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. 2021 Jan 25;27(1):93–105. doi: 10.1007/s12298-021-00933-3

Identifying SSR markers associated with seed characteristics in Perilla (Perilla frutescens L.)

Ye Ju Ha 1,#, Kyu Jin Sa 1,#, Ju Kyong Lee 1,✉
PMCID: PMC7873175  PMID: 33627965

Abstract

Substantial differences exist in seed dormancy between cultivated crops and their wild progenitors. The purpose of this study was to identify simple sequence repeat (SSR) markers associated with seed characteristics in cultivated and weedy types of Perilla crop. By using an association analysis of 29 SSR markers and three seed traits in 38 Perilla accessions, we detected six SSR markers associated with the seed germination rate (SGR), eight SSR markers associated with seed hardness (SH), and seven SSR markers associated with seed size (SS). Among these SSR markers, three (KNUPF3, KNUPF25, KNUPF60) were associated with the SGR, SH, and SS traits. Correlation analysis among the three seed traits of the 38 Perilla accessions showed a positive correlation coefficient for the combination of SGR and SS (0.811**) and a negative correlation coefficient for the combinations of SGR and SH (− 0.706**), and SS and SH (− 0.899**). A phylogenetic tree constructed using the unweighted pair group method with arithmetic mean (UPGMA) revealed that accessions of cultivated P. frutescens var. frutescens could be distinguished from weedy accessions of P. frutescens var. frutescens and P. frutescens var. crispa using the 29 SSR markers. Selected SSR markers related to the three seed traits distinguished accessions of cultivated and weedy types. Therefore, these results are very important for understanding the seed characteristics of cultivated and weedy types of Perilla crop. It will further help for improving the seed quality of Perilla crop through marker-assisted selection (MAS) breeding programs.

Supplementary Information

The online version of this article (10.1007/s12298-021-00933-3) contains supplementary material, which is available to authorized users.

Keywords: Perilla frutescens, Cultivated and weedy types, Seed traits, SSR marker, Association analysis

Introduction

Perilla frutescens (L.) Britt. belongs to family Lamiaceae and, is widely cultivated and used in East Asia. Perilla crop is classified into two different varieties (or cultivated types) based on its distinct morphological characteristics and dual uses (Makino 1961). One type is P. frutescens var. frutescens, which is used as both a leafy vegetable and an oil crop (ren in Chinese, dlggae in Korean, and egoma in Japanese). The other type is P. frutescens var. crispa, which is used in Chinese medicine and as a vegetable crop (zisu in Chinese, chajoki in Korean, and shiso in Japanese) (Lee and Ohnishi 2001, 2003; Nitta et al. 2003). The two cultivated types of Perilla crop have several distinct morphological features: var. frutescens exhibits a taller plant height and larger seed size (> 2 mm) and has soft or hard seeds, green leaves and stem, and non-wrinkled leaves; while var. crispa is smaller in plant height and seed size (< 2 mm), has only hard seeds, exhibits red or green coloration on the leaves and stems, and has wrinkled or non-wrinkled leaves (Makino 1961; Lee and Ohnishi 2001; Nitta et al. 2003). The wild ancestor of cultivated types of Perilla crop in East Asia has not been identified, but the first reports of weedy plants of Perilla in the two cultivated types of P. frutescens were made by Lee and Ohnishi (2001, 2003), Lee et al. (2002), Nitta and Ohnishi (1999), and Nitta et al. (2003). Profiling of random amplification of polymorphic DNA (RAPD) and amplified fragment length polymorphism (AFLP) markers showed that these weedy plants can be grouped into two types viz. one type belonging to the var. frutescens group and the other type belonging to the var. crispa group. However, no specific evidence of the origin of the weedy Perilla plants was provided in their analyses. Weedy Perilla plants grow naturally in East Asia and are often found in habitats in areas such as roadsides and wastelands and in the vicinity of rivers, streams, farmers’ fields, and farmhouses (Lee and Ohnishi 2001, 2003; Nitta et al. 2003, 2005). These weedy plants exhibit green leaves and stems and the same fragrance as var. frutescens, but their seeds are smaller (< 2 mm) and harder than those of cultivated var. frutescens (Lee and Ohnishi 2001). However, in southern China, wild var. frutescens (tetraploid) grows like a weed. It usually presents the same morphological characteristics as the weedy var. frutescens in Korea and Japan, but occasionally it shows similar morphological characteristics to var. crispa, such as red–purple leaf color and a var. crispa-like fragrance (Nitta et al. 2005). The two cultivated types of Perilla crop and their weedy types are tetraploid (2n = 40) with the same number of chromosomes and are cross-fertile by artificial pollination (Yamane 1950; Honda et al. 1990; Nitta et al. 2003). In spite of many taxonomic studies to distinguish the two cultivated types of Perilla crop, it is still difficult to distinguish between them because of the existence of intermediate types and weedy types (Yamane 1950; Makino 1961; Honda et al. 1990; Lee and Ohnishi 2001, 2003; Nitta et al. 2003).

In the process of crop evolution, seed dormancy is one of the largest differences between cultivated crops and the corresponding wild species. In general, seed dormancy in wild species is an evolutionary adaptation that prevents seeds from germinating under unsuitable ecological conditions that would typically lead to a low probability of seedling survival. An important function of seed dormancy is delayed germination. This allows dispersal and prevents the simultaneous germination of all seeds (Foley and Fennimore 1998; Sohindji et al. 2020). However, seed dormancy is not necessary for cultivated crops because they should germinate at the same time after sowing in the field. Thus, seed dormancy is strongly selected against during domestication; hence, it is not expected to be present in cultivated crops (Hancock 1992). In most crops, seed dormancy is an important trait for distinguishing cultivated from wild plants.

To maximize the efficient management and utilization of Perilla germplasm resources, it is necessary to evaluate accurately Perilla germplasm accessions. DNA-based molecular marker systems provide useful information with regard to genetic diversity, genetic relationships, and population structure differences between cultivated species and their wild relatives. With the recent development of molecular breeding technology, marker-assisted selection (MAS) allows breeders to select desired phenotypes directly from genotypes (Park et al. 2014). The first step of MAS is to identify the molecular markers associated with specific traits. Therefore, the development of segregating populations (or lines) for specific traits is necessary for association mapping studies. In the case of Perilla crop, the available genetic information (e.g., linkage maps, quantitative trait loci, sequence information) is insufficient. Association mapping is easier and less expensive than other methods because it does not require genetic map construction and requires fewer markers than are needed to construct a genetic map for QTL mapping (Quarrie et al. 1999; Wenzl et al. 2007). In particular, association mapping has been advocated as the method of choice for identifying loci involved in the inheritance of complex traits (Risch and Merikangas 1996). Furthermore, among the many molecular marker systems, SSR markers have provided useful information for the analysis of genetic diversity, genetic relationships, and population structure in crop species germplasms because they are highly reproducible, polymorphic, generally codominant, and abundant in plant genomes (Powell et al. 1996; Semagn et al. 2006; Park et al. 2009). SSR primer sets have been developed for Perilla crop by many Korean researchers (Kwon et al. 2005; Park et al. 2008; Sa et al. 2018, 2019) and used to analyze genetic diversity and population structure in Perilla germplasm accessions (Park et al. 2008, 2019; Sa et al. 2013, 2015; Ma et al. 2017, 2019).

Therefore, in this study, we used Perilla SSR primers to determine genetic variation, genetic relationships, and population structure and for association mapping in Perilla accessions. In addition, association mapping was employed to identify molecular markers linked with seed characteristics such as seed germination rate (SGR), seed hardness (SH), and seed size (SS) in cultivated and weedy types of Perilla accessions to allow better accession selection in molecular breeding programs for Perilla crop using MAS.

Materials and methods

Plant materials and DNA extraction

In this study, a total of 38 Perilla accessions (18 cultivated var. frutescens, 11 weedy var. frutescens, and 9 weedy var. crispa accessions) were selected for association mapping in accordance with their seed characteristics from among 59 accessions used in a previous study by Sa et al. (2018). SGR, SH, and SS of the Perilla accessions used in this study were measured one month after harvest (Sa et al. 2018). Information on the seed characteristics and the accession numbers of these materials are shown in Table 1. Total DNA was extracted from the young leaf tissue of individual representative plants of each accession according to Plant DNAzol Reagent protocols (GibcoBRL Inc., Grand Island, NY, USA).

Table 1.

Accessions of cultivated var. frutescens and related weedy types used in this study

Accession name Source of material Type Germination rate (%) Seed hardness* Seed size**
Village, town or city Country
1 Jinan-gun, Jeollabuk-do Korea Cultivated var. frutescens 72 S L
2 Jecheon, Chungcheongbuk-do Korea Cultivated var. frutescens 75 S L
3 Jecheon, Chungcheongbuk-do Korea Cultivated var. frutescent 70 H L
4 Mungyeong-si, Gyeongsangbuk-do Korea Cultivated var. frutescent 55 S L
5 Uiseong-gun, Gyeongbuk-do Korea Cultivated var. frutescens 71 S L
6 Uiseong-gun, Gyeongbuk-do Korea Cultivated var. frutescens 64 S L
7 Uiseong-gun, Gyeongbuk-do Korea Cultivated var. frutescens 70 S L
8 Pyeongchang-gun, Gangwon-do Korea Cultivated var. frutescens 67 S L
9 Jeongseon-gun, Gangwon-do Korea Cultivated var. frutescens 69 S L
10 Yeongwol-gun, Gangwon-do Korea Cultivated var. frutescens 59 S L
11 Yeongwol-gun, Gangwon-do Korea Cultivated var. frutescens 72 S L
12 Jecheon, Chungcheongbuk-do Korea Cultivated var. frutescens 58 S L
13 Jecheon, Chungcheongbuk-do Korea Cultivated var. frutescent 73 S L
14 Jecheon, Chungcheongbuk-do Korea Cultivated var. frutescens 76 S L
15 Cheongsong-gun, Gyeongsangbuk-do Korea Cultivated var. frutescens 5 S L
16 Yeongyang-gun, Gyeongsangbuk-do Korea Cultivated var. frutescent 7 S L
17 Yeongyang-gun, Gyeongsangbuk-do Korea Cultivated var. frutescent 12 S L
18 Pyeongchang-gun, Gangwon-do Korea Cultivated var. frutescent 16 S L
19 Mungyeong-si, Gyeongsangbuk-do Korea Weedy var. frutescens 0 H S
20 Uiseong-gun, Gyeongbuk-do Korea Weedy var. frutescens 1 H S
21 Uiseong-gun, Gyeongbuk-do Korea Weedy var. frutescens 0 H S
22 Uiseong-gun, Gyeongbuk-do Korea Weedy var. frutescens 0 H S
23 Uiseong-gun, Gyeongbuk-do Korea Weedy var. frutescens 0 H S
24 Cheongsong-gun, Gyeongsangbuk-do Korea Weedy var. frutescens 0 H S
25 Cheongsong-gun, Gyeongsangbuk-do Korea Weedy var. frutescens 0 H S
26 Cheongsong-gun, Gyeongsangbuk-do Korea Weedy var. frutescens 0 H S
27 Yeongyang-gun, Gyeongsangbuk-do Korea Weedy var. frutescens 0 H S
28 Yeongyang-gun, Gyeongsangbuk-do Korea Weedy var. frutescens 0 S S
29 Yeongyang-gun, Gyeongsangbuk-do Korea Weedy var. frutescens 0 H S
30 Muju-gun, Jeollabuk-do Korea Weedy var. crispa 0 H S
31 Jecheon, Chungcheongbuk-do Korea Weedy var. crispa 1 H S
32 Mungyeong-si, Gyeongsangbuk-do Korea Weedy var. crispa 0 H S
33 Cheongsong-gun, Gyeongsangbuk-do Korea Weedy var. crispa 0 H S
34 Yeongyang-gun, Gyeongsangbuk-do Korea Weedy var. crispa 0 H S
35 Yeongyang-gun, Gyeongsangbuk-do Korea Weedy var. crispa 0 H S
36 Yeongyang-gun, Gyeongsangbuk-do Korea Weedy var. crispa 0 H S
37 Bonghwa-gun, Gyeongsangbuk-do Korea Weedy var. crispa 0 H S
38 Bonghwa-gun, Gyeongsangbuk-do Korea Weedy var. crispa 0 H S
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*Seed hardness: S—Soft, H—Hard

**Seed size: L—Large, S—Small

SSR analysis and electrophoresis

Based on a preliminary test with 120 SSR primer sets to identify efficient SSR primer sets showing high allele band amplification and a clear banding pattern, 29 SSR primer sets were selected. The information on the 29 Perilla SSR primer sets used in this study is shown in Table 2. SSR amplification was conducted in a total volume of 20 μL containing 20 ng of genomic DNA, 0.5 μM forward and reverse primers, 0.2 mM dNTPs, 1 × PCR buffer, and 1 unit of Taq polymerase (BioTools, Spain). The PCR conditions consisted of initial denaturation at 95 °C for 3 min, followed by 36 cycles of 95 °C for 30 s, 55 °C for 30 s, and 72 °C for 1 min 30 s, with a final extension step of 5 min at 72 °C. After PCR, DNA electrophoresis analysis was performed with a QIAxcel advanced system (QIAGEN Co., Hilden, Germany) according to the protocol described in the QIAxcel DNA Handbook. The samples were run in the QIAxcel advanced electrophoresis system, and sample separation was performed over 15 min. Gel images were obtained as the results, and the quantification analysis was performed with QIAxcel software. The results were displayed as gel images and electropherograms acquired from the QIAxcel advanced system software (Supplement Fig. 1).

Table 2.

Characteristics of the 29 Perilla microsatellite loci used in the study

SSR loci Forward Primer(5′-3′) Reverse Primer(3′-5′) Repeat motif
KNUPF1 CTTGCAGCTGATCATTAAGCTA TTTCTTGTGTGCTCTAACAACG (AG)11
KNUPF2 GAAACCAAATTTCTTGTTCTTACA CAAACGCAGACTCTTATCAATG (AG)16
KNUPF3 TTCCTTGTAGTCATCTGATCCC TGGAAATTAATTAAAGGGCTGA (AG)16
KNUPF5 TCCATCTCATCTCATTCAAACA ATGGATCGGAAATCTAAAAACA (AT)10
KNUPF12 AATTCAATCTCGCCTCCATATT TTCTGAATCTTGAAGCTTTGGT (CA)11
KNUPF15 CCACACGTAAACCTCATAAACC TTATCTCTAAAGAAATCGGGCA (CT)16
KNUPF16 CCTGTATCTCTCCCCGATAAAT TGGATTTAATGCAGTTGAGTTG (CT)22
KNUPF23 TTGCAAGTTCTTGAATTGTGAC CACTCCTTCCCTCCTCTTTAAT (TG)11
KNUPF25 GCTTAGTGTGAGGAATTATGTAGGA ACTCAGCATGCTTGAATTCTC (AAG)12
KNUPF30 AACTAGTATATATGGCCTGCAAAAA GACCTCTATCTCCCACATCCTA (ATC)10
KNUPF33 GCAATCTTGTGAAATGAAATGA TGATTCCCAGCGCTACTATTAT (CAT)8
KNUPF37 GGTGTGAAAAAGAGAGTGGAGA TTGAATTGCCTGTTGATAGTGA (GGT)10
KNUPF39 TCACCTTCCCCTTCATTTATTA AGGATCGAACAGAACAAACTGT (TCT)13
KNUPF49 CTAGGTGTGGGTGATTTTCAAT AAACTACCTACCACCATTTCCC (AG)17
KNUPF59 AATCTCGATGCCTAACAACAGT TTCCTTGTAAATCCAGCTAAGG (CAG)7
KNUPF60 GCAATGGACATCTGTGAGAGTA AATTGTGGTAATCATAGGGCAG (CAG)7
KNUPF61 GGGATACCCAAATTTCTACCAT TCATGAAAAATCCAAACATTCA (CAG)7
KNUPF72 TAATTTGAGGGATTCCTTTCCT CGCCACCCTTACTACTTCATAC (TCGA)4
KNUPF74 TTGACTGTACCAGAGCATCAAG GGGTACACTCACAACTCTACCAA (AAAT)6
KNUPF78 GCGTTATTATTTTTCAAGATCG TCAATGATTTTACAGAAGATGCT (AAC)7
KNUPF80 GATTCATCATTCAGCTCTCTCC ATGACCAATGGATTAAACAAGG (CT)17
KNUPF81 TTAAGCAACCAATTGCAGGTA GTTGTGCAAAATTTGGTGATTT (AAT)7
KNUPF82 AAACCAAGGAACTCGTCAACTA CGCTTCGTCTTTATTGTGTGTA (AGA)7
KNUPF83 TTTGTCTTGATCTGCTTTGATG CTACCTCGCAGAATCAAGCTAT (TG)9
KNUPF84 CAAGAATTACACGCATTTGGTA GAGAGCACACAATTATCAAGCA (AAT)7
KNUPF85 GATGACGATGAAGCTTTTCAG CTCCTCAGCAGTTTCACCTAAC (GAT)7
KNUPF86 CAGAAACTAACATTTCATCGCA ATTTATCCACACTCGCTTCAGT (AAAT)4
GBPFM111 ATCATGGATGAATCGCACTT CATTCTCCAAATGTTACTCTATTT (ACACA)8
GBPFM134 CCTCCACTTCTTCTTCTCCC TTTGCATCCTGTCTCTCACA (CT)5
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Data analysis

The DNA fragments amplified for each SSR marker were scored as present (1) or absent (0). Power Marker version 3.25 (Liu and Muse 2005) was applied to obtain information on the number of alleles, allele frequency, major allele frequency (MAF), genetic diversity (GD), and polymorphic information content (PIC). Genetic similarity (GS) was calculated for each pair of accessions using the Dice similarity index (Dice 1945). The similarity matrix was then used to construct a dendrogram with the unweighted pair group method with arithmetic mean (UPGMA) via the application of SAHN clustering in NTSYS-pc V2.1 (Rohlf 1998). Population structure was investigated for 38 Perilla accessions using STRUCTURE 2.2 software (Pritchard and Wen 2003). Five independent runs with K values ranging from one to ten were performed with 100,000 cycles for both burn-in and run length. The delta K statistic, based on the rate of change in the log probability of data between K values (Evanno et al. 2005), was calculated with STRUCTURE HARVESTER (http://taylor0.biology.ucla.edu/structharvest/) based on the STRUCTURE results. Association mapping was performed for marker-trait association using TASSEL 3.0 (Bradbury et al. 2007), which was used to evaluate marker-trait associations using a general linear model (Q GLM). The Q GLM method was performed using the population structure (Q) matrix derived from the STRUCTURE program. The number of permutation runs was set to 10,000 to obtain a marker significance value of P ≤ 0.01. SPSS software was used to perform correlation analysis for the three seed characteristics.

Results

Genetic variation in Perilla accessions revealed using seed characteristics and SSR markers

Information on the seed characteristics, SGR, SH, and SS, of the 38 Perilla accessions used in this study is provided in Table 1. Of the accessions of cultivated var. frutescens, 13 accessions showed high germination rates and large (> 2 mm) soft seeds, while one accession showed a high germination rate and large (> 2 mm) hard seeds. In addition, four accessions of cultivated var. frutescens exhibited large (> 2 mm) soft seeds and showed a low germination rate. Ten accessions of weedy var. frutescens and nine accessions of weedy var. crispa showed low germination rates and small (< 2 mm) hard seeds; however, one accession of weedy var. frutescens showed soft seeds, a low germination rate, and small seeds (< 2 mm) (Table 1). Correlation analysis was performed to evaluate the relationships among the three seed characteristics in 38 accessions of cultivated var. frutescens and related weedy types. Among all combinations, the combination of SGR and SS (0.811**) showed a higher positive correlation coefficient than the other combinations. The combinations of SGR and SH (− 0.706**) and SS and SH (− 0.899**) showed higher negative correlations, with a significance level of 0.01 (Table 3).

Table 3.

Pearson correlation coefficient for three seed characteristics in 38 Perilla accessions

SS SH
SGR 0.811** − 0.706**
SS − 0.899**
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** Significance at P < 0.01

The genetic variation of 29 SSR loci was measured with regard to the number of alleles, GD, PIC, and MAF among the 38 accessions of cultivated var. frutescens and related weedy types (Table 4). The total of 29 SSR loci was confirmed to have a total of 141 alleles in the 38 accessions. The number of alleles per locus ranged from 2 for KNUPE59 to 9 for KNUPE5, and the average number of alleles per locus was 4.86 (Table 4). The average GD was 0.597, with a range of 0.273 (KNUPE61)—0.788 (KNUPE78). The average PIC was 0.550, with a range of 0.247 (KNUPE61)—0.758 (KNUPE78). The average MAF was 0.541, with a range of 0.289 (KNUPE78 and KNUPF80)—0.842 (KNUPE61) (Table 4). Among the 141 alleles, 22 private alleles (15.6%) were only detected in one of the 38 accessions of Perilla crop. The percentage of rare alleles (frequency < 0.05) was 15.6% (22 alleles) among the 141 alleles, whereas intermediate-frequency alleles (frequency of 0.05–0.5) and abundant alleles (frequency > 0.5) represented 73.0% (103 alleles) and 11.3% (16 alleles) of the total alleles, respectively (Fig. 1).

Table 4.

Estimates of allele number, GD, PIC and MAF of 29 SSR primers among 38 Perilla accessions

Marker Allele no. GD PIC MAF
KNUPF12 3.00 0.470 0.410 0.684
KNUPF16 3.00 0.508 0.403 0.579
KNUPF3 6.00 0.662 0.626 0.526
GBPFM111 6.00 0.508 0.486 0.684
GBPFM134 3.00 0.373 0.320 0.763
KNUPF30 4.00 0.694 0.644 0.447
KNUPF60 3.00 0.547 0.445 0.500
KNUPF25 6.00 0.765 0.727 0.316
KNUPF15 5.00 0.561 0.528 0.632
KNUPF23 5.00 0.558 0.523 0.632
KNUPF1 6.00 0.695 0.650 0.447
KNUPF5 9.00 0.733 0.712 0.474
KNUPF33 8.00 0.784 0.753 0.316
KNUPF37 4.00 0.569 0.520 0.605
KNUPF59 2.00 0.361 0.296 0.763
KNUPF39 6.00 0.771 0.737 0.342
KNUPF74 3.00 0.381 0.338 0.763
KNUPF2 6.00 0.620 0.589 0.579
KNUPF61 3.00 0.273 0.247 0.842
KNUPF72 4.00 0.420 0.379 0.737
KNUPF49 5.00 0.611 0.573 0.579
KNUPF85 4.00 0.632 0.567 0.500
KNUPF81 4.00 0.564 0.474 0.526
KNUPF84 4.00 0.543 0.475 0.605
KNUPF83 5.00 0.686 0.640 0.474
KNUPF86 5.00 0.704 0.659 0.447
KNUPF82 7.00 0.771 0.738 0.342
KNUPF78 7.00 0.788 0.758 0.289
KNUPF80 5.00 0.760 0.720 0.289
Mean 4.86 0.597 0.550 0.541
Total 141
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Fig. 1.

Fig. 1

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Histogram of allele frequencies in 38 Perilla accessions

Population structure and association analysis among 38 Perilla accessions using SSR markers and seed characteristics

To understand the population structure of the 38 accessions of cultivated var. frutescens and related weedy types, we divided each accession into corresponding subgroups using the model-based approach in STRUCTURE software. The ad hoc measure ΔK based on the method reported by Evanno et al. (2005) was applied to overcome the difficulty in interpreting actual K values. The highest value of ΔK for the 38 Perilla accessions was obtained for K = 2 (Fig. 2). As shown by the results, all accessions were clearly divided into 2 main groups at K = 2 (Fig. 3): Group I comprised 18 accessions of cultivated var. frutescens; and Group II included 20 accessions, consisting of 11 accessions of weedy var. frutescens and 9 accessions of weedy var. crispa. In addition, to select the SSR markers associated with SGR, SH, and SC in the 38 Perilla accessions, the alleles of the 29 SSR markers and phenotypic data for seed characteristics SGR, SH, and SS were used to confirm significant marker-trait associations (SMTAs) using TASSEL software. From the results, we detected six SSR markers (KNUPF3, KNUPF25, KNUPF30, KNUPF60, KNUPF85, and KNUPF86) associated with the SGR trait using the GLM at a significance level of P ≤ 0.01 (Table 5). In addition, eight SSR markers (KNUPF2, KNUPF3, KNUPF25, KNUPF30, KNUPF60, KNUPF80, KNUPF81, and KNUPF85) were associated with the SH trait. Finally, seven SSR markers (KNUPF2, KNUPF3, KNUPF25, KNUPF30, KNUPF60, KNUPF80, and KNUPF85) were associated with the SS trait (Table 5). Among these significant markers related to seed characteristic traits, the KNUPF3, KNUPF25, and KNUPF60 SSR markers were together associated with the SGR, SH, and SS traits (Table 5). In addition, six SSR markers (KNUPF2, KNUPF3, KNUPF25, KNUPF30, KNUPF60, and KNUPF80) were together associated with the SH and SS traits (Table 5).

Fig. 2.

Fig. 2

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Magnitude of ΔK as a function of K. The peak value of ΔK was observed at K = 2, suggesting the existence of two genetic clusters in 38 accessions of cultivated var. frutescens and related weedy types

Fig. 3.

Fig. 3

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Population structure pattern for the highest ΔK value, K = 2, in 38 accessions of cultivated var. frutescens and related weedy types based on 29 SSRs

Table 5.

List of significant markers detected with the Q GLM model

Trait Marker Marker-p Marker R2
Germination rate KNUPF3 1.44E-04 0.509
KNUPF25 3.48E-04 0.501
KNUPF30 2.12E-06 0.558
KNUPF60 7.10E-09 0.632
KNUPF85 4.51E-07 0.577
KNUPF86 3.87E-06 0.612
Seed hardness KNUPF2 2.15E-04 0.548
KNUPF3 7.81E-06 0.597
KNUPF25 1.09E-09 0.798
KNUPF30 8.89E-09 0.686
KNUPF60 8.11E-15 0.834
KNUPF80 3.12E-05 0.563
KNUPF81 7.17E-06 0.523
KNUPF85 6.35E-11 0.749
Seed size KNUPF2 5.05E-05 0.592
KNUPF3 4.11E-08 0.716
KNUPF25 1.40E-07 0.715
KNUPF30 2.48E-07 0.613
KNUPF60 4.74E-13 0.790
KNUPF80 4.71E-08 0.727
KNUPF85 8.33E-13 0.805
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p < 0.01, R2: > 0.5

Genetic relationships among 38 Perilla accessions determined using SSR markers

A phylogenetic tree constructed using UPGMA revealed that the 38 Perilla accessions clustered into three major groups with a genetic similarity of 42.4% (Fig. 4). Group I included 18 accessions of cultivated var. frutescens; Group II comprised 11 accessions, consisting of 10 accessions of weedy var. frutescens and one accession of weedy var. crispa; and Group III included 9 accessions, consisting of one accession of weedy var. frutescens and 8 accessions of weedy var. crispa. The clustering patterns from our results allowed a clear distinction to be made between the cultivated var. frutescens and related weedy type accessions that were in agreement with their morphological characteristics, such as leaf color, seed hardness, and seed size, except for two weedy type accessions (22, 37). To better understand the classification of cultivated and weedy-type accessions using SSR markers associated with seed characteristics, we also performed UPGMA dendrogram analysis for the 38 Perilla accessions using SSR markers related to the SGR, SH, and SS traits (Fig. 5). In the case of SSR markers related to the SGR trait, a UPGMA dendrogram was constructed using a total of six SSR markers (Fig. 5a), and the 38 Perilla accessions were clustered into two major groups with 33.1% genetic similarity. Group I included 18 accessions of cultivated var. frutescens, while Group II consisted of 20 accessions comprising 11 accessions of weedy var. frutescens and 9 accessions of weedy var. crispa. Group II was further divided into three subgroups with genetic similarity of 45.2%. Group II-1 included seven accessions of weedy var. frutescens and two accessions of weedy var. crispa. Group II-2 included one accession of weedy var. frutescens and seven accessions of weedy var. crispa. Group II-3 consisted of only three accessions of weedy var. frutescens (Fig. 5a). In the case of SSR markers related to the SH trait, a UPGMA dendrogram was constructed using a total of eight SSR markers (Fig. 5b). This showed that the 38 Perilla accessions were clustered into two major groups with 34.0% genetic similarity. Group I included 18 accessions of cultivated var. frutescens. Group II comprised 20 accessions consisting of 11 weedy var. frutescens accessions and 9 weedy var. crispa accessions. Group II was further divided into two subgroups with genetic similarity of 44.0%. Group II-1 included seven accessions of weedy var. frutescens and two accessions of weedy var. crispa. Group II-2 included four accessions of weedy var. frutescens and seven accessions of weedy var. crispa (Fig. 5b). In the case of SSR markers related to the SS trait, a UPGMA dendrogram was constructed using a total of seven SSR markers (Fig. 5c). This revealed that the 38 Perilla accessions clustered into two major groups with genetic similarity of 32.0%. Group I comprised 18 accessions of cultivated var. frutescens. Group II included 20 accessions consisting of 11 weedy var. frutescens accessions and 9 weedy var. crispa accessions. Group II was further divided into two subgroups with genetic similarity of 35.5%. Group II-1 comprised seven accessions of weedy var. frutescens and two accessions of weedy var. crispa. Group II-2 included four accessions of weedy var. frutescens and seven accessions of weedy var. crispa (Fig. 5c). The results of this study showed that the selected SSR markers associated with each seed trait were clearly divided between cultivated var. frutescens and related weedy-type accessions.

Fig. 4.

Fig. 4

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UPGMA dendrogram of 38 accessions of cultivated var. frutescens and related weedy types based on 29 SSR markers. ○: accessions of cultivated var. frutescens, ●: accessions of weedy var. frutescens, ▲: accessions of weedy var. crispa

Fig. 5.

Fig. 5

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UPGMA dendrogram of 38 accessions of cultivated var. frutescens and related weedy types based on SSR markers related to three seed characteristics: SGR (a), SH (b), and SS (c). ○: accessions of cultivated var. frutescens, ●: accessions of weedy var. frutescens, ▲: accessions of weedy var. crispa

Discussion

Domestication is an evolutionary process through which domesticated plants become morphologically (seed size, plant architecture, dispersal mechanisms, etc.) and physiologically (timing of seed germination or ripening, etc.) divergent from their wild ancestors (Schwanitz 1966; Harlan 1992). In this evolutionary process through domestication, plant species become more compatible with farming methods by adapting to human control and being bred in human-manipulated environments. For example, in the process of increasing seed size, selection pressure may not have come from conscious selection by farmers for larger seeds for consumption; instead, deeper seed burial associated with agriculture would have selected for larger seeds that would germinate more effectively and produce larger, more vigorous seedlings with greater fitness (Harlan et al. 1973; Cunniff et al. 2014; Turnbull et al. 1999).

In our study, we used Perilla SSR primers to determine the genetic diversity, genetic relationships, and population structure of 38 Perilla accessions and for association analysis; these accessions exhibited different and contrasting seed characteristics (high or low seed germination rate, large or small seed, or soft or hard seed traits) between cultivated var. frutescens and related weedy types. In the study of genetic variations among these accessions, 29 SSR markers were found to be useful molecular markers for the study of genetic diversity and genetic relationships in the 38 Perilla accessions. A total of 141 alleles with 29 SSRs were detected as segregating alleles in the 38 accessions of cultivated var. frutescens and related weedy types, which yielded an average of 4.86 alleles per locus (Table 2). Thus, the high allele numbers found in our study reflect the utility of SSR markers for determining the unique genotypes of individual accessions of cultivated var. frutescens and related weedy types, which should prove useful for detecting SSR markers associated with seed characteristics among accessions of cultivated var. frutescens and related weedy types. Furthermore, in the dendrogram analysis, the phylogenetic tree constructed using UPGMA revealed that the accessions of cultivated var. frutescens could be clearly distinguished from accessions of weedy var. frutescens and var. crispa by using SSR markers. This was expected based on the different morphological characteristics, such as different seed and leaf traits, of the two varieties of Perilla crop and their weedy types (Lee and Ohnishi 2001). However, although most accessions of weedy var. frutescens and var. crispa were discriminated well by these SSR markers, two weedy accessions (22, 37) were not clearly identified. These exceptional accessions can probably be considered hybrids resulting from natural crosses between weedy types, or the weedy types of var. frutescens and var. crispa might have originated from multiple sources, as previously reported by Lee and Ohnishi (2001, 2003) and Lee et al. (2002). Additional experiments will be needed to obtain more accurate results from these various materials.

Meanwhile, to select SSR markers associated with seed characteristics among accessions of cultivated var. frutescens and related weedy types, we analyzed SMTAs between 29 SSR markers and three seed characteristics (SGR, SH, and SS) in 38 Perilla accessions using TASSEL software. From the results, we identified 6, 8, and 7 SSR markers associated with the SGR, SH, and SS traits, respectively (Table 5). Among these SSR markers related to seed characteristics, KNUPF3, KNUPF25, and KNUPF60 were together associated with the SGR, SH, and SS traits (Table 5). In addition, 6 SSR markers, KNUPF2, KNUPF3, KNUPF25, KNUPF30, KNUPF60, and KNUPF80, were together associated with the SH and SS traits (Table 5). Therefore, these SSR markers are thought to be useful molecular markers for distinguishing seed-related characteristics in Perilla crop.

Furthermore, to better understand the genetic variability and genetic relationships between accessions of cultivated var. frutescens and related weedy types used in this study, we analyzed the phylogenetic relationships of 38 Perilla accessions using selected SSR markers related to three seed characteristics. In the case of 6 SSR markers related to the SGR trait, the 38 Perilla accessions were clustered into two major groups with 33% genetic similarity: Group I included 18 accessions of cultivated var. frutescens, and Group II comprised 20 accessions consisting of 11 weedy var. frutescens and 9 weedy var. crispa (Fig. 5a). As shown by the results, these SSR markers clearly distinguished accessions of cultivated var. frutescens and related weedy types, but they could not clearly distinguish the accessions of cultivated var. frutescens with high or low germination rates. The results suggest that, in the case of the germination rate, genes other than these SSR markers might be directly or indirectly involved in seed dormancy. Van der Schaar et al. (1997) suggested that seed dormancy is a complex quantitative trait under the influence of genetic, hormonal, physiological, and environmental factors. In addition, in the case of 8 SSR markers related to the SH trait, the 38 Perilla accessions were clustered into two major groups with 44% genetic similarity: Group I included 18 accessions of the cultivated var. frutescens, and Group II comprised 20 accessions consisting of 11 weedy var. frutescens and nine weedy var. crispa (Fig. 5b). Among the materials used in this study, all but one of the accessions of cultivated var. frutescens showed soft seeds, and all but one of the accessions of weedy var. frutescens and weedy var. crispa showed hard seeds. With the exception of these two accessions (3, 28), the SSR markers were considered useful markers for clearly distinguishing seed characteristics between the cultivated and weedy-type accessions. Finally, in the case of 7 SSR markers related to the SS trait, a dendrogram generated using UPGMA revealed that the 38 Perilla accessions clustered into two major groups with 32% genetic similarity (Fig. 5c): Group I included 18 accessions of cultivated var. frutescens, and Group II included 20 accessions consisting of 11 weedy var. frutescens and 9 weedy var. crispa accessions.

Based on the results, these SSR markers are considered to be useful markers for clearly distinguishing between accessions of cultivated var. frutescens and related weedy types, but they do not clearly distinguish accessions of weedy var. frutescens and weedy var. crispa. One finding of our study is that the selected SSR markers related to each seed trait clearly distinguish accessions of cultivated var. frutescens and related weedy types. Therefore, these SSR markers are considered to be useful molecular markers for clearly discriminating accessions of the cultivated and weedy types of Perilla crop. Furthermore, with the exception of a few accessions, the seed characteristics examined in our study (SGR, SH, and SS) were found to be useful morphological characteristics for discrimination between accessions of cultivated and weedy types of var. frutescens as well as accessions of cultivated var. frutescens and weedy var. crispa; this is consistent with previous reports by Lee and Ohnishi (2001) and Sa et al. (2013). Namely, they proposed that cultivated var. frutescens may be sufficiently differentiated from the weedy var. frutescens, whereas cultivated var. crispa may not yet be sufficiently differentiable from weedy var. crispa (Lee and Ohnishi 2001; Sa et al. 2013). In addition, correlation analysis of the three seed characteristics of the 38 accessions of cultivated var. frutescens and related weedy types showed that the combination of SGR and SS (0.811**) had a comparatively higher positive correlation coefficient than the other combinations. The combinations of SGR and SH (− 0.706**) and SS and SH (− 0.899**) showed higher negative correlations (Table 3). Therefore, these seed traits of Perilla accessions are thought to be useful morphological traits for discrimination between cultivated and weedy types of Perilla crop and for segregating populations for association mapping.

Meanwhile, genomic information for the selected Perilla SSR markers associated with seed characteristics is still lacking for Perilla crop, which makes it difficult to compare genetic characteristics. In future, if studies such as genome analysis are actively conducted in Perilla crop, it is expected that specific analysis of the characteristics of selected Perilla SSR markers will become possible. Also, the results of this study should provide useful information for understanding the patterns of seed variability of Perilla, which depend on the cultivation process and are related with seed characteristics. Thus, these results are very important for understanding the seed characteristics of Perilla crop and related weedy types; they may also provide an opportunity for effective selection and utilization of existing accessions and allow Perilla breeders to improve crop seed quality through MAS breeding programs.

Conclusion

In our study, to select the SSR markers associated with seed characteristics among accessions of cultivated var. frutescens and related weedy types, we analyzed marker-trait associations between 29 SSR markers and three seed characteristics (SGR, SH, and SS) in 38 Perilla accessions using TASSEL software. From the results, we identified 6, 8, and 7 SSR markers associated with SGR, SH, and SS, respectively. Among these SSR markers related to seed characteristics, KNUPF3, KNUPF25, and KNUPF60 were together associated with the SGR, SH, and SS traits. In addition, six SSR markers, KNUPF2, KNUPF3, KNUPF25, KNUPF30, KNUPF60, and KNUPF80, were together associated with the SH and SS traits. Therefore, these SSR markers are thought to be useful molecular markers for distinguishing seed-related characteristics in Perilla crop. The phylogenetic tree constructed using UPGMA revealed that accessions of cultivated var. frutescens could be clearly distinguished from weedy type accessions of var. frutescens and var. crispa by using 29 SSR markers. In addition, the selected SSR markers related to the three seed characteristics clearly distinguished accessions of cultivated var. frutescens and related weedy types. Although wild Perilla species have not been found in East Asia, the results of this study are expected to provide useful information for understanding the patterns of seed variability of cultivated var. frutescens and related weedy types.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary material 1 (DOCX 984 kb) (984.1KB, docx)

Acknowledgements

This study was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science, and Technology (#2016R1D1A1B01006461), and the Cooperative Research Program for Agriculture Science and Technology Development (Project Nos. PJ014227032020 and PJ0142272020; PJ0151832020), Rural Development Administration, Republic of Korea.

Authors’ contributions

JKL wrote the manuscript and performed the experiments. YJH and KJS designed the experiment, analyzed the data, and helped to draft the manuscript. All authors read and approved the final manuscript.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Footnotes

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Ye Ju Ha and Kyu Jin Sa have contributed equally to this work.

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