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Genetic Diversity Patterns and Discrimination of 172 Korean Soybean (Glycine max (L.) Merrill) Varieties Based on SSR Analysis

Genetic Diversity Patterns and Discrimination of 172 Korean Soybean (Glycine max (L.) Merrill)... agriculture Article Genetic Diversity Patterns and Discrimination of 172 Korean Soybean (Glycine max (L.) Merrill) Varieties Based on SSR Analysis 1 2 3 , ,y 3 , ,y Tae-Young Hwang , Byeong Sam Gwak , Jwakyung Sung * and Hong-Sig Kim * International Technology Cooperation Center, Rural Development Administration, Jeonju, Jeollabuk-do 54875, Korea; soybeanhwang@korea.kr Gyeongsangbuk-do Silkworm & Insect Management Center, Sangju 37110, Korea; 95042040@daum.net Department of Crop Science, College of Agriculture, Life Science and Environmental Chemistry, Chungbuk National University, Cheongju, Chungcheongbuk-do 28644, Korea * Correspondence: jksung73@chungbuk.ac.kr (J.S.); hongsigk@chungbuk.ac.kr (H.-S.K.); Tel.: +82-43-261-2512 (J.S.); +82-43-261-2513 (H.-S.K.) y These authors contributed equally to this article. Received: 10 February 2020; Accepted: 11 March 2020; Published: 13 March 2020 Abstract: The soybean development goal in Korea has changed over time, but the pattern of genetic diversity in modern varieties has not yet been well characterized. In this study, 20 simple sequence repeat (SSR) markers are shown to generate a total of 344 alleles, where the number of alleles ranges from 7 to 29, with an average of 17.2 per locus, and the polymorphism informative content (PIC) values range from 0.6799 to 0.9318, with an average of 0.8675. Five di erent clusters are classified using the unweighted pair group mean arithmetic (UPGMA) method. The genetic distance between clusters I and V (0.3382) is the farthest, and that between clusters III and IV (0.0819) is the closest. The genetic distance between all pairings of groups, according to the time period of their release, is lowest (0.1909) between varieties developed in the 1990s and those from 2000 onward, and highest (0.5731) between varieties developed in the 1980s and those from 2000 onward. Model-based structure analysis revealed the presence of three sub-populations and 17 admixtures in the Korean soybean varieties. All 172 Korean soybean varieties were tested for discrimination using six SSR markers. The numbers of varieties that were clustered in each step are as follows: 7 (4.1%) in step 1 (Sat_076), 73 (42.4%) in step 2 (Sat_417), 69 (40.1%) in step 3 (Sat_043), 13 (7.6%) in step 4 (Satt197), 8 (4.6%) in step 5 (Satt434), and 2 (1.2%) in step 6 (Satt179). These results, based on the analysis of genetic resources, can contribute to the creation of a core collection for soybean conservation and breeding, as well as to the development of future varieties with useful traits. Keywords: soybean; genetic diversity; discrimination; population structure; simple sequence repeat (SSR); period of release 1. Introduction Soybean (Glycine max (L.) Merrill) has historically been an important source of protein and oil for Korean people and is widely grown in East Asia, including in Northeast China [1]. The early cultivation of soybean in Korea is estimated to have begun around the fourth to fifth century BCE, and, since the era of the Three Kingdoms, it has become common in the Korean diet. In Korea, the goal of the development of soybean varieties was to increase yield up to the 1960s and 1970s. In the 1980s, mechanization-adaptive varieties were developed, in response to the declining labor force in rural areas. In the 1990s, soybean varieties were developed with the goal of breeding for diversification of use, multiplicity, and high quality, to allow for di erentiation from imported varieties. In recent Agriculture 2020, 10, 77; doi:10.3390/agriculture10030077 www.mdpi.com/journal/agriculture Agriculture 2020, 10, 77 2 of 15 years, the target when breeding soybean varieties has shifted to developing soybean for various uses; for example, as high-quality functional food or eco-friendly, safe, and insect- and disease-resistant varieties for stable production and to secure variety in genetic material for future needs [2,3]. The genetic diversity of crops or genetic resources used in breeding, as well as their inter-relationships, are crucial for increasing genetic variation, eciently breeding, hybridizing elite breeding materials with close genetic distances [4], and increasing genetic resistance to insect pests or abiotic stresses [5]. Discrimination of variety is important for distinguishing between domestic and foreign agricultural products. Korea ratified the International Union for the Protection of New Varieties of Plants (UPOV) in 1991, as well as e ectuating the Seed Industry Act in 1997 and the Nagoya Protocol in 2012, covering legislation on biodiversity conservation and utilization. For the rapid and accurate discrimination of varieties, techniques are required to protect breeder ’s rights. However, in any crop, old varieties are replaced by new varieties and, so, the discrimination of varieties is challenging. Discrimination of variety is complicated by agronomic characteristics, the natural environment, and social factors. Therefore, the discrimination of varieties is important for distinguishing between varieties, as well as to maintain the purity of the varieties. Bang et al. [6] reported that simple sequence repeat (SSR) markers can be used to test the distinction, uniformity, and stability of varieties for consistent identification and characterization of collected varieties, according to their origin and purity of seeds, allowing for the protection of varieties and the establishment of a seed management system. In recent years, the use of molecular markers in varietal development has increased, where it is applied for the evaluation of genetic diversity and variation. Collecting various genetic resources and selecting excellent traits through botanical classification, external morphological characteristics, and genetic evaluation is important [7]. Many scientists have previously used morphological characteristics to analyze the genetic diversity and relationships between crop varieties and genetic resources; however, molecular markers are the most popular tool in genetic analysis at present. Molecular markers, including restriction fragment length polymorphisms (RFLP), amplified fragment length polymorphisms (AFLP), randomly amplified polymorphic DNA (RAPD), SSR, and single nucleotide polymorphism (SNP) markers have been developed and are actively used in genetic diversity analysis, discrimination of varieties, and the detection and cloning of useful genes. As these molecular markers are not a ected by the environment nor numerically limited, they can be e ectively applied in genetic resource management and for the analysis of diversity in quantitative traits [8–11]. The identification of genetic structure using molecular markers, as well as morphological characteristics, is necessary for the ecient use of genetic resources. SSR markers are widely distributed across the genome, and their use as markers is based on di erences in repetition of simple motifs; typically two or three nucleotide bases (e.g., AT ) [11,12]. SSR markers are two or three simple nucleotide sequences (microsatellites) which are widely distributed throughout the genome and their use as markers is based on di erences in repetition. The value of polymorphism informative content (PIC) is considered high if a utility value is produced with many alleles in each locus, representing an ideal situation when used for identifying genetic diversity [13]. The aim of this study is to analyze the genetic diversity and inter-relationship, according to the release year of developed soybean varieties in Korea using SSR markers, in order for this information to be used to discriminate varieties and as basic data for future breeding activities. 2. Materials and Methods 2.1. Plant Material A total of 172 varieties of soybean—which are used for sauce and paste (81 varieties), as bean sprouts (52 varieties), for cooking with rice (24 varieties), and as vegetables and early maturation (15 varieties)—have been developed in Korea from 1913 to 2013 (Table 1 and Table S1). Agriculture 2020, 10, 77 3 of 15 Table 1. List of 172 Korean soybean varieties, classified by release years and use type. Use Type Release Period Total Vegetable and Soy Sauce and Tofu Bean Sprout Cooking with Rice Early Maturity Jangdanbaekmok, Chungbukbaek, Iksan, Buseok, Haman, Keumkangdaelip, Geumgangsorip, Yukwoo 3, Before 1980 Hill, Danyeob 18 Kwangdu, Shelby, Kwangkyo, Bongeui, Kanglim, Dongpuktae, Baegcheon, Jangyeob Hwangkeum, Jangbeak, Namcheon, Deokyu, Milyang, Baegun, Saeal, Paldal, 1980s Pangsa, Eunha, Namhae 17 Pokwang, Dankyeong, Muhan, Jangkyong, Jangsu, Danweon Malli, Shinpaldal, Samnam, Bukwang, Hannam, Kwangan, Sinpaldal2, Taekwang, Duyou, Geomjeong1, Hwaeomput, Pureun, Sobaegnamul, Danbaek, Soyang, Jinpum, Geomjeong2, Hwasongput, Myeongjunamul, Iksannamul, 1990s Geumgang, Dajang, Alchan, Ilpumgeomjeong, Geomjeongol, 43 Pungsannamul, Tawon, Jinpum2, Jangmi, Daewon, Heugcheong, Jinyul, Saeol, Somyeong, Paldo, Sowon, Ilmi, Sodam, Songhag, Galmi Seonheuk Doremi Daehwang Jangwon, Jinmi, Daepung, Cheongja, Geomjeong3, Saebyeol, Soho, Sorog, Daol, Hojang, Daemang, Geumjeong4, Anpyeong, Dachae, Seonam, Seonnog, Sunyu, Daemang2, Hoban, Cheongdu1, Cheongja2, Dagi, Shingi, Sojin, Bosuk, Shillok, Danmi, Socheong, Nokwon, Jonam, Cheongja3, Sokang, Nogchae, Aga1, Aga2, Dajin, Cheonga, Daewang, Gangil, Gyeongsang2, Wonhwang, Jangki, Geomjeongsaeol, After 1999 Nampung, Manpoong, Ilpumgeomjeong2, Youngyang, Soyoung, Mansu, Mirang, 94 (2000–2013) Daeha1, Daeha, Gyeongsang3, Gyeongsang1, Pungwon, Pulmu-heukchae, Danmi2, Wonyul, Soheug, Haessal, Josaengseori, Gaechuk1, Daeheug, Aga3, Aga4, Sangwon, Cheonsang, Uram, Gaechuk2, Heugmi, Wonkwang, Hoseo, Shinhwa, Hanol, Saedanbaek, Yeonpung, Daeyang, Heugseong, Galchae, Sohwang, Pulmujigi, Cheongyeob1 Jinyang, Wonhyun, Neulchan, Jinnong, Geomjeong5, Wonheug, Aga8, Aga9, Aga10 Jang Yon, Gipung Socheong2 Total 81 52 24 15 172 2.2. DNA Extraction and SSR Analysis Genomic DNA was extracted using the CTAB method [14]. The extracted DNA was used for PCR analysis by first checking the DNA concentration using a spectrophotometer (Nanodrop ND-1000, Thermo Scientific, Waltham, MA, USA) and adjusting the concentration to 20 ng/L. A total of 20 markers were used for the genetic diversity analysis (Table 2), selected from previous studies as having one primer with high polymorphism in each linkage group [9,15]. PCR was performed in a reaction mixture of 20 L containing 20 ng of total genomic DNA, 0.4 M of each primer, 10 mM dNTP mixture, 10 PCR bu er, and 1 unit Taq polymerase (Bioneer, Daejeon Korea) using a MG96G (Longgene scientific, Hangzhou, China). The amplification protocol included initial denaturation for 5 min at 94 C; 35 cycles of denaturation for 30 s at 94 C, annealing for 30 s at 47–57 C, and extension for 60 s at 72 C; and a final extension step for 10 min at 72 C. Amplification products were resolved using electrophoresis though 4% polyacrylamide sequencing gel at 1600 V for 100 to 120 min in 0.5  Tris-borate-ethylenediaminetetraacetic acid (TBE, Skælskør, Denmark) bu er. The gel was stained with silver staining [16]. Agriculture 2020, 10, 77 4 of 15 Table 2. Information of the 20 markers used for simple sequence repeat (SSR) analysis in this study. 0 0 Marker Linkage Group Chromosome No. Repeat Motif AT ( C) Sequence (5 to 3 ) F: GCGTTGAAACCGTTATAAACCAACTCA (AT) Sat_374 A1 05 57 R: GCGCTTTATTGGCAATACTTTTAACTCACAT F: GCGCCTCCCTCCTCTCCTTTCTT (AT) AW132402 A2 08 54 R: GCGTTTCCCACATATTCTATCATTTGTT F: CACTGCTTTTTCCCCTCTCT (ATT) Satt197 B1 11 49 R: AAGATACCCCCAACATTATTTGTAA F: GCGACTCTGGGGAAAATTAGTTTAG (AT) Sat_342 B2 14 53 R: GCGGAGTCGGGGAGCACTACTTGTC F: CACCAATGGCTAAAGGTACATAT (ATT) Satt164 C1 04 47 R: AGGAGAAGAAAAAATCACATAAAATATC F: GCGTAATTAACACCAATATATGACATG (AT) Sat_076 C2 06 47 R: GCGGGGTTAAAAATTCAAAATGT F: GGGATTAGGTTTATGGAAGTTTATTAT (ATT) Satt179 D1a 01 48 R: GGGTCATTAAAACGATCAGTAAGA F: TACCCTTAATCACCGGACAA (ATT) Satt216 D1b 02 47 R: AGGGAACTAACACATTTAATCATCA F: GCGGCCTTTTCTGACTGTTAA (AT) Sat_022 D2 17 47 R: GCGCAGTGACTAAAACTTACTAT F: GCGCATATGAATAGGTAAGTTGCACTAA (ATT) Satt185 E 15 49 R: GCGTTTTCCTACAATAATATTTCAT F: GCGAATATGGCGTTGAAAATAGTGAT (AT) Sat_417 F 13 54 R: GCGACCCAGATTCTGTGCTAAGA Agriculture 2020, 10, 77 5 of 15 Table 2. Cont. 0 0 Marker Linkage Group Chromosome No. Repeat Motif AT ( C) Sequence (5 to 3 ) F: GCGCCAGCAACAAAGTTCCTGACAAA (AT) Sat_210 G 18 52 R: GCGCATGCAAATGAAATAATAA F: GCGTTCCGATATACTATATAATCCTAAT (ATT) Satt434 H 12 48 R: GCGGGGTTAGTCTTTTTATTTAACTTAA F: TTCCATACAAGATATCAAGTGAATTG (AT) Sat_105 I 20 47 R: GCTCCCCTACATTGGTAGTAAA F: GCTGCATCAGATCACCCATCCTTC (AT) Sat_151 J 16 50 R: CATGCCATGTTGTATGTATGT F: GCGGTCCGTCAATGAATATTAAATTAAAA (AT) Sat_043 K 09 53 R: GCGAAAGCGGCAGAGAGAGAAAGGT F: GCGAGCCTACTTTTACTAGAACGTCAACAAG (AT) Sat_245 L 19 55 R: GCGAAAAATTCAACTCCCCTTTAATAGATTC F: GCGTAGGCATCGGTCAATATTTT (AT) Sat_391 M 07 52 R: GCGTTAGCGAGTGGATCAAGATCA F: TAATATGCTTTAAGTGGTGTGGTTATG (ATT) Satt339 N 03 49 R: GTTAAGCAGTTCCTCTCATCACG F: GCGTGGAAAATATTTTAAAGATTAGGA (AT) Sat_190 O 10 51 R: GCGTGTGTACATTTAGTCTACTTGAGAA annealing temperature. Agriculture 2020, 10, 77 6 of 15 2.3. Selection of SSR Markers for Discrimination of the Varieties The 172 varieties of Korean soybean were discriminated in six steps using six markers, including Sat_076, Sat_417, Sat_043, Satt197, and Satt434 with high polymorphism, and one marker, Satt179, with the lowest polymorphism. The discrimination of soybean varieties was determined by combining the markers with the highest polymorphism in the first step and the marker with the next highest polymorphism in each step. Four markers (Sat_076, Sat_417, Sat_043, and Satt197), which were highly diverse among the 20 markers [9], were initially selected. Additional markers (Satt434 and Satt179) were selected to eciently identify the unknown varieties. 2.4. Data Analysis The number of alleles, major allele frequency, genetic diversity, and PIC values were analyzed using PowerMarker 3.25 software [17]. Genetic distances were obtained using PowerMarker, according to the method of Nei [18], and the phylogenetic tree was prepared using the unweighted pair group mean arithmetic (UPGMA) method to compare group classification. The software program Structures 2.3.3 [19,20] was utilized to detect possible sub-populations (K = 1 to K = 10) with a model allowing for admixture and correlated allele frequencies using a burn-in of 100,000 and run length of 100,000, followed by 5 iterations. The optimal number of populations corresponds to the highest peak in the DK graph [21], and the Korean soybean varieties with membership probabilities (more than 90%) were assigned to each sub-population. 3. Results 3.1. Genetic Diversity and Polymorphism of SSR Loci Table 3 shows the results of genetic diversity analysis using the 20 SSR markers for the 172 soybean varieties developed in Korea. A total of 344 alleles were detected. The number of alleles ranged from 7 (Satt164 and Satt179) to 29 (Sat_076) per locus, with an average of 17.2. The size of alleles ranged from 107 to 361 bp, with Sat_417 and Sat_190 at the widest range of 139–229 bp and 125–215 bp, respectively; whereas Sat_164 had the narrowest range of 222–246 bp (Table S2). PIC values ranged from 0.6799 to 0.9318, with Sat_076 having the highest (0.9318) and Satt179 the lowest (0.6799). The average PIC value was 0.8675. The total varieties per cluster were as follows: 4 (2.3%) in cluster I, 52 (30.2%) in cluster II, 61 (35.5%) in cluster III, 46 (26.7%) in cluster IV, and 9 (5.2%) in cluster V. More than 92% of the total varieties were found in three clusters (II, III, and VI; Figure 1, Table S1 and Table 4). Calculation of genetic distance for pairwise combinations of all soybean varieties supported this conclusion (Table 5). Although the results were obtained with only a limited number of genotypes, clusters III and IV showed the lowest genetic distance (Table 5). Agriculture 2020, 10, 77 7 of 15 Table 3. Linkage group analysis of 20 simple sequence repeat (SSR) markers used for genotyping in 172 Korean soybean varieties. y z Marker Size (bp) N N PIC A A F Sat_374 273–343 21 0.1919 0.9060 AW132402 137–183 15 0.1977 0.8630 Agriculture 2020, 10, x FOR PEER REVIEW 4 of 18 Satt197 107–182 16 0.1395 0.9126 Sat_342 176–246 19 0.2035 0.9113 Dongpuktae, Jangkyong, Satt164 222–246 7 0.3256 0.7529 Baegcheon, Jangyeob Jangsu, Sat_076 122–208 29 0.1337 0.9318 Danweon, Satt179 130–184 7 0.4244 0.6799 Gemjeong1, Satt216 164–248 15 0.3430 0.7850 Soho, Jinmi, Sokang, 9 V Geomjeong2, Tawon, Sat_022 203–255 18 0.1919 0.9114 Nogchae, Cheonsang (5.2%) Daewon Satt185 210–270 16 0.1919 0.8819 Sat_417 139–229 22 0.1105 0.9267 172 Total 18 17 43 94 Sat_210 217–287 18 0.2151 0.8741 (100%) Satt434 301–361 18 0.2326 0.8875 Note: Value in brackets correspond to the percentage distribution of varieties in each cluster classified Sat_105 228–274 18 0.1628 0.9067 according to release year. Sat_151 210–272 17 0.2849 0.8241 Sat_043 242–294 18 0.1453 0.9167 Table 5. Genetic distance between all pairings of clusters based on SSR analysis. Sat_245 119–207 19 0.1395 0.9107 Sat_391 214–282 16 0.1628 0.8903 Cluster I II III IV V Satt339 203–245 14 0.2849 0.8041 I - Sat_190 125–215 21 0.2616 0.8737 II 0.2412 - Total 344 MeanIII 0.2451 0.0873 17.2 - 0.2172 0.8675 y z § IV number of alleles;0.2482 0 major allele frequency;.0915 0 polymorphic.0819 information content.- V 0.3382 0.1659 0.1459 0.1473 - Figure Figure 1. 1. Unweighted Unweighted pair pair group groumean p mean arithmetic (U arithmetic (UPGMA) PGMA) p phylogenetic hylogenetic tretr e u ee sing 20 SS using 20 R markers SSR markers in 172in 172 Korean Korean soybean soybean varietie varieties. s. 3.2. Polymorphism of SSR Loci by Time Period of Release Years and Population Structure Agriculture 2020, 10, x; doi: FOR PEER REVIEW www.mdpi.com/journal/agriculture Agriculture 2020, 10, 77 8 of 15 Table 4. List of varieties in each cluster classified by release years using SSR markers in 172 Korean soybean varieties. Release Period Cluster Total Before 1980 1980s 1990s 2000 or Later Jinpum, Jinpum2, I Gyeongsang1 4 (2.3%) Heugcheong Saebyeol, Sorog, Anpyeong, Daepung, Dachae, Seonam, Dagi, Hojang, Shingi, Sojin, Bukwang, Danbaek, Bosuk, Aga1, Aga2, Kwangan, Wonhwang, Jangki, Myeongjunamul, Deokyu, Baegun, Youngyang, Soyoung, II Iksan, Shelby, Danyeob, Iksannamul, Alchan, 52 (30.2%) Pangsa, Eunha, Pungwon, Jonam, Pungsannamul, Jangmi, Pulmu-heukchae, Aga3, Aga4, Somyeong, Sodam, Wonkwang, Hoseo, Shinhwa, Sowon, Doremi, Galchae, Sohwang, Pulmujigi, Wonheug, Socheong2, Aga8, Aga9, Aga10 Jangwon, Cheongja, Seonnog, Shillok, Geomjeong3, Geumjeong4, Daol, Danmi, Cheongdu1, Daemang, Dajin, Cheongja2, Cheongja3, Sunyu, Sinpaldal2, Duyou, Geomjeongsaeol, Hwasongput, Hannam, Gyeongsang2, Daemang2, Pureun, Geumgang, Mirang, Danmi2, Sobaegnamul, Dajang, Ilpumgeomjeong2, Gemjeongol, III Hwangkeum Josaengseori, Gaechuk1, 61 (35.5%) Ilpumgeomjeong, Gaechuk2, Heugmi, Nokwon, Ilmikong, Saeol, Mansu, Cheonga, Gangil, Seonheuk, Songhag, Nampung, Sangwon, Jinyul, Daehwang, Daeheug, Daeyang, Galmi Manpoong, Heugseong, Daeha1, Gyeongsang3, Jinnong, Gemjeong5, Cheongyeob1, Wonyul, Haessal, Yeonpung, Jinyang Jangdanbaekmok, Jangbeak, Chungbukbaek, Buseok, Namcheon, Milyang, Haman, Keumkangdaelip, Malli, Shinpaldal, Hoban, Socheong, Daewang, Saeal, Paldal, GeumgangSorip, Yukwoo3, Samnam, Taekwang, Daeha, Hanol, Soheug, Uram, IV Pokwang, 46 (26.7%) Kwangdu, Hill, Kwangkyo, Hwaeomput, Soyang, Saedanbaek, Wonhyun, Dankyeong, Muhan, Bongeui, Kanglim, Paldo, Neulchan, JangYon, Gipung Namhae, Jangkyong, Dongpuktae, Baegcheon, Jangsu, Danweon, Jangyeob Gemjeong1, Soho, Jinmi, Sokang, Nogchae, V Geomjeong2, Tawon, 9 (5.2%) Cheonsang Daewon Total 18 17 43 94 (100%) Note: Value in brackets correspond to the percentage distribution of varieties in each cluster classified according to release year. Table 5. Genetic distance between all pairings of clusters based on SSR analysis. Cluster I II III IV V I - II 0.2412 - III 0.2451 0.0873 - IV 0.2482 0.0915 0.0819 - V 0.3382 0.1659 0.1459 0.1473 - 3.2. Polymorphism of SSR Loci by Time Period of Release Years and Population Structure The number of alleles and PIC values obtained as a result of our SSR analysis of the 172 Korean soybean varieties of each group, as classified by release year, are shown in Table 6. A total of 185 alleles in Korean soybean varieties were developed before 1980, where the average number of alleles was 9.3 and the average PIC value was 0.8188. In the 1980s, the total number of alleles was 167, the average number of alleles was 8.4, and the average PIC value was 0.7913. A total of 251 alleles in Agriculture 2020, 10, 77 9 of 15 Korean soybean varieties were developed in the 1990s, where the average number of alleles was 12.6 and the average PIC value was 0.8416. Since 2000, the allele total was 315, the average number of alleles was 15.8, and PIC value was 0.8631. The soybean varieties developed after the 1990s and 2000s had a higher number of alleles and higher PIC values than those developed before 1980. Due to the sample size di erence, the varieties developed after the 1990s and 2000s had more alleles than the varieties developed before 1980. Table 6. Information of 20 markers used for SSR analysis by release period. Release Period Before 1980 1980s 1990s 2000 or later Marker y z N . PIC N . PIC N . PIC N . PIC A A A Sat_374 9 0.8143 9 0.8198 17 0.9006 20 0.8999 AW132402 8 0.7941 7 0.7928 10 0.8383 13 0.8604 Satt197 10 0.8431 7 0.7713 13 0.8890 15 0.9121 Sat_342 10 0.8431 10 0.8288 15 0.8724 19 0.9028 Satt164 4 0.6658 6 0.7817 7 0.7810 7 0.7188 Sat_076 13 0.9001 13 0.8846 19 0.9165 24 0.9285 Satt179 5 0.6987 6 0.5299 5 0.5851 6 0.7093 Satt216 8 0.6886 6 0.5769 10 0.8074 11 0.7779 Sat_022 12 0.8795 9 0.8032 15 0.8927 18 0.9116 Satt185 12 0.8929 9 0.8427 11 0.8395 15 0.8727 Sat_417 8 0.8197 11 0.8752 17 0.9127 20 0.9252 Sat_210 10 0.8645 9 0.8427 12 0.7753 16 0.8778 Satt434 9 0.8024 9 0.8343 13 0.8682 17 0.8904 Sat_105 9 0.8422 8 0.8339 12 0.8724 17 0.9074 Sat_151 7 0.7900 7 0.7157 10 0.7695 16 0.8350 Sat_043 10 0.8640 11 0.8830 15 0.9003 17 0.8982 Sat_245 12 0.8724 6 0.7907 14 0.8989 18 0.9052 Sat_391 11 0.8649 9 0.8266 12 0.8470 15 0.8889 Satt339 8 0.7704 5 0.7557 11 0.8034 12 0.8001 Sat_190 10 0.8645 10 0.8372 13 0.8612 19 0.8399 Total 185 167 251 315 Mean 9.25 0.8188 8.35 0.7913 12.55 0.8416 15.75 0.8631 y z number of alleles; polymorphic information content. The varieties in each cluster, as classified by release period, are shown in Table 4. We found that 18 varieties developed before 1980 were distributed in clusters II and IV, with the most varieties (15) occurring in cluster IV. The 17 varieties developed in the 1980s were in clusters II, III, and IV, with most of these (12) in cluster IV. The 43 varieties developed in the 1990s were distributed throughout all clusters, with 17 varieties found in cluster IV. The varieties developed since the 2000s were distributed in all clusters, but mostly in clusters II and III (80%). The genetic distances between all pairings of groups by release period are shown in Table 7. The genetic distance between varieties developed after 2000 and those developed in the 1980s was the furthest (0.5731), while the genetic distance between varieties developed after 2000 and those developed in the 1990s was the closest (0.1909; Table 7). Table 7. Genetic distance between all pairings of group by release period based on SSR analysis. Release Period Before 1980 1980s 1990s 2000 or Later Before 1980 - 1980s 0.3382 - 1990s 0.4906 0.4444 - 2000 or later 0.5587 0.5731 0.1909 - Agriculture 2020, 10, 77 10 of 15 The population structure of the 172 Korean soybean varieties was inferred using the Structure V2.3.3 software, based on 20 SSR markers. For each K value (K = 1–10), we determined the optimal value of K by calculating DK. As we used Korean soybean varieties in this study, we surveyed the highest DK value over “K = 3”. The three sub-populations, referred to as Pop. 1–3, were observed with the highest DK value (Figure 2) and 17 varieties were admixture type, with a membership probability < 90% (Figure 3). Most soybean varieties were distinguished according to release period. The result of population structure analysis is shown in Table 8. We found that only 18 varieties developed before or in the 1980s were in Pop. 3, except Iksan and Hwangkeumkong. The varieties developed since the 2000s were Agriculture distributed 2020, 10, x FOR PEER in all clusters, REVIEWbut mostly in Pops. 1 and 2 (79%). 7 of 18 Agriculture 2020, 10, x FOR PEER REVIEW 7 of 18 Figure 2. Values of ΔK, with its modal value detecting a true K of three groups (K = 3). Figure 2. Figure 2. ValuesValues of of DK, with ΔK, wi its th it modal s modalvalue value detect detecting ing a true K of a true three gr K of thr oups ( ee gr Koups = 3). (K = 3). Figure 3. Model-based membership of 172 Korean soybean varieties using STRUCTURE. Colors Figure 3. Model-based membership of 172 Korean soybean varieties using STRUCTURE. Colors Figure 3. Model-based membership of 172 Korean soybean varieties using STRUCTURE. Colors represent model-based represent model-base sub-populations d sub-populations as as follows: follows: re red, d, Po Pop. p. 1; lime, 1; lime, Pop. Pop. 2; and blue, Po 2; and blue, p. 3. Pop. 3. represent model-based sub-populations as follows: red, Pop. 1; lime, Pop. 2; and blue, Pop. 3. Agriculture 2020, 10, x; doi: FOR PEER REVIEW www.mdpi.com/journal/agriculture Agriculture 2020, 10, x; doi: FOR PEER REVIEW www.mdpi.com/journal/agriculture Agriculture 2020, 10, 77 11 of 15 Table 8. Each step of discrimination of 172 Korean soybean varieties using six SSR markers. Percentage of No. of Identified Step Marker Identified Varieties Identified Varieties Varieties Gaechuk2, Geumjeong4, Heugcheong, Jinnong, Step 1 Sat_076 7 4.1% Manpoong, Seonam, Sobaegnamul Bongeui, Galmi, Geumgangsorip, Jangkyong, Somyeong, Heugseong, Jangyon, Jangsu, Pureun, Baegcheon, Daemang2, Dankyeong, Deokyu, Haessal, Taekwang, Ilpumgeomjung, Jangdanbaekmok, Wonkwang, Saedanbaek, Tawon, Wonheug, Dajang, Geomjeongol, Cheongja2, Cheongyeob1, Daehwang, Galchae, Paldo, Pokwang, Shinpaldal, Jinyang, Milyang, Wonhwang, Aga2, Step 2 Step 1 + Sat_417 73 42.4% Samnam, Sohwang, Sorog, Pulmujigi, Shingi, Haman, Hwaeomput, Heugmi, Jangyeob, Malli, Jangwon, Dongpuktae, Jonam, Saeal, Soyoung, Bukwang, Daepung, Pungsannamul, Saebyeol, Jangmi, Kwangan, Muhan, Sangwon, Shinhwa, Youngyang, Hoban, Gyeongsang1, Jangbeak, Shelby, Wonhyun, Josaengseori, Pangsa, Doremi, Hanol, Danyeob, Hojang, Hoseo, Namcheon, Soho Duyou, Geumgang, Cheongja, Geomjeong1, Daewon, Shillok, Ilpumgeomjeong2, Sunyu, Cheonga, Cheongja3, Sowon, Sodam, Alchan, Dachae, Iksannamul, Socheong2, Dagi, Songhag, Danbaek, Anpyeong, Eunha, Baegun, Gyeongsang2, Gyeongsang3, Gaechuk1, Aga4, Aga9, Jangki, Paldal, Danmi2, Geomjeongsaeol, Hill, Chungbukbaek, Step 3 Step 2 + Sat_043 Danweon, Jinpum, Jinpum2, Cheongdu1, Daemang, 69 40.1% Daeyang, Geomjeong3, Buseok, Kwangdu, Nokwon, Seonnog, Daol, Jinyul, Gangil, Dajin, Pulmu-heukchae, Saeol, Geomjeong2, Mirang, Daeheug, Myeongjunamul, Danmi, Hannam, Soyang, Ilmi, Yukwoo 3, Kanglim, Kwangkyo, Jinmi, IksanCheonsang, Hwangkeum, Keumkangdaelip, Neulchan, Soheug, Wonyul Gipung, Uram, Daewang, Socheong, Hwasongput, Step 4 Step 3 + Satt197 Yeonpung, Aga10, Sojin, Aga1, Namhae, Seonheuk, 13 9.6% Geomjeong5, Sinpaldal2 Nogchae, Sokang, Aga3, Aga8, Pungwon, Bosuk, Step 5 Step 4 + Satt434 8 4.6% Daeha, Daeha1 Step 6 Step 5 + Satt179 Mansu, Nampung 2 1.2% Total 172 100% 3.3. Discrimination of Soybean Varieties In the first step, seven (4.1%) varieties were discriminated according to the results when using Sat_076, which had the highest allele and PIC values, and were not discriminated from 165 varieties. In step 2, 73 (42.4%) varieties were discriminated among the 165 varieties by including Sat_417. In step 3, 69 (40.1%) varieties were discriminated among 92 varieties by including Sat_043. In step 4, 13 (7.6%) of 23 varieties were discriminated when including Satt197. In step 5, 8 (4.6%) of 10 varieties were discriminated when including Satt434. In the final step, two varieties (1.2%), Mansu and Nampung, were discriminated by including Sat_179, allowing for discrimination of all 172 varieties. Of the 20 markers, Mansu and Nampung could only be discriminated when using Sat_179 (Table 8, Figure S2). 4. Discussion Many genetic diversity analyses using DNA markers have been reported for the development of new varieties, discrimination of varieties, and to search for useful genes. Song et al. [22] reported the use of 72 markers to analyze 185 accessions of genetic resources collected from Korea, China, Japan, India, Myanmar, Philippines, and the United States, detecting 3–31 alleles per locus and an average of 10.9 alleles, which were used to classify the data into three groups, following which detailed groups were formed according to geographical origins. Kuroda et al. [23] used the genetic resources of 1318 Agriculture 2020, 10, 77 12 of 15 native varieties and wild-type plants from China, Japan, Korea, and Russia to analyze the genetic diversity and develop a core collection. A high number of rare alleles were found in the wild types of Korea, suggesting that the ratio of soybean core collection to selection was high. Korea has its own wild soybean types, but other types of resources have also been introduced from both China and Japan, resulting in accessions with high genetic diversity. Wang et al. [24] analyzed 23 developed soybean and native varieties and 17 wild types using 40 SSR markers, and detected that the high genetic diversity of wild types was due to the loss of many alleles in the evolutionary process. The genetic distance between all pairings of groups by release period shows that those in the 1990s and 2000 onward were the closest (0.1909), and 2000 onward and before or during the 1980s were the farthest (0.5587 and 0.5731, respectively; Table 7). The PIC value, in terms of release periods among Korean soybean varieties, was the highest in those developed from 2000 onward (Table 6). The reason for this finding is that, since the mid-1980s, various varieties have been developed with the aim of diversification of use and high quality. Many varieties have been developed for comparison to other groups by release period. We also found that the most genetic diversity among varieties can be observed in those developed since the 2000s (Figure 1 and Figure S1, Table 4). This trend is expected to continue in the future. Many results of genetic diversity analyses and discrimination of varietal studies using DNA markers have been reported. Kim et al. [25] discriminated 82 of the 91 developed soybean varieties from 1913 to 2002 using five SSR markers—Sat_043, Sat_022, Sat_036, Sat_088, and Satt045—and reported that unidentified varieties could not be distinguished using their morphological characteristics. Hwang et al. [26] reported that 18 sequence-tagged sites—cleaved amplification polymorphic sequence (STS-CAPS) markers (51 combinations)—were developed from base sequence information in prepared genomic DNA libraries, and 106 Korean soybean varieties were discriminated in 14 steps. Gao et al. [27] discriminated 83 soybean varieties using nine markers with high polymorphism and high diversity within the population. An allele of each marker was coded to form a nine-digit identification (ID) for the discrimination of soybean varieties. In Korea, the first developed soybean variety was Jangdanbaekmok produced by separating landrace through pure line isolation in 1913, and the first hybrid variety was developed in 1969 characterized with high yielding ability and resistant to mosaic virus [28]. Until 2014, a total of 178 soybean varieties have been developed and registered at the two national institutes, the RDA-Genebank Information Center and the Korea Seed & Variety Service [28,29]. Lee et al. [29] analysis that a total of four pedigrees involving 168 varieties (94.4% out of 178 varieties), which form the broadest network of pedigrees. We analyzed genetic diversity using 172 Korean soybean varieties developed until 2013. Of these, 162 varieties include in four pedigrees, the results of genetic diversity and pedigrees provide information for selection of parental lines and design of crossing strategies. The full-length sequence of the American soybean variety Willams 82 has been analyzed and published for the first time [30] and, using the sequence information, a large amount of information regarding candidate polymorphic SSR markers has also been published [31]. In Korea, Kim et al. [32] analyzed the full-length sequences of six Korean soybean varieties to distinguish genomes from dense variation blocks (dVBs) with SNP accumulation and sparse variation blocks with little SNPs. As dVBs have distinctive characteristics according to variety, various di erences were indeed found to exist among the varieties. Therefore, 202 dVB-specific insertion/deletion (indel) markers have been developed [33]. A barcode system was established, which clearly distinguishes 147 Korean soybean varieties and is regularly updated whenever new varieties are developed [34,35]. The development of DNA markers has been simplified due to the active progression of genome research, but the development of DNA markers through full-length genome analysis in a small-scale laboratory is still dicult. As the marker analysis was not performed directly, polymorphism information is not known in full-length genome analysis. In this study, we analyzed 172 Korean soybean varieties developed from 1913 to 2013 using SSR markers that have been previously confirmed to be highly polymorphic. The analysis of genetic resources, including newly developed soybean varieties after 2014, in addition Agriculture 2020, 10, 77 13 of 15 to the data accumulated in this study, will contribute to the development of future varieties with useful traits, such as drought- and disease-resistance, including the determination of species and the creation of a core collection for soybean conservation and breeding. 5. Conclusions In this study, the genetic diversity of 172 Korean soybean varieties was analyzed and discriminated using SSR Markers. Five di erent clusters were classified using the UPGMA method, among which the genetic distance between clusters I and V was the farthest, and that between clusters III and IV was the closest. The genetic diversity distance by release period between all pairings of groups was the lowest between varieties developed after 2000 and the 1990s, and the highest between varieties developed after 2000 and the 1980s. The results demonstrate that the greatest genetic diversity among varieties was found in those that had been developed after 2000. The 172 varieties of Korean soybean were discriminated in six steps using six markers (Sat_076, Sat_417, Sat_043, Satt197, Satt434, and Satt179). The analysis of genetic resources, including newly developed varieties, in addition to the data accumulated in this study, will contribute to the creation of a core collection for soybean conservation and breeding, as well as to the development of future varieties with useful traits. Supplementary Materials: The following are available online at http://www.mdpi.com/2077-0472/10/3/77/s1, Table S1: Number of soybean varieties classified by released year and breeding organization for this experiment, Table S2: Information of band size (bp) based on 20 markers for SSR analysis in 172 Korean soybean varieties, Figure S1: Principal coordinate analysis (PCoA) plot of 172 Korean soybean varieties using 20 SSR markers, Figure S2: Discrimination of varieties at each step by UPGMA dendrogram using six markers in 172 Korean soybean varieties. Author Contributions: Conceptualization, J.S. and H.-S.K.; Methodology, T.-Y.H. and B.S.G.; Formal analysis, T.-Y.H. and B.S.G.; Investigation, T.-Y.H., B.S.G., and J.S.; Resources, H.-S.K.; Data curation, T.-Y.H.; Writing—original draft preparation, T.-Y.H. and B.S.G.; Writing—review and editing, J.S. and H.-S.K. Visualization, T.-Y.H.; Supervision, J.S.; Project administration, H.-S.K.; Funding acquisition, H.-S.K. 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Genetic Diversity Patterns and Discrimination of 172 Korean Soybean (Glycine max (L.) Merrill) Varieties Based on SSR Analysis

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agriculture Article Genetic Diversity Patterns and Discrimination of 172 Korean Soybean (Glycine max (L.) Merrill) Varieties Based on SSR Analysis 1 2 3 , ,y 3 , ,y Tae-Young Hwang , Byeong Sam Gwak , Jwakyung Sung * and Hong-Sig Kim * International Technology Cooperation Center, Rural Development Administration, Jeonju, Jeollabuk-do 54875, Korea; soybeanhwang@korea.kr Gyeongsangbuk-do Silkworm & Insect Management Center, Sangju 37110, Korea; 95042040@daum.net Department of Crop Science, College of Agriculture, Life Science and Environmental Chemistry, Chungbuk National University, Cheongju, Chungcheongbuk-do 28644, Korea * Correspondence: jksung73@chungbuk.ac.kr (J.S.); hongsigk@chungbuk.ac.kr (H.-S.K.); Tel.: +82-43-261-2512 (J.S.); +82-43-261-2513 (H.-S.K.) y These authors contributed equally to this article. Received: 10 February 2020; Accepted: 11 March 2020; Published: 13 March 2020 Abstract: The soybean development goal in Korea has changed over time, but the pattern of genetic diversity in modern varieties has not yet been well characterized. In this study, 20 simple sequence repeat (SSR) markers are shown to generate a total of 344 alleles, where the number of alleles ranges from 7 to 29, with an average of 17.2 per locus, and the polymorphism informative content (PIC) values range from 0.6799 to 0.9318, with an average of 0.8675. Five di erent clusters are classified using the unweighted pair group mean arithmetic (UPGMA) method. The genetic distance between clusters I and V (0.3382) is the farthest, and that between clusters III and IV (0.0819) is the closest. The genetic distance between all pairings of groups, according to the time period of their release, is lowest (0.1909) between varieties developed in the 1990s and those from 2000 onward, and highest (0.5731) between varieties developed in the 1980s and those from 2000 onward. Model-based structure analysis revealed the presence of three sub-populations and 17 admixtures in the Korean soybean varieties. All 172 Korean soybean varieties were tested for discrimination using six SSR markers. The numbers of varieties that were clustered in each step are as follows: 7 (4.1%) in step 1 (Sat_076), 73 (42.4%) in step 2 (Sat_417), 69 (40.1%) in step 3 (Sat_043), 13 (7.6%) in step 4 (Satt197), 8 (4.6%) in step 5 (Satt434), and 2 (1.2%) in step 6 (Satt179). These results, based on the analysis of genetic resources, can contribute to the creation of a core collection for soybean conservation and breeding, as well as to the development of future varieties with useful traits. Keywords: soybean; genetic diversity; discrimination; population structure; simple sequence repeat (SSR); period of release 1. Introduction Soybean (Glycine max (L.) Merrill) has historically been an important source of protein and oil for Korean people and is widely grown in East Asia, including in Northeast China [1]. The early cultivation of soybean in Korea is estimated to have begun around the fourth to fifth century BCE, and, since the era of the Three Kingdoms, it has become common in the Korean diet. In Korea, the goal of the development of soybean varieties was to increase yield up to the 1960s and 1970s. In the 1980s, mechanization-adaptive varieties were developed, in response to the declining labor force in rural areas. In the 1990s, soybean varieties were developed with the goal of breeding for diversification of use, multiplicity, and high quality, to allow for di erentiation from imported varieties. In recent Agriculture 2020, 10, 77; doi:10.3390/agriculture10030077 www.mdpi.com/journal/agriculture Agriculture 2020, 10, 77 2 of 15 years, the target when breeding soybean varieties has shifted to developing soybean for various uses; for example, as high-quality functional food or eco-friendly, safe, and insect- and disease-resistant varieties for stable production and to secure variety in genetic material for future needs [2,3]. The genetic diversity of crops or genetic resources used in breeding, as well as their inter-relationships, are crucial for increasing genetic variation, eciently breeding, hybridizing elite breeding materials with close genetic distances [4], and increasing genetic resistance to insect pests or abiotic stresses [5]. Discrimination of variety is important for distinguishing between domestic and foreign agricultural products. Korea ratified the International Union for the Protection of New Varieties of Plants (UPOV) in 1991, as well as e ectuating the Seed Industry Act in 1997 and the Nagoya Protocol in 2012, covering legislation on biodiversity conservation and utilization. For the rapid and accurate discrimination of varieties, techniques are required to protect breeder ’s rights. However, in any crop, old varieties are replaced by new varieties and, so, the discrimination of varieties is challenging. Discrimination of variety is complicated by agronomic characteristics, the natural environment, and social factors. Therefore, the discrimination of varieties is important for distinguishing between varieties, as well as to maintain the purity of the varieties. Bang et al. [6] reported that simple sequence repeat (SSR) markers can be used to test the distinction, uniformity, and stability of varieties for consistent identification and characterization of collected varieties, according to their origin and purity of seeds, allowing for the protection of varieties and the establishment of a seed management system. In recent years, the use of molecular markers in varietal development has increased, where it is applied for the evaluation of genetic diversity and variation. Collecting various genetic resources and selecting excellent traits through botanical classification, external morphological characteristics, and genetic evaluation is important [7]. Many scientists have previously used morphological characteristics to analyze the genetic diversity and relationships between crop varieties and genetic resources; however, molecular markers are the most popular tool in genetic analysis at present. Molecular markers, including restriction fragment length polymorphisms (RFLP), amplified fragment length polymorphisms (AFLP), randomly amplified polymorphic DNA (RAPD), SSR, and single nucleotide polymorphism (SNP) markers have been developed and are actively used in genetic diversity analysis, discrimination of varieties, and the detection and cloning of useful genes. As these molecular markers are not a ected by the environment nor numerically limited, they can be e ectively applied in genetic resource management and for the analysis of diversity in quantitative traits [8–11]. The identification of genetic structure using molecular markers, as well as morphological characteristics, is necessary for the ecient use of genetic resources. SSR markers are widely distributed across the genome, and their use as markers is based on di erences in repetition of simple motifs; typically two or three nucleotide bases (e.g., AT ) [11,12]. SSR markers are two or three simple nucleotide sequences (microsatellites) which are widely distributed throughout the genome and their use as markers is based on di erences in repetition. The value of polymorphism informative content (PIC) is considered high if a utility value is produced with many alleles in each locus, representing an ideal situation when used for identifying genetic diversity [13]. The aim of this study is to analyze the genetic diversity and inter-relationship, according to the release year of developed soybean varieties in Korea using SSR markers, in order for this information to be used to discriminate varieties and as basic data for future breeding activities. 2. Materials and Methods 2.1. Plant Material A total of 172 varieties of soybean—which are used for sauce and paste (81 varieties), as bean sprouts (52 varieties), for cooking with rice (24 varieties), and as vegetables and early maturation (15 varieties)—have been developed in Korea from 1913 to 2013 (Table 1 and Table S1). Agriculture 2020, 10, 77 3 of 15 Table 1. List of 172 Korean soybean varieties, classified by release years and use type. Use Type Release Period Total Vegetable and Soy Sauce and Tofu Bean Sprout Cooking with Rice Early Maturity Jangdanbaekmok, Chungbukbaek, Iksan, Buseok, Haman, Keumkangdaelip, Geumgangsorip, Yukwoo 3, Before 1980 Hill, Danyeob 18 Kwangdu, Shelby, Kwangkyo, Bongeui, Kanglim, Dongpuktae, Baegcheon, Jangyeob Hwangkeum, Jangbeak, Namcheon, Deokyu, Milyang, Baegun, Saeal, Paldal, 1980s Pangsa, Eunha, Namhae 17 Pokwang, Dankyeong, Muhan, Jangkyong, Jangsu, Danweon Malli, Shinpaldal, Samnam, Bukwang, Hannam, Kwangan, Sinpaldal2, Taekwang, Duyou, Geomjeong1, Hwaeomput, Pureun, Sobaegnamul, Danbaek, Soyang, Jinpum, Geomjeong2, Hwasongput, Myeongjunamul, Iksannamul, 1990s Geumgang, Dajang, Alchan, Ilpumgeomjeong, Geomjeongol, 43 Pungsannamul, Tawon, Jinpum2, Jangmi, Daewon, Heugcheong, Jinyul, Saeol, Somyeong, Paldo, Sowon, Ilmi, Sodam, Songhag, Galmi Seonheuk Doremi Daehwang Jangwon, Jinmi, Daepung, Cheongja, Geomjeong3, Saebyeol, Soho, Sorog, Daol, Hojang, Daemang, Geumjeong4, Anpyeong, Dachae, Seonam, Seonnog, Sunyu, Daemang2, Hoban, Cheongdu1, Cheongja2, Dagi, Shingi, Sojin, Bosuk, Shillok, Danmi, Socheong, Nokwon, Jonam, Cheongja3, Sokang, Nogchae, Aga1, Aga2, Dajin, Cheonga, Daewang, Gangil, Gyeongsang2, Wonhwang, Jangki, Geomjeongsaeol, After 1999 Nampung, Manpoong, Ilpumgeomjeong2, Youngyang, Soyoung, Mansu, Mirang, 94 (2000–2013) Daeha1, Daeha, Gyeongsang3, Gyeongsang1, Pungwon, Pulmu-heukchae, Danmi2, Wonyul, Soheug, Haessal, Josaengseori, Gaechuk1, Daeheug, Aga3, Aga4, Sangwon, Cheonsang, Uram, Gaechuk2, Heugmi, Wonkwang, Hoseo, Shinhwa, Hanol, Saedanbaek, Yeonpung, Daeyang, Heugseong, Galchae, Sohwang, Pulmujigi, Cheongyeob1 Jinyang, Wonhyun, Neulchan, Jinnong, Geomjeong5, Wonheug, Aga8, Aga9, Aga10 Jang Yon, Gipung Socheong2 Total 81 52 24 15 172 2.2. DNA Extraction and SSR Analysis Genomic DNA was extracted using the CTAB method [14]. The extracted DNA was used for PCR analysis by first checking the DNA concentration using a spectrophotometer (Nanodrop ND-1000, Thermo Scientific, Waltham, MA, USA) and adjusting the concentration to 20 ng/L. A total of 20 markers were used for the genetic diversity analysis (Table 2), selected from previous studies as having one primer with high polymorphism in each linkage group [9,15]. PCR was performed in a reaction mixture of 20 L containing 20 ng of total genomic DNA, 0.4 M of each primer, 10 mM dNTP mixture, 10 PCR bu er, and 1 unit Taq polymerase (Bioneer, Daejeon Korea) using a MG96G (Longgene scientific, Hangzhou, China). The amplification protocol included initial denaturation for 5 min at 94 C; 35 cycles of denaturation for 30 s at 94 C, annealing for 30 s at 47–57 C, and extension for 60 s at 72 C; and a final extension step for 10 min at 72 C. Amplification products were resolved using electrophoresis though 4% polyacrylamide sequencing gel at 1600 V for 100 to 120 min in 0.5  Tris-borate-ethylenediaminetetraacetic acid (TBE, Skælskør, Denmark) bu er. The gel was stained with silver staining [16]. Agriculture 2020, 10, 77 4 of 15 Table 2. Information of the 20 markers used for simple sequence repeat (SSR) analysis in this study. 0 0 Marker Linkage Group Chromosome No. Repeat Motif AT ( C) Sequence (5 to 3 ) F: GCGTTGAAACCGTTATAAACCAACTCA (AT) Sat_374 A1 05 57 R: GCGCTTTATTGGCAATACTTTTAACTCACAT F: GCGCCTCCCTCCTCTCCTTTCTT (AT) AW132402 A2 08 54 R: GCGTTTCCCACATATTCTATCATTTGTT F: CACTGCTTTTTCCCCTCTCT (ATT) Satt197 B1 11 49 R: AAGATACCCCCAACATTATTTGTAA F: GCGACTCTGGGGAAAATTAGTTTAG (AT) Sat_342 B2 14 53 R: GCGGAGTCGGGGAGCACTACTTGTC F: CACCAATGGCTAAAGGTACATAT (ATT) Satt164 C1 04 47 R: AGGAGAAGAAAAAATCACATAAAATATC F: GCGTAATTAACACCAATATATGACATG (AT) Sat_076 C2 06 47 R: GCGGGGTTAAAAATTCAAAATGT F: GGGATTAGGTTTATGGAAGTTTATTAT (ATT) Satt179 D1a 01 48 R: GGGTCATTAAAACGATCAGTAAGA F: TACCCTTAATCACCGGACAA (ATT) Satt216 D1b 02 47 R: AGGGAACTAACACATTTAATCATCA F: GCGGCCTTTTCTGACTGTTAA (AT) Sat_022 D2 17 47 R: GCGCAGTGACTAAAACTTACTAT F: GCGCATATGAATAGGTAAGTTGCACTAA (ATT) Satt185 E 15 49 R: GCGTTTTCCTACAATAATATTTCAT F: GCGAATATGGCGTTGAAAATAGTGAT (AT) Sat_417 F 13 54 R: GCGACCCAGATTCTGTGCTAAGA Agriculture 2020, 10, 77 5 of 15 Table 2. Cont. 0 0 Marker Linkage Group Chromosome No. Repeat Motif AT ( C) Sequence (5 to 3 ) F: GCGCCAGCAACAAAGTTCCTGACAAA (AT) Sat_210 G 18 52 R: GCGCATGCAAATGAAATAATAA F: GCGTTCCGATATACTATATAATCCTAAT (ATT) Satt434 H 12 48 R: GCGGGGTTAGTCTTTTTATTTAACTTAA F: TTCCATACAAGATATCAAGTGAATTG (AT) Sat_105 I 20 47 R: GCTCCCCTACATTGGTAGTAAA F: GCTGCATCAGATCACCCATCCTTC (AT) Sat_151 J 16 50 R: CATGCCATGTTGTATGTATGT F: GCGGTCCGTCAATGAATATTAAATTAAAA (AT) Sat_043 K 09 53 R: GCGAAAGCGGCAGAGAGAGAAAGGT F: GCGAGCCTACTTTTACTAGAACGTCAACAAG (AT) Sat_245 L 19 55 R: GCGAAAAATTCAACTCCCCTTTAATAGATTC F: GCGTAGGCATCGGTCAATATTTT (AT) Sat_391 M 07 52 R: GCGTTAGCGAGTGGATCAAGATCA F: TAATATGCTTTAAGTGGTGTGGTTATG (ATT) Satt339 N 03 49 R: GTTAAGCAGTTCCTCTCATCACG F: GCGTGGAAAATATTTTAAAGATTAGGA (AT) Sat_190 O 10 51 R: GCGTGTGTACATTTAGTCTACTTGAGAA annealing temperature. Agriculture 2020, 10, 77 6 of 15 2.3. Selection of SSR Markers for Discrimination of the Varieties The 172 varieties of Korean soybean were discriminated in six steps using six markers, including Sat_076, Sat_417, Sat_043, Satt197, and Satt434 with high polymorphism, and one marker, Satt179, with the lowest polymorphism. The discrimination of soybean varieties was determined by combining the markers with the highest polymorphism in the first step and the marker with the next highest polymorphism in each step. Four markers (Sat_076, Sat_417, Sat_043, and Satt197), which were highly diverse among the 20 markers [9], were initially selected. Additional markers (Satt434 and Satt179) were selected to eciently identify the unknown varieties. 2.4. Data Analysis The number of alleles, major allele frequency, genetic diversity, and PIC values were analyzed using PowerMarker 3.25 software [17]. Genetic distances were obtained using PowerMarker, according to the method of Nei [18], and the phylogenetic tree was prepared using the unweighted pair group mean arithmetic (UPGMA) method to compare group classification. The software program Structures 2.3.3 [19,20] was utilized to detect possible sub-populations (K = 1 to K = 10) with a model allowing for admixture and correlated allele frequencies using a burn-in of 100,000 and run length of 100,000, followed by 5 iterations. The optimal number of populations corresponds to the highest peak in the DK graph [21], and the Korean soybean varieties with membership probabilities (more than 90%) were assigned to each sub-population. 3. Results 3.1. Genetic Diversity and Polymorphism of SSR Loci Table 3 shows the results of genetic diversity analysis using the 20 SSR markers for the 172 soybean varieties developed in Korea. A total of 344 alleles were detected. The number of alleles ranged from 7 (Satt164 and Satt179) to 29 (Sat_076) per locus, with an average of 17.2. The size of alleles ranged from 107 to 361 bp, with Sat_417 and Sat_190 at the widest range of 139–229 bp and 125–215 bp, respectively; whereas Sat_164 had the narrowest range of 222–246 bp (Table S2). PIC values ranged from 0.6799 to 0.9318, with Sat_076 having the highest (0.9318) and Satt179 the lowest (0.6799). The average PIC value was 0.8675. The total varieties per cluster were as follows: 4 (2.3%) in cluster I, 52 (30.2%) in cluster II, 61 (35.5%) in cluster III, 46 (26.7%) in cluster IV, and 9 (5.2%) in cluster V. More than 92% of the total varieties were found in three clusters (II, III, and VI; Figure 1, Table S1 and Table 4). Calculation of genetic distance for pairwise combinations of all soybean varieties supported this conclusion (Table 5). Although the results were obtained with only a limited number of genotypes, clusters III and IV showed the lowest genetic distance (Table 5). Agriculture 2020, 10, 77 7 of 15 Table 3. Linkage group analysis of 20 simple sequence repeat (SSR) markers used for genotyping in 172 Korean soybean varieties. y z Marker Size (bp) N N PIC A A F Sat_374 273–343 21 0.1919 0.9060 AW132402 137–183 15 0.1977 0.8630 Agriculture 2020, 10, x FOR PEER REVIEW 4 of 18 Satt197 107–182 16 0.1395 0.9126 Sat_342 176–246 19 0.2035 0.9113 Dongpuktae, Jangkyong, Satt164 222–246 7 0.3256 0.7529 Baegcheon, Jangyeob Jangsu, Sat_076 122–208 29 0.1337 0.9318 Danweon, Satt179 130–184 7 0.4244 0.6799 Gemjeong1, Satt216 164–248 15 0.3430 0.7850 Soho, Jinmi, Sokang, 9 V Geomjeong2, Tawon, Sat_022 203–255 18 0.1919 0.9114 Nogchae, Cheonsang (5.2%) Daewon Satt185 210–270 16 0.1919 0.8819 Sat_417 139–229 22 0.1105 0.9267 172 Total 18 17 43 94 Sat_210 217–287 18 0.2151 0.8741 (100%) Satt434 301–361 18 0.2326 0.8875 Note: Value in brackets correspond to the percentage distribution of varieties in each cluster classified Sat_105 228–274 18 0.1628 0.9067 according to release year. Sat_151 210–272 17 0.2849 0.8241 Sat_043 242–294 18 0.1453 0.9167 Table 5. Genetic distance between all pairings of clusters based on SSR analysis. Sat_245 119–207 19 0.1395 0.9107 Sat_391 214–282 16 0.1628 0.8903 Cluster I II III IV V Satt339 203–245 14 0.2849 0.8041 I - Sat_190 125–215 21 0.2616 0.8737 II 0.2412 - Total 344 MeanIII 0.2451 0.0873 17.2 - 0.2172 0.8675 y z § IV number of alleles;0.2482 0 major allele frequency;.0915 0 polymorphic.0819 information content.- V 0.3382 0.1659 0.1459 0.1473 - Figure Figure 1. 1. Unweighted Unweighted pair pair group groumean p mean arithmetic (U arithmetic (UPGMA) PGMA) p phylogenetic hylogenetic tretr e u ee sing 20 SS using 20 R markers SSR markers in 172in 172 Korean Korean soybean soybean varietie varieties. s. 3.2. Polymorphism of SSR Loci by Time Period of Release Years and Population Structure Agriculture 2020, 10, x; doi: FOR PEER REVIEW www.mdpi.com/journal/agriculture Agriculture 2020, 10, 77 8 of 15 Table 4. List of varieties in each cluster classified by release years using SSR markers in 172 Korean soybean varieties. Release Period Cluster Total Before 1980 1980s 1990s 2000 or Later Jinpum, Jinpum2, I Gyeongsang1 4 (2.3%) Heugcheong Saebyeol, Sorog, Anpyeong, Daepung, Dachae, Seonam, Dagi, Hojang, Shingi, Sojin, Bukwang, Danbaek, Bosuk, Aga1, Aga2, Kwangan, Wonhwang, Jangki, Myeongjunamul, Deokyu, Baegun, Youngyang, Soyoung, II Iksan, Shelby, Danyeob, Iksannamul, Alchan, 52 (30.2%) Pangsa, Eunha, Pungwon, Jonam, Pungsannamul, Jangmi, Pulmu-heukchae, Aga3, Aga4, Somyeong, Sodam, Wonkwang, Hoseo, Shinhwa, Sowon, Doremi, Galchae, Sohwang, Pulmujigi, Wonheug, Socheong2, Aga8, Aga9, Aga10 Jangwon, Cheongja, Seonnog, Shillok, Geomjeong3, Geumjeong4, Daol, Danmi, Cheongdu1, Daemang, Dajin, Cheongja2, Cheongja3, Sunyu, Sinpaldal2, Duyou, Geomjeongsaeol, Hwasongput, Hannam, Gyeongsang2, Daemang2, Pureun, Geumgang, Mirang, Danmi2, Sobaegnamul, Dajang, Ilpumgeomjeong2, Gemjeongol, III Hwangkeum Josaengseori, Gaechuk1, 61 (35.5%) Ilpumgeomjeong, Gaechuk2, Heugmi, Nokwon, Ilmikong, Saeol, Mansu, Cheonga, Gangil, Seonheuk, Songhag, Nampung, Sangwon, Jinyul, Daehwang, Daeheug, Daeyang, Galmi Manpoong, Heugseong, Daeha1, Gyeongsang3, Jinnong, Gemjeong5, Cheongyeob1, Wonyul, Haessal, Yeonpung, Jinyang Jangdanbaekmok, Jangbeak, Chungbukbaek, Buseok, Namcheon, Milyang, Haman, Keumkangdaelip, Malli, Shinpaldal, Hoban, Socheong, Daewang, Saeal, Paldal, GeumgangSorip, Yukwoo3, Samnam, Taekwang, Daeha, Hanol, Soheug, Uram, IV Pokwang, 46 (26.7%) Kwangdu, Hill, Kwangkyo, Hwaeomput, Soyang, Saedanbaek, Wonhyun, Dankyeong, Muhan, Bongeui, Kanglim, Paldo, Neulchan, JangYon, Gipung Namhae, Jangkyong, Dongpuktae, Baegcheon, Jangsu, Danweon, Jangyeob Gemjeong1, Soho, Jinmi, Sokang, Nogchae, V Geomjeong2, Tawon, 9 (5.2%) Cheonsang Daewon Total 18 17 43 94 (100%) Note: Value in brackets correspond to the percentage distribution of varieties in each cluster classified according to release year. Table 5. Genetic distance between all pairings of clusters based on SSR analysis. Cluster I II III IV V I - II 0.2412 - III 0.2451 0.0873 - IV 0.2482 0.0915 0.0819 - V 0.3382 0.1659 0.1459 0.1473 - 3.2. Polymorphism of SSR Loci by Time Period of Release Years and Population Structure The number of alleles and PIC values obtained as a result of our SSR analysis of the 172 Korean soybean varieties of each group, as classified by release year, are shown in Table 6. A total of 185 alleles in Korean soybean varieties were developed before 1980, where the average number of alleles was 9.3 and the average PIC value was 0.8188. In the 1980s, the total number of alleles was 167, the average number of alleles was 8.4, and the average PIC value was 0.7913. A total of 251 alleles in Agriculture 2020, 10, 77 9 of 15 Korean soybean varieties were developed in the 1990s, where the average number of alleles was 12.6 and the average PIC value was 0.8416. Since 2000, the allele total was 315, the average number of alleles was 15.8, and PIC value was 0.8631. The soybean varieties developed after the 1990s and 2000s had a higher number of alleles and higher PIC values than those developed before 1980. Due to the sample size di erence, the varieties developed after the 1990s and 2000s had more alleles than the varieties developed before 1980. Table 6. Information of 20 markers used for SSR analysis by release period. Release Period Before 1980 1980s 1990s 2000 or later Marker y z N . PIC N . PIC N . PIC N . PIC A A A Sat_374 9 0.8143 9 0.8198 17 0.9006 20 0.8999 AW132402 8 0.7941 7 0.7928 10 0.8383 13 0.8604 Satt197 10 0.8431 7 0.7713 13 0.8890 15 0.9121 Sat_342 10 0.8431 10 0.8288 15 0.8724 19 0.9028 Satt164 4 0.6658 6 0.7817 7 0.7810 7 0.7188 Sat_076 13 0.9001 13 0.8846 19 0.9165 24 0.9285 Satt179 5 0.6987 6 0.5299 5 0.5851 6 0.7093 Satt216 8 0.6886 6 0.5769 10 0.8074 11 0.7779 Sat_022 12 0.8795 9 0.8032 15 0.8927 18 0.9116 Satt185 12 0.8929 9 0.8427 11 0.8395 15 0.8727 Sat_417 8 0.8197 11 0.8752 17 0.9127 20 0.9252 Sat_210 10 0.8645 9 0.8427 12 0.7753 16 0.8778 Satt434 9 0.8024 9 0.8343 13 0.8682 17 0.8904 Sat_105 9 0.8422 8 0.8339 12 0.8724 17 0.9074 Sat_151 7 0.7900 7 0.7157 10 0.7695 16 0.8350 Sat_043 10 0.8640 11 0.8830 15 0.9003 17 0.8982 Sat_245 12 0.8724 6 0.7907 14 0.8989 18 0.9052 Sat_391 11 0.8649 9 0.8266 12 0.8470 15 0.8889 Satt339 8 0.7704 5 0.7557 11 0.8034 12 0.8001 Sat_190 10 0.8645 10 0.8372 13 0.8612 19 0.8399 Total 185 167 251 315 Mean 9.25 0.8188 8.35 0.7913 12.55 0.8416 15.75 0.8631 y z number of alleles; polymorphic information content. The varieties in each cluster, as classified by release period, are shown in Table 4. We found that 18 varieties developed before 1980 were distributed in clusters II and IV, with the most varieties (15) occurring in cluster IV. The 17 varieties developed in the 1980s were in clusters II, III, and IV, with most of these (12) in cluster IV. The 43 varieties developed in the 1990s were distributed throughout all clusters, with 17 varieties found in cluster IV. The varieties developed since the 2000s were distributed in all clusters, but mostly in clusters II and III (80%). The genetic distances between all pairings of groups by release period are shown in Table 7. The genetic distance between varieties developed after 2000 and those developed in the 1980s was the furthest (0.5731), while the genetic distance between varieties developed after 2000 and those developed in the 1990s was the closest (0.1909; Table 7). Table 7. Genetic distance between all pairings of group by release period based on SSR analysis. Release Period Before 1980 1980s 1990s 2000 or Later Before 1980 - 1980s 0.3382 - 1990s 0.4906 0.4444 - 2000 or later 0.5587 0.5731 0.1909 - Agriculture 2020, 10, 77 10 of 15 The population structure of the 172 Korean soybean varieties was inferred using the Structure V2.3.3 software, based on 20 SSR markers. For each K value (K = 1–10), we determined the optimal value of K by calculating DK. As we used Korean soybean varieties in this study, we surveyed the highest DK value over “K = 3”. The three sub-populations, referred to as Pop. 1–3, were observed with the highest DK value (Figure 2) and 17 varieties were admixture type, with a membership probability < 90% (Figure 3). Most soybean varieties were distinguished according to release period. The result of population structure analysis is shown in Table 8. We found that only 18 varieties developed before or in the 1980s were in Pop. 3, except Iksan and Hwangkeumkong. The varieties developed since the 2000s were Agriculture distributed 2020, 10, x FOR PEER in all clusters, REVIEWbut mostly in Pops. 1 and 2 (79%). 7 of 18 Agriculture 2020, 10, x FOR PEER REVIEW 7 of 18 Figure 2. Values of ΔK, with its modal value detecting a true K of three groups (K = 3). Figure 2. Figure 2. ValuesValues of of DK, with ΔK, wi its th it modal s modalvalue value detect detecting ing a true K of a true three gr K of thr oups ( ee gr Koups = 3). (K = 3). Figure 3. Model-based membership of 172 Korean soybean varieties using STRUCTURE. Colors Figure 3. Model-based membership of 172 Korean soybean varieties using STRUCTURE. Colors Figure 3. Model-based membership of 172 Korean soybean varieties using STRUCTURE. Colors represent model-based represent model-base sub-populations d sub-populations as as follows: follows: re red, d, Po Pop. p. 1; lime, 1; lime, Pop. Pop. 2; and blue, Po 2; and blue, p. 3. Pop. 3. represent model-based sub-populations as follows: red, Pop. 1; lime, Pop. 2; and blue, Pop. 3. Agriculture 2020, 10, x; doi: FOR PEER REVIEW www.mdpi.com/journal/agriculture Agriculture 2020, 10, x; doi: FOR PEER REVIEW www.mdpi.com/journal/agriculture Agriculture 2020, 10, 77 11 of 15 Table 8. Each step of discrimination of 172 Korean soybean varieties using six SSR markers. Percentage of No. of Identified Step Marker Identified Varieties Identified Varieties Varieties Gaechuk2, Geumjeong4, Heugcheong, Jinnong, Step 1 Sat_076 7 4.1% Manpoong, Seonam, Sobaegnamul Bongeui, Galmi, Geumgangsorip, Jangkyong, Somyeong, Heugseong, Jangyon, Jangsu, Pureun, Baegcheon, Daemang2, Dankyeong, Deokyu, Haessal, Taekwang, Ilpumgeomjung, Jangdanbaekmok, Wonkwang, Saedanbaek, Tawon, Wonheug, Dajang, Geomjeongol, Cheongja2, Cheongyeob1, Daehwang, Galchae, Paldo, Pokwang, Shinpaldal, Jinyang, Milyang, Wonhwang, Aga2, Step 2 Step 1 + Sat_417 73 42.4% Samnam, Sohwang, Sorog, Pulmujigi, Shingi, Haman, Hwaeomput, Heugmi, Jangyeob, Malli, Jangwon, Dongpuktae, Jonam, Saeal, Soyoung, Bukwang, Daepung, Pungsannamul, Saebyeol, Jangmi, Kwangan, Muhan, Sangwon, Shinhwa, Youngyang, Hoban, Gyeongsang1, Jangbeak, Shelby, Wonhyun, Josaengseori, Pangsa, Doremi, Hanol, Danyeob, Hojang, Hoseo, Namcheon, Soho Duyou, Geumgang, Cheongja, Geomjeong1, Daewon, Shillok, Ilpumgeomjeong2, Sunyu, Cheonga, Cheongja3, Sowon, Sodam, Alchan, Dachae, Iksannamul, Socheong2, Dagi, Songhag, Danbaek, Anpyeong, Eunha, Baegun, Gyeongsang2, Gyeongsang3, Gaechuk1, Aga4, Aga9, Jangki, Paldal, Danmi2, Geomjeongsaeol, Hill, Chungbukbaek, Step 3 Step 2 + Sat_043 Danweon, Jinpum, Jinpum2, Cheongdu1, Daemang, 69 40.1% Daeyang, Geomjeong3, Buseok, Kwangdu, Nokwon, Seonnog, Daol, Jinyul, Gangil, Dajin, Pulmu-heukchae, Saeol, Geomjeong2, Mirang, Daeheug, Myeongjunamul, Danmi, Hannam, Soyang, Ilmi, Yukwoo 3, Kanglim, Kwangkyo, Jinmi, IksanCheonsang, Hwangkeum, Keumkangdaelip, Neulchan, Soheug, Wonyul Gipung, Uram, Daewang, Socheong, Hwasongput, Step 4 Step 3 + Satt197 Yeonpung, Aga10, Sojin, Aga1, Namhae, Seonheuk, 13 9.6% Geomjeong5, Sinpaldal2 Nogchae, Sokang, Aga3, Aga8, Pungwon, Bosuk, Step 5 Step 4 + Satt434 8 4.6% Daeha, Daeha1 Step 6 Step 5 + Satt179 Mansu, Nampung 2 1.2% Total 172 100% 3.3. Discrimination of Soybean Varieties In the first step, seven (4.1%) varieties were discriminated according to the results when using Sat_076, which had the highest allele and PIC values, and were not discriminated from 165 varieties. In step 2, 73 (42.4%) varieties were discriminated among the 165 varieties by including Sat_417. In step 3, 69 (40.1%) varieties were discriminated among 92 varieties by including Sat_043. In step 4, 13 (7.6%) of 23 varieties were discriminated when including Satt197. In step 5, 8 (4.6%) of 10 varieties were discriminated when including Satt434. In the final step, two varieties (1.2%), Mansu and Nampung, were discriminated by including Sat_179, allowing for discrimination of all 172 varieties. Of the 20 markers, Mansu and Nampung could only be discriminated when using Sat_179 (Table 8, Figure S2). 4. Discussion Many genetic diversity analyses using DNA markers have been reported for the development of new varieties, discrimination of varieties, and to search for useful genes. Song et al. [22] reported the use of 72 markers to analyze 185 accessions of genetic resources collected from Korea, China, Japan, India, Myanmar, Philippines, and the United States, detecting 3–31 alleles per locus and an average of 10.9 alleles, which were used to classify the data into three groups, following which detailed groups were formed according to geographical origins. Kuroda et al. [23] used the genetic resources of 1318 Agriculture 2020, 10, 77 12 of 15 native varieties and wild-type plants from China, Japan, Korea, and Russia to analyze the genetic diversity and develop a core collection. A high number of rare alleles were found in the wild types of Korea, suggesting that the ratio of soybean core collection to selection was high. Korea has its own wild soybean types, but other types of resources have also been introduced from both China and Japan, resulting in accessions with high genetic diversity. Wang et al. [24] analyzed 23 developed soybean and native varieties and 17 wild types using 40 SSR markers, and detected that the high genetic diversity of wild types was due to the loss of many alleles in the evolutionary process. The genetic distance between all pairings of groups by release period shows that those in the 1990s and 2000 onward were the closest (0.1909), and 2000 onward and before or during the 1980s were the farthest (0.5587 and 0.5731, respectively; Table 7). The PIC value, in terms of release periods among Korean soybean varieties, was the highest in those developed from 2000 onward (Table 6). The reason for this finding is that, since the mid-1980s, various varieties have been developed with the aim of diversification of use and high quality. Many varieties have been developed for comparison to other groups by release period. We also found that the most genetic diversity among varieties can be observed in those developed since the 2000s (Figure 1 and Figure S1, Table 4). This trend is expected to continue in the future. Many results of genetic diversity analyses and discrimination of varietal studies using DNA markers have been reported. Kim et al. [25] discriminated 82 of the 91 developed soybean varieties from 1913 to 2002 using five SSR markers—Sat_043, Sat_022, Sat_036, Sat_088, and Satt045—and reported that unidentified varieties could not be distinguished using their morphological characteristics. Hwang et al. [26] reported that 18 sequence-tagged sites—cleaved amplification polymorphic sequence (STS-CAPS) markers (51 combinations)—were developed from base sequence information in prepared genomic DNA libraries, and 106 Korean soybean varieties were discriminated in 14 steps. Gao et al. [27] discriminated 83 soybean varieties using nine markers with high polymorphism and high diversity within the population. An allele of each marker was coded to form a nine-digit identification (ID) for the discrimination of soybean varieties. In Korea, the first developed soybean variety was Jangdanbaekmok produced by separating landrace through pure line isolation in 1913, and the first hybrid variety was developed in 1969 characterized with high yielding ability and resistant to mosaic virus [28]. Until 2014, a total of 178 soybean varieties have been developed and registered at the two national institutes, the RDA-Genebank Information Center and the Korea Seed & Variety Service [28,29]. Lee et al. [29] analysis that a total of four pedigrees involving 168 varieties (94.4% out of 178 varieties), which form the broadest network of pedigrees. We analyzed genetic diversity using 172 Korean soybean varieties developed until 2013. Of these, 162 varieties include in four pedigrees, the results of genetic diversity and pedigrees provide information for selection of parental lines and design of crossing strategies. The full-length sequence of the American soybean variety Willams 82 has been analyzed and published for the first time [30] and, using the sequence information, a large amount of information regarding candidate polymorphic SSR markers has also been published [31]. In Korea, Kim et al. [32] analyzed the full-length sequences of six Korean soybean varieties to distinguish genomes from dense variation blocks (dVBs) with SNP accumulation and sparse variation blocks with little SNPs. As dVBs have distinctive characteristics according to variety, various di erences were indeed found to exist among the varieties. Therefore, 202 dVB-specific insertion/deletion (indel) markers have been developed [33]. A barcode system was established, which clearly distinguishes 147 Korean soybean varieties and is regularly updated whenever new varieties are developed [34,35]. The development of DNA markers has been simplified due to the active progression of genome research, but the development of DNA markers through full-length genome analysis in a small-scale laboratory is still dicult. As the marker analysis was not performed directly, polymorphism information is not known in full-length genome analysis. In this study, we analyzed 172 Korean soybean varieties developed from 1913 to 2013 using SSR markers that have been previously confirmed to be highly polymorphic. The analysis of genetic resources, including newly developed soybean varieties after 2014, in addition Agriculture 2020, 10, 77 13 of 15 to the data accumulated in this study, will contribute to the development of future varieties with useful traits, such as drought- and disease-resistance, including the determination of species and the creation of a core collection for soybean conservation and breeding. 5. Conclusions In this study, the genetic diversity of 172 Korean soybean varieties was analyzed and discriminated using SSR Markers. Five di erent clusters were classified using the UPGMA method, among which the genetic distance between clusters I and V was the farthest, and that between clusters III and IV was the closest. The genetic diversity distance by release period between all pairings of groups was the lowest between varieties developed after 2000 and the 1990s, and the highest between varieties developed after 2000 and the 1980s. The results demonstrate that the greatest genetic diversity among varieties was found in those that had been developed after 2000. The 172 varieties of Korean soybean were discriminated in six steps using six markers (Sat_076, Sat_417, Sat_043, Satt197, Satt434, and Satt179). The analysis of genetic resources, including newly developed varieties, in addition to the data accumulated in this study, will contribute to the creation of a core collection for soybean conservation and breeding, as well as to the development of future varieties with useful traits. Supplementary Materials: The following are available online at http://www.mdpi.com/2077-0472/10/3/77/s1, Table S1: Number of soybean varieties classified by released year and breeding organization for this experiment, Table S2: Information of band size (bp) based on 20 markers for SSR analysis in 172 Korean soybean varieties, Figure S1: Principal coordinate analysis (PCoA) plot of 172 Korean soybean varieties using 20 SSR markers, Figure S2: Discrimination of varieties at each step by UPGMA dendrogram using six markers in 172 Korean soybean varieties. Author Contributions: Conceptualization, J.S. and H.-S.K.; Methodology, T.-Y.H. and B.S.G.; Formal analysis, T.-Y.H. and B.S.G.; Investigation, T.-Y.H., B.S.G., and J.S.; Resources, H.-S.K.; Data curation, T.-Y.H.; Writing—original draft preparation, T.-Y.H. and B.S.G.; Writing—review and editing, J.S. and H.-S.K. Visualization, T.-Y.H.; Supervision, J.S.; Project administration, H.-S.K.; Funding acquisition, H.-S.K. 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Agriculture – Multidisciplinary Digital Publishing Institute

Published: Mar 13, 2020

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