker [16]. Song et al. identified more than 49,000 INDEL between Heduo 12 and Williams 82 through deep resequencing [16]. From these INDEL, a total of 165 PCR-based INDEL markers were developed [16]. Of these, 92% were able to show biallelic bands in 12 other soybean cultivars. Additional alleles were detected using the remaining markers [16].
In some situations, a basket of different types of markers can be analyzed together to increase the marker density. For example, a linkage map of an F2 population was built from restriction fragment length polymorphism (RFLP), SSR, AFLP, AFLP-derived sequence-tag site (STS), bacterial artificial chromosome (BAC)-end or expression sequence tag (EST)-derived STS, random amplification of polymorphic DNA (RAPD), and morphological markers [17]. It reduces the genetic distance between markers by half compared to a previous study [17]. However, such a strategy also required a battery of different genotyping techniques.
To facilitate breeding, effort has been made to develop markers linked to a genomic region conferring specific agronomic traits. Examples of these markers concern mainly traits including disease resistance [18,19], abiotic stress tolerance [20,21], maturity [22,23,24,25,26,27,28,29], plant architecture [30,31], and nutritional values [32,33]. For instance, loci governing maturity are important for soybean acreage expansion along the latitude. Thus, maturity-related loci are the major targets for markers development. Markers have been developed for the E1 locus [29], E2 locus [27], E3 locus [27], E4 locus [23,27], J locus [25], etc., contributing largely to local breeding program. Nevertheless, some of these markers are cultivar-specific, limiting their general usage, while some of the others are only associated with the causal loci but do not hit directly on the causal polymorphisms of the desired phenotype.
In this study, to facilitate soybean research and breeding programs through simple and standardized applications with a user-friendly DNA marker panel, we developed a set of 100 dimorphic DNA markers based on single-nucleotide polymorphism (SNP), copy-number variation (CNV), and present–absent variation (PAV) against the soybean reference genome with precise coordinates. Among them, 88 were randomly distributed in the soybean genome, and they are mainly applicable to germplasm identification, germplasm protection, quick low-resolution mapping, etc. In addition, based on the literature, 12 trait-associated markers linked to the respective causal genes were designed. The PCR conditions of these markers have been tested and standardized with well-defined amplicon sizes. Thus, genotyping of a soybean germplasm can be performed on a 96-well PCR plate in a single run. Each marker has also been tested with a panel of 32 soybean accessions collected worldwide [3,34] to ensure the universal applicability and diversity of the markers.

2. Materials and Methods

2.1. Germplasm Resources

Thirty-two soybean (Glycine max L. and Glycine soja) accessions originating from, or popularized in, different world regions were used for validating the developed markers [3,34,35] (Table 1). An additional 16 accessions were used for the validation of hilum color-associated markers (Table 1).

2.2. Sequencing Data, Mapping, and SNP Calling

Raw reads of 228 re-sequenced soybean cultivars from a previous study [5] were retrieved from the Short Read Archive (SRA) with accession number SRP062560. The raw reads were trimmed using Trim_Galore http://www.bioinformatics.babraham.ac.uk/projects/trim_galore/ (accessed on 31 January 2021) for filtering adaptors and low-quality reads before being mapped to the wild soybean W05 reference genome [35] using the Burrow-Wheeler Aligner (BWA, version 0.7.15-r1140) [37] with default parameters. After alignment, the properly paired and uniquely mapped reads were abstracted using SAMtools (version 1.2) [38]. SNP calling was conducted using the samtools mpileup pipeline.

2.3. Marker Design

The 1000 bp sequence at and around the SNPs (with 500 bp upstream and 499 bp downstream) were retrieved using the blastdbcmd command for blastn (-task megablast) search against the wild soybean W05 reference genome. Only SNPs with flanking sequences uniquely identified in the search using the default settings were used for the SNP marker design to ensure specific amplification. SNP markers were designed based on the tetra-primer ARMS-PCR technique [39] provided by the Primer1 web service [40] with the following settings: range of relative size difference of two inner products = 1.1–1.7; range of inner product size = 100–400. SNPs that failed to generate markers using these settings were discarded. Phenotype-associated markers were designed against the polymorphism dictated by the respective publications. All primer information can be found in Table S1.

2.4. PCR Verification of the DNA Markers

The genomic DNA of soybean germplasms was extracted from primary leaves using the DNeasy Plant Mini Kit (Cat No. 69104, Qiagen, Germantown, MD, USA) according to the manufacturer’s protocol. DNA concentration was measured using a Nanodrop (Thermo Scientific, Waltham, MA, USA). DNA was diluted to 5 ng/µL with MilliQ water before use. All PCR reactions were conducted according to a standard operating procedure (SOP) (File S1). In brief, each 25 µL PCR reaction contained 5 ng of genomic DNA, 1× Go Taq Flex 2 green buffer (Cat No. M7801, Promega, Madison, WI, USA), 0.33 µM of each primer, 0.2 µM of dNTP, 3 mM MgCl2, and 0.5 U of Go Taq Flex 2 DNA polymerase (Cat No. M7801, Promega, Madison, WI, USA). Because the specificity of the ARMS tetra primer reaction is sensitive to temperature, reactions should be set up on ice to prevent temperature fluctuation and undesired amplification. Reactions were run on the BioRad C1000 Thermal Cycler (BioRad, Hercules, CA, USA) with the following protocol: 95 °C for 2 min for initial denaturation, 35 cycles each of 95 °C for 30 s for denaturation, 60 °C for 30 s for annealing, and 72 °C for 1 min for extension, followed by a 10 min final extension at 72 °C. The PCR products were either immediately analyzed by gel electrophoresis or stored at −20 °C until further operations. The PCR products were resolved on 2% agarose gel with 1× Tris-Acetate-EDTA (TAE) buffer. DNA was visualized with RedSafe nucleic acid staining solution (Cat No. 21141, iNtRON Biotechnology, Seongnam, Kyonggi-do, Republic of Korea) in a BioRad Gel Doc EZ System (BioRad, Hercules, CA, USA). All primer sets have been tested with at least 32 different germplasms. Only primer sets that gave a dimorphic band pattern in the tested samples under the standardized conditions were chosen. All primer information can be found in Table S1.

3. Results

3.1. Selecting and Validating SNP Markers with Widely Cultivated Chinese Soybean Cultivars

The raw re-sequencing data of 228 popular soybean accessions were retrieved from a previous study [5]. For the sequence variation calling, to avoid bias, instead of using reference genomes of cultivated soybean of specific geographical lineage, we adopted the high-quality wild soybean W05 genome as the reference genome. After re-mapping the sequencing reads to the W05 genome, we obtained 3,923,154 SNPs distributed across the 20 soybean chromosomes. After filtering out the SNPs located in the duplicated regions of the genome using blastn, random SNPs were selected for PCR-based marker design using the tetra-primer ARMS-PCR technique [39]. The designed markers were tested against 32 diverse soybean accessions to ensure the desired amplicons of the reference and alternative alleles, and the common outer PCR products could be consistently obtained without obvious non-specific amplification (Figure 1, Table S2). During marker testing, we also compiled a standard operating procedure (SOP) with a standardized PCR protocol and gel electrophoresis procedure applicable to all markers tested to simplify the overall operation and ensure the reproducibility of these PCR reactions (File S1). In the end, 88 SNP markers randomly distributed across the 20 chromosomes were selected (Figure 2, Table S1). The frequencies of the reference alleles in the 228 accessions ranged from 0.06 to 0.94 with a median of 0.61 (Figure 2), indicating the diversity of the markers. The corresponding coordinate of the SNPs in the latest version of the Williams 82 (Wm82a5) genome assembly [41] can also be found in Table S1.

3.2. Selecting Trait-Associated Markers and Primer Design

In addition to the random SNP markers, twelve markers associated with nine important agronomic traits were also selected, and their associated primers were designed based on the known polymorphisms (Figure 1 and Figure 2, Table 2 and Table S1). Furthermore, presence–absence variations (PAVs) and amplification-length polymorphisms were also adopted in the designs, depending on the causal gene variations with these traits. The PCR conditions for amplifying these markers were also optimized and harmonized with those for amplifying the random SNP markers.

3.3. Evaluation of the Selected Trait-Associated Markers

Among the twelve trait-associated markers, two markers associated with qualitative traits were selected for validation. Apart from the seed coat color, hilum color also plays an important role in determining the whiteness of soy products. The unpigmented (yellow) hilum in soybean was determined to have resulted from RNA interference, which is generated by the fusion of the DNAJ and inverted CHS3 (iCHS3) genes [2,50]. A pair of primers specific for the DNAJ-iCHS3 chimera were designed (Marker ID: GmIRCHS). An additional primer pair specific to the unfused copy of CHS3 (Glysoja.08G020222) was also designed as the positive control for the reaction (Table S1). Apart from the 32 germplasms used above in validating the random SNP markers (Table S2), 16 additional germplasms were genotyped. Fifteen out of the sixteen germplasms displayed the amplicon for DNAJ-iCHS3, which was consistent with their hilum color (Figure 3A). As expected, LH1 with a lightly pigmented hilum did not possess the DNAJ-iCHS3 allele (Figure 3A), implying that this marker can clearly distinguish varieties with pigmented hilum from those with unpigmented hilum.
The salt tolerance of soybean is mainly determined by the integrity of the GmCHX1/GmSALT3 gene [21,43]. While the salt-tolerant allele is mostly conserved, the DNA sequences of salt-sensitive alleles are diversified [21,43]. An SNP (Chr03:40453338) linked to GmCHX1/GmSALT3 was identified to be associated with salt tolerance based on previous research [21]. Again, a marker was developed to target this SNP. Using a set of germplasms with well-defined salt tolerance [21], we validated that the designed marker can differentiate between the salt-tolerant cultivars and the salt-sensitive ones (Figure 3B).

4. Discussion

According to the National Human Genome Research Institute (NHGRI) Genome Sequencing Program, the sequencing cost per megabase has been reduced from USD 0.06 to USD 0.006 in the past decade [54]. Although sequencing itself is now much more affordable for most researchers, bioinformatic analyses can still be a technological barrier to the effective mining of information from the sequencing data. In theory, genome sequencing allows the identification of most, if not all, sequence variations in a genome, providing information for genetic studies, breeding, and variety protection. Nevertheless, the often-redundant information provided by genome sequencing and the technological barrier mentioned above have hindered the use of genome sequencing by small research groups or breeders in the field.
The development of soybean DNA markers is, in general, challenging due to the highly duplicated genome. Soybean has undergone two recent rounds of whole genome duplication [55]. Apart from that, the soybean genome also contains up to 60% of repeated sequence. The designation of PCR primers specific to certain region of the soybean genome may end up obtaining PCR products from multiple duplicated regions, which could lead to misinterpretation of the genotype. This should always be considered during the development of DNA markers for soybean.
Here, we developed a panel of easy-to-adopt and scalable PCR-based dimorphic DNA markers applicable to most, but perhaps not all, soybean varieties based on the enormous amount of published soybean genome sequencing data. This panel is designed to provide randomly distributed markers throughout the soybean genome for variety identification and protection, small-scale marker-assisted breeding, and quick low-resolution mapping for genetic studies, and it has the following advantages over the existing markers: (1) these new markers were developed from high-quality reference genomes with precise genomic locations and sequence reliability; (2) the use of these markers does not require any special knowledge or expensive equipment beyond end-point PCR and agarose gel electrophoresis, and therefore can be adopted by researchers and breeders in different fields of work with minimal training; (3) dimorphic sequence variations, such as SNP and insertion/deletion (INDEL), were used for marker development, instead of polymorphisms, such as SSR and restriction fragment length polymorphism (RFLP); therefore, they are easy to interpret with no ambiguity, and with enough resolution using simple agarose gel electrophoresis; (4) a standard operating procedure, including a PCR protocol, has been developed for the entire marker panel, without the need for optimization with individual primer pairs, and thus it can be readily executed with minimal training; and (5) the marker panel is scalable. Any other dimorphic markers with well-defined genomic locations developed and validated by the SOP included here can be added to the panel.
In addition to the random SNP markers, a set of trait-associated markers linked to important agronomic traits of soybean were also developed. Most of these trait-associated markers are unlinked and distributed in 10 of the 20 chromosomes (Figure 2). The causal gene sequence variations for these traits have been functionally well-characterized. Thus, the detected allele should precisely infer the phenotype of the plant, as we have demonstrated using hilum color and salt tolerance as examples. These markers can allow breeders to introduce these traits into target varieties or stack new traits onto existing ones without losing the distinctive traits of the recipient lines. The prevalent utilization of SNP markers in GWAS due to their high density and genome coverage also favors the development of the SOP based on SNP markers. GWAS has been widely applied to identify polymorphisms associated with important traits in soybean, and the identified SNPs, such as those significantly associated with seed weight [56,57], flowering time [58], seed yield under different environments [59], and seed coat color [60], can also be tested to expand the marker panel.
Soybean, as the most widely grown legume in the world, plays important roles in the economy, the environment, and human nutrition. Breeding to improve soybean agronomic traits is not only a constant mission for the major soybean-producing countries but also important for emerging soybean-growing regions. Our soybean DNA marker panel serves as an easily accessible tool for users of all skill levels in the field of soybean research.

5. Conclusions

In this study, we developed a set of 100 dimorphic DNA markers using publicly available re-sequencing data of 228 popular soybean accessions and optimized the standard operating procedure for using this marker panel for efficient genotyping of soybean. The trait-associated markers would also greatly benefit the breeding of soybean with desired agronomic traits to meet global demand.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/agronomy13112708/s1, File S1: Standard operating procedure; Table S1: Information on the DNA markers tested; Table S2: Summary of genotyping results of 32 soybean germplasms.

Author Contributions

Conceptualization, H.-M.L., T.-F.C., G.C., G.Z., M.-W.L. and X.W.; investigation, M.-W.L., X.W., C.-C.S. and F.-L.W.; resources, H.-M.L.; writing—original draft preparation, H.-M.L., T.-F.C., M.-W.L., W.-S.Y. and X.W.; writing—review and editing, H.-M.L., T.-F.C., G.C., G.Z., M.-W.L., W.-S.Y. and X.W.; visualization, M.-W.L.; supervision, H.-M.L.; project administration, H.-M.L.; funding acquisition, H.-M.L. and T.-F.C. All authors have read and agreed to the published version of the manuscript.

Funding

The research was funded by the Guangdong Provincial Department of Science and Technology 2020 Key Areas Research and Development Programs: Breeding for High Yield and High Quality New Soybean Cultivars for Tropics and Subtropics (2020B020220008), Hong Kong Research Grants Council Area of Excellence Scheme RGC-AoE scheme (AoE/M-403/16) to H.-M.L. and T.-F.C. and Lo Kwee-Seong Biomedical Research Fund to H.-M.L.

Data Availability Statement

The data presented in this study are available within the article and its companion Supplementary Materials.

Acknowledgments

We thank Sachiko Isobe from Kazusa DNA Research Institute for her kind sharing of the seeds of JC01 and JC03, Ching-Yee Luk and Man-Chun Wu for the literature search regarding the soybean agronomic traits, Michelle Chau for soybean photo taking, and Jee-Yan Chu for copy-editing this manuscript. Any opinions, findings, conclusions, or recommendations expressed in this publication do not reflect the views of the Government of Hong Kong Special Administrative Region or the Innovation and Technology Commission.

Conflicts of Interest

The authors declare no conflict of interest.

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Agronomy 13 02708 g001
Figure 1. Gel images illustrating the expected amplified band patterns of the 100 markers. Agarose gel images of 88 random SNP markers and 12 trait-associated markers are shown. The left and right lanes of each gel image are the amplified band pattern of the reference genotype and of the alternative genotype, respectively.
Figure 1. Gel images illustrating the expected amplified band patterns of the 100 markers. Agarose gel images of 88 random SNP markers and 12 trait-associated markers are shown. The left and right lanes of each gel image are the amplified band pattern of the reference genotype and of the alternative genotype, respectively.
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Figure 2. Distributions and allele frequencies of the selected markers on the 20 chromosomes of the soybean genome. Grey horizontal bars, soybean chromosomes; green vertical lines, physical positions of random SNP markers; purple vertical lines, physical positions of trait-associated markers; orange bars above the chromosomes, frequencies (with scale above the corresponding chromosome numbers on the left) of the reference alleles in the 228 re-sequenced soybean accessions. The chromosomes and markers were illustrated using the R package “ChromoMap” (version 4.1.1) [42].
Figure 2. Distributions and allele frequencies of the selected markers on the 20 chromosomes of the soybean genome. Grey horizontal bars, soybean chromosomes; green vertical lines, physical positions of random SNP markers; purple vertical lines, physical positions of trait-associated markers; orange bars above the chromosomes, frequencies (with scale above the corresponding chromosome numbers on the left) of the reference alleles in the 228 re-sequenced soybean accessions. The chromosomes and markers were illustrated using the R package “ChromoMap” (version 4.1.1) [42].
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Figure 3. Validation of soybean trait-associated marker examples selected from the panel. (A) Markers associated with hilum color. Pigmentation indicates the color of the hilum. U: unpigmented; P: pigmented. (B) Markers associated with salt tolerance. Salt tolerance levels were retrieved from a previous study [21]. Asterisk, common outer amplicon; filled triangle, amplified fragment of the salt tolerance allele; open triangle, amplified fragment of the salt-sensitive allele.
Figure 3. Validation of soybean trait-associated marker examples selected from the panel. (A) Markers associated with hilum color. Pigmentation indicates the color of the hilum. U: unpigmented; P: pigmented. (B) Markers associated with salt tolerance. Salt tolerance levels were retrieved from a previous study [21]. Asterisk, common outer amplicon; filled triangle, amplified fragment of the salt tolerance allele; open triangle, amplified fragment of the salt-sensitive allele.
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Table 1. Summary of soybean accessions used for marker verification.
Table 1. Summary of soybean accessions used for marker verification.
AccessionDescriptionSource/Reference
W01Wild soybean, China[3]
W02Wild soybean, China[3]
W03Wild soybean, China[3]
W04Wild soybean, China[3]
W05Wild soybean, China[3]
W06Wild soybean, China[3]
W07Wild soybean, China[3]
W08Wild soybean, China[3]
W09Wild soybean, China[3]
W10Wild soybean, China[3]
W11Wild soybean, China[3]
W12Wild soybean, China[3]
W13Wild soybean, China[3]
W14Wild soybean, China[3]
W15Wild soybean, China[3]
W16Wild soybean, China[3]
W17Wild soybean, China[3]
C01Cultivar, China[3]
C02Cultivar, China[3]
C08Cultivar, USA[3]
C12Cultivar, China[3]
C14Cultivar, Brazil[3]
C16Cultivar, Taiwan[3]
C17Cultivar, China[3]
C19Cultivar, China[3]
C24Cultivar, China[3]
C27Cultivar, China[3]
C30Cultivar, China[3]
C33Cultivar, China[3]
C34Cultivar, China[3]
C35Cultivar, China[3]
Zhonghuang13 (ZH13)Cultivar, China[34]
JC01Cultivar, JapanGift from Prof Sachiko Isobe
JC03Cultivar, JapanGift from Prof Sachiko Isobe
KC08Cultivar, JapanChung’s Wild Legume Germplasm Collection
KC09Cultivar, JapanChung’s Wild Legume Germplasm Collection
KC11Cultivar, Republic of KoreaChung’s Wild Legume Germplasm Collection
KC12Cultivar, Republic of KoreaChung’s Wild Legume Germplasm Collection
KC13Cultivar, Republic of KoreaChung’s Wild Legume Germplasm Collection
DN50Cultivar, ChinaGansu Academy of Agricultural Sciences
JX6Cultivar, ChinaLam HM’s laboratory soybean collections
JX208Cultivar, ChinaLam HM’s laboratory soybean collections
JXHMDCultivar, ChinaLam HM’s laboratory soybean collections
LH1Cultivar, China[36]
LD78-1Cultivar, ChinaGansu Academy of Agricultural Sciences
QJDCultivar, ChinaLam HM’s laboratory soybean collections
SJD1Cultivar, ChinaLam HM’s laboratory soybean collections
SDD1Cultivar, ChinaLam HM’s laboratory soybean collections
Table 2. Selected traits for polymorphic marker development.
Table 2. Selected traits for polymorphic marker development.
Associated PhenotypeAssociated Gene/
Polymorphism		References
Salt toleranceGmCHX1/GmSALT3[21,43]
Flowering and maturation timeE1[44]
E2/GmGIa[27]
GmPRR3b/Tof12[24,45]
Stay green/dormancyG[46]
Pod shatteringPdh1[47]
Flower colorW1[48]
Plant height and trailing habitGA2ox8[49]
Hilum colorGmIRCHS/I locus[2,50]
Protein/oil contentPOWR1[51,52]
Leaf shape and seed numberGmJAG1/Ln[53]
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Li, M.-W.; Wang, X.; Sze, C.-C.; Yung, W.-S.; Wong, F.-L.; Zhang, G.; Chung, G.; Chan, T.-F.; Lam, H.-M. Development of a Set of Polymorphic DNA Markers for Soybean (Glycine max L.) Applications. Agronomy 2023, 13, 2708. https://doi.org/10.3390/agronomy13112708

AMA Style

Li M-W, Wang X, Sze C-C, Yung W-S, Wong F-L, Zhang G, Chung G, Chan T-F, Lam H-M. Development of a Set of Polymorphic DNA Markers for Soybean (Glycine max L.) Applications. Agronomy. 2023; 13(11):2708. https://doi.org/10.3390/agronomy13112708

Chicago/Turabian Style

Li, Man-Wah, Xin Wang, Ching-Ching Sze, Wai-Shing Yung, Fuk-Ling Wong, Guohong Zhang, Gyuhwa Chung, Ting-Fung Chan, and Hon-Ming Lam. 2023. "Development of a Set of Polymorphic DNA Markers for Soybean (Glycine max L.) Applications" Agronomy 13, no. 11: 2708. https://doi.org/10.3390/agronomy13112708

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