1. Introduction
Pod shattering is an essential mechanism for reproduction under appropriate environments in many wild species [1,2,3]. Several morphological and physiological changes have been found during domestication, and this is termed ‘domestication syndrome’ [4,5]. Among the domestication syndromes, pod shattering is one of the key strategies for increasing the survival rate of plants through reducing competition [4]. Crops including soybean (Glycine max) have been evolved to become more tolerant to pod shattering than their wild progenitors through the natural selection from non-pod shattering plants [6].
Pod shattering is common in the species of Legume, Gramineae, and Brassicaceae crop families [2,7]. In addition, many studies on pod shattering have been conducted in domesticated crops, resulting in numerous genes being identified in rice [8,9], soybean [2,10], common bean [11], Medicago [12], and cowpea [13,14]. In soybean, pod shattering at maturing stages can result in serious yield loss, especially under dry weather conditions. The cultivation of pod-shattering-susceptible cultivars could lead to yield losses of 50–100% [15]. In many breeding programs, tolerance to pod shattering is considered an essential trait to minimize yield loss under current environmental conditions, including global warming and dry weather conditions.
Pod shattering in soybeans is considered a quantitative trait controlled by several genes [16] and researchers have reported that the trait is regulated by a major quantitative trait locus (QTL) and multiple minor QTLs [17]. In soybeans, a major QTL associated with pod shattering was first identified on chromosome 16 using restriction fragment length polymorphism (RFLP) markers [18]. Funatsuki et al. [19] found this QTL to be localized between the simple sequence repeat (SSR) markers Sat_093 and Sat_366 and named it qPDH1 [20]. Several studies detected the qPDH1 locus through linkage mapping using bi-parental populations with different genetic backgrounds [16,21,22,23,24]. Fine mapping [25,26] led to cloning of the Pdh1 (Glyma.16g141400) gene, which increases the torsion of dried pods under low humidity, and was found to have a premature stop codon [2]. Also identified using wild soybean was the NAC gene, named SHAT1-5 (Glyma.16g019400), which activates secondary wall biosynthesis and promotes the thickening of fiber cap cells [10]. Several minor QTLs were also detected on chromosomes 2, 5, 10, 14, and 19 [16,18]. More recently, several novel QTLs were found on chromosomes 1, 4, 6, 8, 9, 11, 17, 18, and 20 through genome-wide association analysis (GWAS) [27], on chromosomes 1, 5, 8, and 14 through specific-locus amplified fragment sequencing (SLAF) [28], and on chromosome 19 through RNA sequencing [5]. Despite the high value of phenotypic variance explained (PVE) by the major QTL on chromosome 16 (qPDH1), which accounts for more than 50% of the entire phenotypic variance [19,23], the mechanism of pod shattering tolerance is, presently, still not completely understood. Therefore, additional QTL information from diverse genetic backgrounds is pivotal to developing soybean cultivars with shattering tolerance.
Daewonkong (DW) [29], developed in 1997, is an elite cultivar with several desirable traits, such as strong tolerance to pod shattering and seed quality for processing soy food, and accounts for more than 80% of the total soybean cultivation area in Korea [30]. Although it is an important genetic source for pod shattering tolerance in Korea, genetic information on the pod shattering tolerance of DW has not previously been reported. In our previous study [30], we investigated pod shattering tolerance using two recombinant inbred line (RIL) populations derived from DW and found that the lines that were tolerant and susceptible to pod shattering were remarkably distinct. From the results, we predicted that a major QTL would be segregated in the populations.
The objectives of this study were (1) to construct high-density linkage maps and discover the QTLs for pod shattering using two RIL populations derived from DW and (2) to predict novel candidate QTL/genes based on the allele patterns obtained in the populations. To achieve these objectives, we evaluated the pod shattering ratio in the RIL populations over three years and genotyped the populations using a 180 K SNP array. 2. Results 2.1. Variation in Pod Shattering Ratios
To evaluate the pod shattering tolerance of the mapped populations, we performed the oven-dry test for soybean grown over a three-year period (2016 to 2018) and calculated the pod shattering ratio after drying matured pods for 24, 48, and 72 h [30]. Across the three experimental years, DW showed a strong tolerance to pod shattering with no shattered pods in the three oven-dry tests. On the other hand, all the pods of Saeolkong (SO) were shattered after 24 h in the oven-dry test. Tawonkong (TW) showed a moderate tolerance up to 24 h with a 25% pod shattering ratio. However, up to 80% of the pods were shattered after 72 h of the drying test (Table 1 and Figure 1).
In the DT population, 16.6%, 37.0%, and 49.7% of pods were shattered after 24, 48, and 72 h, respectively, showing a tendency for more pod shattering with the extension of drying time. A similar tendency was also found in the DS population with 26.7%, 48.7%, and 55.3% pod shattering ratios after 24, 48, and 72 h, respectively (Table 1). The H2 in the DT population ranged from 0.38 to 0.88 and, in the DS population, from 0.62 to 0.84 (Table 1). The distributions of pod shattering in the mapping populations were similar (Figure 1).
2.2. Linkage Map Construction
Out of the 169,028 high-quality and genotyped SNP markers, 24,407 (DW and TW) and 21,462 (DW and SO) markers were found to be polymorphic between the parental lines. After deleting the redundant markers with >5% missing values and segregation distortion of p < 0.05, a total of 2321 and 1739 SNPs were selected and used to construct linkage maps for the DT and DS populations, respectively. The SNPs were distributed across all 20 chromosomes with 1.1 and 1.8 cM average distances between adjacent SNPs in the DT and DS populations, respectively (Supplementary Tables S1 and S2).
2.3. QTL Analysis
Across the years of the experiment, a major QTL was detected on chromosome 16 in both populations with high logarithm of odds (LOD) (up to 67.1 in DT and 60.0 in DS) and PVE values (up to 82.8% in DT and 80.1% in DS). The major QTL identified in the present study was designated as qPS-DT16-2 and includes the qPDH1 locus (Table 2 and Table 3). Similarly, qPS-DT16-1 and qPS-DS16-3 were also detected within the qPDH1 locus. These QTLs were stably detected in individuals and across the years. The LOD scores for the QTLs of the DT population ranged 13.4–67.1 with a PVE of 26.1–82.8%, and those of the DS population ranged 14.6–60.0 and 36.0–80.1%, respectively (Table 2 and Table 3). Specifically, qPS-DT16-2 showed the highest LOD and PVE values in 2017 for 72 h of drying. The identified QTL region contains five candidate genes which are located within the previously reported qPDH1 locus [21,23,26].
Several population-specific QTLs for pod shattering were also detected on chromosomes 2, 6, 11, 13, 14, and 20 in the DT population and on chromosome 16 in the DS population (Figure 2). Possibly, due to the great effect of the major QTL, these QTLs showed relatively lower LOD (3.6 to 33.4) and PVE (1.3 to 16.4%) values than those of the major QTL. qPS-DT13 showed the lowest PVE value and qPS-DT14 showed the highest PVE value. Among these QTLs, qPS-DT11, qPS-DT14, and qPS-DS16-1 were also promising QTLs with relatively high values of PVE (>10%). All QTLs showed a negative additive effect (Table 2 and Table 3).
2.4. Phenotypic Variation According to the Allele Patterns
The combination of RILs with the P1 (DW) allele in qPS-DS16-2 (major QTL locus) and qPS-DS16-1 simultaneously presented stronger tolerance to pod shattering with a 2.5% pod shattering ratio compared to those with the P2 (SO) allele (6.2%, p < 0.05) at 24 h of drying. Despite having the P2 (SO) allele in qPS-DS16-2, the RILs with the P1 (DW) allele in qPS-DS16-1 presented better tolerance than those with the P2 (SO) allele at all drying periods (Table 4). There were also differences in the pod shattering ratio according to the allele patterns in the qPS-DS16-1 region; RILs in which the P1 (DW) allele followed the qPS-DS16-1 region showed a higher level of tolerance to pod shattering than when the P2 (SO) allele followed (Figure 3). However, qPS-DT11 and qPS-DT14 showed no significant differences in pod shattering tolerance regardless of the P1 (DW) or P2 (TW) allele in the major QTL (qPS-DT16-2) region (Table 4). Considering all allele patterns of the QTL, the qPS-DS16-1 region played a key role in the pod shattering tolerance of the RIL populations. Therefore, we inferred the existence of a candidate gene for pod shattering tolerance in the qPS-DS16-1 region.
2.5. Identification of Candidate Genes within and Near QTL
In the present study, several QTLs were detected on chromosome 16. Within and near the QTL regions, there were five candidate genes in the major QTL (qPS-DT16-2) which showed the highest value of PVE and 41 candidate genes in the promising QTL (qPS-DS16-1) (Table 2 and Table 3). Among the genes residing in the promising QTL regions, six genes had SNPs or insertions/deletions that could cause amino acid sequence variations: Glyma.16g072700, Glyma.16g073700, Glyma.16g076100, Glyma.16g076300, Glyma.16g076500, and Glyma.16g076600 (Table 5).
Except for Glyma.16g073700 (unknown function), the expression of the other five genes (Supplementary Table S3) was analyzed by qPCR in the pods harvested from R3 (beginning pod) to R6 (full seed) growing stages [31,32]. Among these five genes, Glyma.16g076600 showed a significantly different expression level between the tolerant parent (DW) and susceptible parents (TW and SO) (Figure 4). There were six polymorphisms in Glyma.16g076600, which includes four missense variant SNP and two insertion/deletion. In particular, an 18 bp insertion in position 7,775,970 is a stop-gain mutation that results in a premature termination codon at the end of the sequence in exon 7.
3. Discussion
Pod shattering at the maturing stage (R8, full maturity) is an essential strategy for reproduction in wild species, including in Glycine soja. However, it can cause tremendous yield loss in cultivated crop species and be one of the main limiting factors for machinery harvesting. Therefore, developing varieties tolerant to pod shattering is one of the fundamental goals of many crop breeding programs. In Korea, DW is the most widely cultivated soybean cultivar due to its strong tolerance to pod shattering. Therefore, DW could be a valuable genetic source for developing pod shattering tolerant varieties. In the present study, DW showed strong tolerance to pod shattering in the oven-dry test, where no pods were shattered until 72 h of drying (Supplementary Figure S1), whereas for the susceptible parents, TW and SO, numerous pods were shattered after 24 h of drying (Table 1 and Figure 1).
In soybean, pod shattering is considered as a quantitative trait of high heritability that is controlled by a major gene and several minor genes [17]. Since identification of the first major QTL on chromosome 16 [18], two genes on chromosome 16 have been characterized, named pdh1 (Glyma.16g141400) from cultivated soybean and SHAT1-5 (Glyma16g019400) from wild soybean [2,10]. In cultivated soybean, pdh1 is a major gene for pod shattering and is annotated as a dirigent-like protein in the soybean reference genome (Wm82.a1.v2). Recently, based on the changed gene model Wm82.a2.v1, the pdh1 gene was identified as Glyma.16g141400 (unknown function). In this study, we exploited the distinct phenotypic difference between the parents to successfully identify the major QTL on chromosome 16 located within or nearby the qPDH1 locus in both populations (qPS-DT16-1, qPS-DT16-2, qPS-DS16-2, and qPS-DS16-3) (Table 2 and Table 3). However, the QTL region including SHAT1-5, which was identified in previous studies, was not detected in the present study. Among the major QTLs, the qPS-DT16-2 in the DT population showed the highest LOD and PVE values in this study. The QTL region presented higher PVE values (82.8%) and was mapped at a narrower interval (59 kb) than previous studies, and the region was found to have only five candidate genes (Table 2 and Table 3). The negative values of the additive effect indicate that the phenotypic variation explained by the QTLs result from the pod shattering-tolerant parent DW.
More recently, several novel candidate genes were identified on chromosome 9 (Glyma09g06290) using genome-wide association mapping [27], chromosome 1 (Glyma.01g045800 and Glyma.01g046000), chromosome 5 (Glyma.05g005600, Glyma.05g225900, and Glyma.05g227400), and chromosome 8 (Glyma.08g271900 and Glyma.08g274500) by specific-locus amplified fragment sequencing [28], and chromosome 19 (Glyma.19g231900) by RNA sequencing [5]. In this study, we also found several QTLs that showed relatively low PVE values on chromosomes 2, 6, 11, 13, 14, 16, and 20 (Figure 2). Among these, only qPS-DT02 on chromosome 2 mapped to a previously reported region, in a study by Kang et al. (2009), whereas the others mapped to different physical positions on the same chromosomes [16,27,28] (Table 2 and Table 3). There was a total of 472 candidate genes in novel QTLs based on the physical position of Williams82 (Wm82.a2.v1). Those genes were related to many metabolic and biological process in plant and contributed to biosynthesis or catabolism of several plant hormones such as auxin, gibberellic acid, and abscisic acid (ABA) (Supplementary Table S4).
Among the detected QTLs, excluding the qPDH1 locus, we selected only the promising QTLs for further searching of candidate genes with >10% PVE values on chromosomes 11, 14, and 16. The results of the allele patterns of these QTLs showed that the allele patterns in the qPS-DS16-1 region could lead to a significant difference in the pod shattering tolerance between genotypes. A total of 41 candidate genes were annotated within or near the qPS-DS16-1 region (72–78 Mbp, based on the physical position of the reference genome Wm82.a2.v1) (Table 3). Out of the 41 genes, six genes have SNP/indel variations in the coding sequence of the parents. Glyma.16g076600, one of the six genes, showed highly differential levels of expression between the parents in the growth stages R3 to R6 (Figure 4). This gene was highly expressed in the susceptible parent SO, one of the parents of the RIL population where QTL qPS-DS16-1 was detected. On the other hand, there was a very low expression of this gene in the resistant parent DW and intermediate expression in TW, indicating the role of Glyma.16g076600 in pod shattering.
Glyma.16g076600 is a homolog gene of AT4G19230 in Arabidopsis, which is a member of the CYP707A gene family encoding a protein related to ABA catabolism [33]. ABA is a hormone that responses to environmental stresses and regulates water consumption in plants. Under drought conditions, the endogenous ABA level increases in plants, and the enzyme CYP707A controls endogenous ABA levels; overexpression of CYP707A leads to a decrease in ABA levels [34,35]. An upregulation of ABA induces the expression of many genes that play an important role in the adaptation to abiotic stresses and reduced shattering [36].
In this study, the distinctly high expression of Glyma.16g076600 in TW and SO could be an indicator of the level of ABA production in the pods that might cause pod shattering. The role of hormones, including ABA, has been reported as regulating silique dehiscence in Arabidopsis and Brassica [36]. Thus, it could be assumed that the Glyma.16g076600 regulates the ABA expression in pods, and high expression could be associated with susceptibility to pod shattering. However, further studies are required to confirm the relationship between the pod shattering and ABA levels in the pods as well as other parts in soybeans.
4. Materials and Methods 4.1. Plant Material
Two RIL populations were derived from the crosses between a pod-shattering-tolerant cultivar Daewonkong (DW) as the female parent and two pod-shattering-susceptible cultivars, Tawonkong (TW) [37] and Saeolkong (SO) [38], as the male parents. Daewonkong, developed in 1997, is the most popular soybean cultivar in South Korea due to its strong tolerance to pod shattering. The RIL populations were developed through the single seed descent method from the F2 generation to F5:6, F6:7, and F7:8 generations in each experimental year (2016, 2017, and 2018). The populations consisting of 154 lines (DW × TW (DT)) and 153 lines (DW × SO (DS)) were used to construct high-density linkage maps and to analyze for QTLs associated with pod shattering tolerance [30].
4.2. Evaluation of Pod Shattering Tolerance
The evaluation of pod shattering tolerance over three years was performed as described in our previous study [30]. Briefly, mature pods from a total of 307 RILs and three parents grown in field conditions at the Miryang in South Korea were harvested at the maturing stage (R8 stage) and kept at room temperature for a week [23]. After one week, the pod shattering ratio of each RIL was determined as the ratio of the number of shattered pods to the total number of pods counted for 24, 48, and 72 h of drying in a dry oven at 40 °C [39] for three experimental years (2016, 2017, and 2018).
4.3. Genomic DNA Extraction and Genotyping
Young trifoliate leaves of the parents and F7:8 RILs were bulk harvested at the V2 stage and kept in a deep freezer at −80 °C. The bulked leaves were ground using a bead beater (TissueLyser II; Qiagen, Hilden, Germany). The genomic DNA was extracted using a commercial kit (Exgene Plant SV Miniprep Kit; GeneAll, Seoul, Korea) following the manufacturer’s instruction and 50 µL of AE buffer was used to elute DNA [40]. The 180K Axiom® SoyaSNP array [41,42,43] was used to genotype the parents and 307 RILs of two populations.
4.4. Linkage Map Construction and QTL Mapping
Prior to the construction of linkage maps, redundant markers with identical segregation patterns that did not provide an additional contribution to genetic mapping were discarded using the Bin function in QTL IciMapping V4.2 software. The parameters for map construction were set as grouping by a 3.0 logarithm of odds (LOD) threshold, ordering by nnTwoOpt, and rippling by the sum of adjacent recombination fractions. Mapping was conducted using Kosambi’s mapping function, and QTLs were identified with inclusive composite interval mapping (ICIM) using QTL IciMapping V4.2 [44].
The names of QTLs were designated by combining different letters and numbers as follows: q, quantitative trait loci, PS, pod shattering, DT, RIL derived from the cross between Daewonkong and Tawonkong, and DS, RIL derived from the cross between Daewonkong and Saeolkong. The numbers followed by the letters indicating the name of the RIL population signify the chromosome containing the QTL. For example, QTLs named qPS-DT16-2 and qPS-DT11 indicate the second QTL for pod shattering on chromosome 16 and only one QTL for pod shattering on chromosome 11 in the RIL population (DT) derived from Daewonkong and Tawonkong, respectively. 4.5. Prediction of Novel Candidate QTL and Genes
The QTLs with high PVE values (>10%), excluding the qPDH1 locus, were selected. Then, the statistically significant QTLs for pod shattering were identified according to their allele patterns based on the SNP sequence within the QTL regions. Candidate genes within or adjacent to the candidate QTL were identified based on the SoyBase (www.soybase.org) and Phytozome (www.phytozome.net). The gene description was obtained from the Williams_82 soybean reference genome (Wm82.a2.v1).
4.6. Gene Expression Analysis of the Candidate Genes
For total RNA extraction, pods of DW, TW, and SO were harvested from bulked plants at the R3, R4, R5, and R6 growth stages, respectively. The total RNA of the pods was isolated using a RNeasy PowerPlant Kit (Qiagen, Hilden, Germany) and the cDNA was synthesized using a reverse transcription reaction (EcoDry cDNA Synthesis Premix, Takara Bio, Inc., Ohtsu, Japan) following the manufacturer’s instructions. Gene expression was determined by qPCR using an ABI 7300 system (Applied Biosystems, Foster City, CA, USA) with Power SYBR Green PCR Master Mix (Applied Biosystems, Woolston Warrington, UK). All experiments were performed with three replications, and the results were analyzed using ExpressionSuite Software V1.3 (Life Technologies, Foster City, CA, USA). Primers for the target genes (Supplementary Table S3) were designed using Primer3.0 (http://primer3.ut.ee/). GmActin gene (Glyma.18g290800) was used as a control gene.
4.7. Statistical Analysis
Student’s t-test and Duncan’s multiple range test (DMRT) were conducted using R V3.6.3 software (R Core Team, 2020) to compare the phenotypic variations of pod shattering tolerance and the expression levels of candidate genes. The broad-sense heritability (H2) was calculated using the following equation:H2= σg2 / σp2 [45].
5. Conclusions In the present study, one major QTL, on chromosome 16, which included the qPDH1 locus, and several novel QTLs on chromosomes 2, 6, 11, 13, 14, 16, and 20 were identified from high-density linkage maps of two RIL populations. Among these novel QTLs, we identified qPS-DS16-1 (Glyma.16g076600) as potentially playing a role in pod shattering tolerance based on the expression patterns of this gene. The function of Glyma.16g076600, a member of the CYP707A family, has been reported as possibly being related to the catabolism of ABA, a hormone known to be associated with a variety of physiological functions, including pod shattering. Therefore, the results of this study provide useful information to understand the genetic mechanism of pod shattering and could be used for improving the efficiency of marker-assisted selection for developing pod-shattering-tolerant varieties in soybean.
Enlarge this image.Figure 1. Distribution of pod shattering in the two recombinant inbred line (RIL) populations after pod drying; (a) for 24 h, (b) for 48 h, (c) for 72 h (DW; Daewonkong, TW; Tawonkong, SO; Saeolkong) (Seo et al. 2019).
Enlarge this image.Figure 2. Quantitative trait loci (QTLs) associated with pod shattering tolerance; (a) population derived from 'Daewonkong' and 'Tawonkong', (b) population derived from 'Daewonkong' and 'Saeolkong'. The bars inside each chromosome represent the position of markers used to construct the linkage map.
Enlarge this image.Figure 3. Variation of pod shattering ratio after 24, 48, and 72 h of drying according to the allele patterns in qPS-DS16-2 (major QTL locus) and qPS-DS16-1 locus. 'P1' indicates that the allele patterns are the same as 'Daewonkong (DW)' and 'P2' indicates that the allele patterns are the same as 'Saeolkong (SO)'; (a) following 'P1' allele in qPS-DS16-2, (b) following 'P2' allele in qPS-DS16-2.
Enlarge this image.Figure 4. Expression level of five candidate genes in the pods harvested at the growth stages from R3 to R6. Same letters denote no significant differences at 0.05 by DMRT.
| Drying Time (Hour) | Year | Pod Shattering Ratio ± SE (%) † | Broad-Sense Heritability | |||||
|---|---|---|---|---|---|---|---|---|
| Parents | RILs | |||||||
| DW | TW | SO | DT | DS | DT | DS | ||
| 24 | 2016 | 0 | 25 | 100 | 12.1 ± 24.6 | 24.4 ± 33.5 | ||
| 2017 | 0 | 0 | 100 | 9.9 ± 34.8 | 23.3 ± 33.8 | |||
| 2018 | 0 | 50 | 100 | 27.8 ± 40.4 | 32.5 ± 32.0 | |||
| average | 0 | 25 | 100 | 16.6 ± 19.0c ‡ | 26.7 ± 26.2c | 0.38 | 0.62 | |
| 48 | 2016 | 0 | 35 | 100 | 27.8 ± 34.7 | 47.7 ± 42.9 | ||
| 2017 | 0 | 20 | 100 | 38.4 ± 39.6 | 48.0 ± 46.1 | |||
| 2018 | 0 | 50 | 100 | 44.8 ± 36.4 | 50.5 ± 38.6 | |||
| average | 0 | 35 | 100 | 37.0 ± 31.9b | 48.7 ± 39.1b | 0.78 | 0.84 | |
| 72 | 2016 | 0 | 80 | 100 | 43.6 ± 40.3 | 54.7 ± 43.6 | ||
| 2017 | 0 | 100 | 100 | 53.5 ± 43.2 | 51.1 ± 46.8 | |||
| 2018 | 0 | 60 | 100 | 52.0 ± 37.7 | 60.1 ± 37.3 | |||
| average | 0 | 80 | 100 | 49.7 ± 37.4a | 55.3 ± 39.0a | 0.88 | 0.82 | |
† mean pod shattering ratio at the different drying time over three experimental years (DW; Daewonkong, TW; Tawonkong, SO; Saeolkong, DT; RIL derived from crossing between ‘Daewonkong’ and ‘Tawonkong’, DS; RIL derived from crossing between ‘Daewonkong’ and ‘Saeolkong’. ‡ Different letters mean their means are significantly different in the Duncan’s multiple range test (p < 0.05).
| Name of QTL | Chr. (LG) 1 | Year | Drying Time (Hour) | Position (cM) | Physical Interval 2 (bp) | LOD 3 | PVE 4 (%) | Add 5 | No. Candidate Genes | References |
|---|---|---|---|---|---|---|---|---|---|---|
| qPS-DT02 | 2 (D1b) | 2017 | 24 | 122 | 7,211,739–8,082,789 | 6.0 | 1.9 | −5.9 | 87 | Kang et al. 2009 |
| qPS-DT06 | 6 (C2) | 2016 | 48 | 103 | 5,498,397–6,170,213 | 3.6 | 6.1 | −9.0 | 82 | Novel |
| qPS-DT11 | 11 (B1) | 2017 | 48 | 92 | 26,717,840–30,547,921 | 17.4 | 13.3 | −20.0 | 189 | Novel |
| qPS-DT13 | 13 (F) | 2017 | 24 | 108 | 35,019,516–35,459,889 | 4.3 | 1.3 | −5.0 | 54 | Novel |
| qPS-DT14 | 14 (B2) | 2017 | 24 | 11 | 48,063,838–48,101,634 | 33.4 | 16.4 | −17.8 | 2 | Novel |
| qPS-DT16-1 | 16 (J) | 2017 | 24 | 63 | 29,532,807–29,809,243 | 7.4 | 2.3 | −6.7 | 21 | qPDH1(nearby) |
| 2018 | 72 | 29,532,807–29,809,243 | 31.0 | 67.8 | −32.0 | |||||
| qPS-DT16-2 | 16 (J) | 2016 | 48 | 64 | 29,913,393–29,972,096 | 13.4 | 26.1 | −19.0 | 5 | qPDH1 (Funatsuki et al. 2008) |
| 2016 | 72 | 29,913,393–29,972,096 | 23.9 | 48.0 | −29.2 | |||||
| 2017 | 48 | 29,913,393–29,972,096 | 30.1 | 29.6 | −30.3 | |||||
| 2017 | 72 | 29,913,393–29,972,096 | 67.1 | 82.8 | −42.0 | |||||
| Combined 6 | 24 | 29,913,393–29,972,096 | 13.4 | 33.1 | −11.2 | |||||
| Combined | 48 | 29,913,393–29,972,096 | 35.7 | 60.8 | −25.9 | |||||
| Combined | 72 | 29,913,393–29,972,096 | 55.4 | 78.7 | −34.6 | |||||
| qPS-DT20 | 20 | Combined | 48 | 93 | 2,151,689–2,392,383 | 3.7 | 3.7 | −6.3 | 17 | Novel |
1 Chromosome (Linkage group). 2 Based on the physical position of ‘Williams82’ ver. 2.1 (www.soybase.org). 3 Logarithm of odds. 4 Phenotypic variance explained by QTL. 5 Additive effect; negative values indicate that ‘Daewonkong’ contributed to the trait. 6 Average value of 3 years, 2016, 2017, and 2018.
| Name of QTL | Chr. (LG) 1 | Year | Drying Time (Hour) | Position (cM) | Physical Interval 2 (bp) | LOD 3 | PVE 4 (%) | Add 5 | No. Candidate Genes | References |
|---|---|---|---|---|---|---|---|---|---|---|
| qPS-DS16-1 | 16 (J) | Combined 6 | 24 | 61 | 7,325,202–7,760,393 | 5.4 | 11.2 | −7.1 | 41 | Novel |
| qPS-DS16-2 | 16 (J) | 2016 | 48 | 45 | 29,857,920–30,146,414 | 19.5 | 44.8 | −28.7 | 20 | qPDH1 (Funatsuki et al. 2008) |
| 2016 | 72 | 29,857,920–30,146,414 | 18.0 | 42.4 | −28.3 | |||||
| 2017 | 24 | 29,857,920–30,146,414 | 14.6 | 36.0 | −20.2 | |||||
| 2017 | 48 | 29,857,920–30,146,414 | 56.0 | 61.2 | −40.9 | |||||
| 2017 | 72 | 29,857,920–30,146,414 | 60.0 | 63.0 | −41.9 | |||||
| Combined | 24 | 29,857,920–30,146,414 | 19.2 | 48.7 | −14.8 | |||||
| Combined | 48 | 29,857,920–30,146,414 | 40.9 | 70.3 | −26.8 | |||||
| Combined | 72 | 29,857,920–30,146,414 | 40.8 | 71.8 | −26.9 | |||||
| qPS-DS16-3 | 16 (J) | 2018 | 24 | 43 | 30,144,267–30,580,464 | 15.2 | 56.8 | −24.4 | 25 | qPDH1 (nearby) |
| 2018 | 48 | 30,144,267–30,580,464 | 31.8 | 80.1 | −37.0 | |||||
| 2018 | 72 | 30,144,267–30,580,464 | 27.2 | 76.4 | −34.3 |
1 Chromosome (Linkage group). 2 Based on the physical position of ‘Williams82’ ver. 2.1 (www.soybase.org). 3 Logarithm of odds. 4 Phenotypic variance explained by QTL. 5 Additive effect; negative values indicate that ‘Daewonkong’ contributed to the trait. 6 Average value of 3 years, 2016, 2017, and 2018.
| Genotype 1 | qPS-DS16-1 | qPS-DT11 | qPS-DT14 | |||||||
|---|---|---|---|---|---|---|---|---|---|---|
| Major | Minor | 24 h | 48 h | 72 h | 24 h | 48 h | 72 h | 24 h | 48 h | 72 h |
| P1 | P1 | 2.5 | 6.5 | 10.9 | 2.6 | 6.5 | 10.7 | 3.5 | 8.1 | 12.7 |
| P2 | 6.2 | 12.5 | 18.6 | 4.3 | 10.7 | 15.6 | 3.8 | 9.0 | 13.6 | |
| t-value 2 | −1.68 * | −1.50 ns | −1.61 ns | −1.15 ns | −1.45 ns | −1.44 ns | −0.15 ns | −0.29 ns | −0.24 ns | |
| P2 | P1 | 24.3 | 60.5 | 76.1 | 37.2 | 70.3 | 82.8 | 32.0 | 65.6 | 81.7 |
| P2 | 37.6 | 69.9 | 83.5 | 31.9 | 65.9 | 80.8 | 36.8 | 69.8 | 81.6 | |
| t-value | −3.81 ** | −2.07 * | −2.00 * | 1.43 ns | 1.12 ns | 0.73 ns | −1.32 ns | −1.09 ns | 0.04 ns | |
1 ‘P1’ indicates that the allele patterns are the same as ‘Daewonkong’ and ‘P2’ indicates that the allele patterns are the same as ‘Tawonkong’ and ‘Saeolkong’. 2 * and ** denote significant difference between the phenotypes by student t-test (p < 0.05 and p < 0.01, respectively), ns denotes there was no significant difference.
| Gene Name | Position (bp) | Reference (Williams82) | DW | TW | SO | AA Change | Description |
|---|---|---|---|---|---|---|---|
| Glyma.16g072700 | 7,335,363 | - | - | 12 bp deletion | 12 bp deletion | Deletion | Myb-like DNA-binding domain, DNA binding |
| Glyma.16g073700 | 7,446,096 | A | A | T | T | Tyr/Asn | Unknown |
| Glyma.16g076100 | 7,665,996 | C | T | C | C | Glu/Lys | Rho guanyl-nucleotide exchange factor activity |
| Glyma.16g076300 | 7,725,602 | A | C | A | A | Met/Leu | GMC oxidoreductase |
| Glyma.16g076500 | 7,760,100 | T | C | T | T | Leu/Ser | Adaptin N terminal region |
| 7,760,393 | A | C | A | A | Ile/Leu | ||
| Glyma.16g076600 | 7,775,892 | C | T | C | C | Glu/Lys | Cytochrome P450, |
| 7,775,945 | T | A | T | T | Lys/Met | (+)-abscisic acid 8′-hydroxylase activity | |
| 7,775,948 | A | G | A | A | Ile/Thr | ||
| 7,775,970 | - | 18 bp insertion | - | - | Insertion | ||
| 7,776,045 | C | T | C | C | Met/Ile | ||
| 7,777,575 | - | 3 bp deletion | - | - | Asn/- |
DW: Daewonkong, TW: Tawonkong, SO: Saeolkong, AA: amino acid.
Supplementary Materials
The following are available online at https://www.mdpi.com/2223-7747/9/9/1163/s1, Table S1: Linkage map construction of the RIL population derived from a crossing between 'Daewonkong' and 'Tawonkong'; Table S2: Linkage map construction of the RIL population derived from a crossing between 'Daewonkong' and 'Saeolkong'; Table S3: Primer information of the candidate genes used for qRT-PCR; Table S4: The description of candidate genes; Figure S1: The dried pods of the tolerant parent (Daewonkong) and two susceptible parents (Tawonkong and Saeolkong) drying after 72 h.
Author Contributions
Conceptualization, J.-H.S., H.-S.K., C.-S.J., and T.-H.J.; Methodology, J.-H.S.; Investigation, J.-H.O. and J.-H.P.; Resources, B.-K.K. and M.-S.C.; Data Curation, I.-Y.B., S.-O.S., and J.-S.S.; Writing-Original Draft Preparation, J.-H.S. and T.-H.J.; Writing-Review & Editing, S.K.D., K.-S.K., J.-H.S., and T.-H.J.; Funding Acquisition, J.-H.S. All authors have read and agreed to the published version of the manuscript.
Funding
This research was supported by the National Institute of Crop Science (NICS), Rural Development Administration (RDA) of Republic of Korea (No. PJ01186803).
Conflicts of Interest
The authors declare no conflict of interest.
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Jeong-Hyun Seo1, Beom-Kyu Kang1, Sanjeev K. Dhungana1, Jae-Hyeon Oh2, Man-Soo Choi1, Ji-Hee Park1, Sang-Ouk Shin1, Hong-Sik Kim1, In-Youl Baek1, Jung-Sook Sung1, Chan-Sik Jung1, Ki-Seung Kim3 and Tae-Hwan Jun4,5,*
1National Institute of Crop Science, Rural Development Administration, Jeonju 55365, Korea
2National Institute of Agricultural Sciences, Rural Development Administration, Jeonju 55365, Korea
3FarmHannong, Ltd., Daejeon 34115, Korea
4Department of Plant Bioscience, Pusan National University, Miryang 50463, Korea
5Life and Industry Convergence Research Institute, Pusan National University, Miryang 50463, Korea
*Author to whom correspondence should be addressed.
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; Oh, Jae-Hyeon
; Man-Soo, Choi; Ji-Hee, Park; Sang-Ouk Shin; Hong-Sik, Kim; Baek, In-Youl; Jung-Sook, Sung; Chan-Sik, Jung; Ki-Seung, Kim; Tae-Hwan, Jun