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Genetic diversity and population structure analysis in Perilla crop and their weedy types from northern and southern areas of China based on simple sequence repeat (SSRs)

Abstract

Introduction

Identification of genetic variation is an essential ability for the long-term success of breeding programs and maximizes the use of germplasm resources. In East Asia, China has a long history of the cultivation of Perilla crop, but there has been little research on the genetic diversity and genetic relationships among accessions of Perilla crop and their weedy types.

Objectives

To better understand the genetic variations of the cultivated and weedy types of Perilla crop in China, the 91 accessions were evaluated for genetic diversity by 21 simple sequence repeat (SSR) markers.

Methods

SSR amplifications were conducted in a total volume of 20 µL, consisting of 20 ng genomic DNA, 1X PCR buffer, 0.5 µM forward and reverse primers, 0.2 mM dNTPs, and 1 U Taq polymerase. Power Marker version 3.25 was applied to obtain the information on the number of alleles, allele frequency, major allele frequency, gene diversity (GD), and polymorphic information content (PIC). The similarity matrix was used to construct an unweighted pair group method with arithmetic mean dendrogram by the application of SAHN-Clustering from NTSYS-pc.V.2.1.

Results

A total of 147 alleles were identified with an average of 7 alleles per locus. The average values of PIC and GD were 0.577 and 0.537, respectively. The genetic diversity level of accessions from Northern China was lower than accessions from Southern China. The genetic diversity level and PIC values for accessions of var. crispa were the highest. For accessions of cultivated var. frutescens, genetic diversity in Southern China was higher than that in Northern China.

Conclusion

Most cultivated Perilla accessions were clearly separated from weedy Perilla accessions, but there was no clear geographic structure between cultivated Perilla crop and weedy types based on their regional distribution. This study demonstrated the utility of SSR analysis for performing genetic and population analysis of cultivated and weedy types of Perilla accessions in China.

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References

  • Ali ML, Rajewski JF, Baenziger PS, Gill KS, Eskridge KM, Dweikat I (2008) Assessment of genetic diversity and relationship among a collection of US sweet sorghum germplasm by SSR markers. Mol Breed 21:497–509

    Article  CAS  Google Scholar 

  • Da Cunha C, Resende F, Zucchi M, Pinheiro J (2014) SSR-based genetic diversity and structure of garlic accessions from Brazil. Genetica 142:419–431

    Article  CAS  PubMed  Google Scholar 

  • Dice LR (1945) Measures of the amount of ecologic association between species. Ecology 26:297–302

    Article  Google Scholar 

  • Evanno G, Regnaut S, Goudet J (2005) Detecting the number of clusters of individuals using the software STRUCTURE: a simulation study. Mol Ecol 14:2611–2620

    Article  CAS  PubMed  Google Scholar 

  • Ganapathy KN, Gnanesh BN, Byre Gowda M, Venkatesha SC, Gomashe SS, Channamallikarjuna V (2011) AFLP analysis in pigeonpea (Cajanus cajan (L.) Millsp.) revealed close relationship of cultivated genotypes with some of its wild relatives. Genet Resour Crop Evol 58:837–847

    Article  Google Scholar 

  • Hamza S, Hamida WB, Rebai A, Harrabi M (2004) SSR-based genetic diversity assessment among Tunisian winter barley and relationship with morphological traits. Euphytica 135:107–118

    Article  CAS  Google Scholar 

  • Hu Y, Sun LW, Neo MC, Zhang YX, Wen CX, Xie XL, Liu YJ (2010) Primary identifications and palynological observations of Perilla L. in China. J Syst Evol 48:133–145

    Article  Google Scholar 

  • Kwon SJ, Lee JK, Kim NS, Yu JW, Dixit A, Cho EG, Park YJ (2005) Isolation and characterization of SSR markers in Perilla frutescens Britt. Mol Ecol Notes 5:454–456

    Article  CAS  Google Scholar 

  • Lai JJ, Li ZZ, Man YP, Lei R, Wang YC (2015) Genetic diversity of five wild Actinidia arguta populations native to China as revealed by SSR markers. Sci Hortic 191:101–107

    Article  Google Scholar 

  • Lee JK, Kim NS (2007) Genetic diversity and relationships of cultivated and weedy types of Perilla frutescens collected from East Asia revealed by SSR markers. Korean J Breed Sci 39:491–499

    Google Scholar 

  • Lee JK, Ohnishi O (2001) Geographical differentiation of morphological characters among Perilla crops and their weedy types in East Asia. Breed Sci 51:247–255

    Article  Google Scholar 

  • Lee JK, Ohnishi O (2003) Genetic relationships among cultivated types of Perilla frutescens and their weedy types in East Asia revealed by AFLP markers. Genet Resour Crop Evol 50:65–74

    Article  CAS  Google Scholar 

  • Lee JK, Nitta M, Kim NS, Park CH, Yoon KM, Shin YB, Ohnishi O (2002) Genetic diversity of Perilla and related weedy types in Korea determined by AFLP analyses. Crop Sci 42:2161–2166

    Article  CAS  Google Scholar 

  • Li HL (1969) The vegetables of ancient China. Econ Bot 23:235–260

    Article  Google Scholar 

  • Liu K, Muse SV (2005) PowerMarker: an integrated analysis environment for genetic marker analysis. Bioinformatics 21:2128–2129

    Article  CAS  PubMed  Google Scholar 

  • Liu YX, Zhang WM (1998) Classification and resources distribution of Perilla. Chin Wild Plant Resour 17:1–4

    CAS  Google Scholar 

  • Liu YX, Zhang WM, Qian XS (1996) Research and utilization of Perilla. Chin Wild Plant Resour 3:24–27

    Google Scholar 

  • Ma SJ, Lee JK (2017) Morphological variation of two cultivated types of Perilla crop from different areas of China. Korean J Hortic Sci Biotechnol 35:510–522

    Google Scholar 

  • Ma SJ, Sa KJ, Hong TK, Lee JK (2017) Genetic diversity and population structure analysis in Perilla frutescens from Northern areas of China based on simple sequence repeats. Genet Mol Res 16(3):gmr16039746

    Article  Google Scholar 

  • Makino T (1961) Makino’s new illustrated flora of Japan. Hokuryukan, Tokyo (in Japanese)

    Google Scholar 

  • Meng F, Liu L, Peng M, Wang ZK, Wang C, Zhao Y (2015) Genetic diversity and population structure analysis in wild strawberry (Fragaria nubicola L.) from Motuo in Tibet Plateau based on simple sequence repeats (SSRs). Biochem Syst Ecol 63:113–118

    Article  CAS  Google Scholar 

  • Ni J, Colowit PM, Mackill DJ (2002) Evaluation of genetic diversity in rice subspecies using microsatellite markers. Crop Sci 42:601–607

    Article  CAS  Google Scholar 

  • Nitta M (2001) Origin Perilla crops and their weedy type. Ph.D. Thesis, Kyoto University, Kyoto, p 78

  • Nitta M, Ohnishi O (1999) Genetic relationships among two Perilla crops, shiso and egoma, and the weedy type revealed by RAPD markers. Jpn J Genet 74:43–48

    CAS  Google Scholar 

  • Nitta M, Lee JK, Ohnishi O (2003) Asian Perilla crops and their weedy forms: their cultivation, utilization and genetic relationships. Econ Bot 57:245–253

    Article  Google Scholar 

  • Nitta M, Lee JK, Kobayashi H, Liu D, Nagamine T (2005) Diversification of multipurpose plant, Perilla frutescens. Genet Resour Crop Evol 52:663–670

    Article  Google Scholar 

  • Palmer JD, Jansen RK, Michaels HJ, Chase MW, Manhart JR (1988) Chloroplast DNA variation and plant phylogeny. Ann Missouri Bot Gard 75:1180–1206

    Article  Google Scholar 

  • Park YJ, Dixit A, Ma KH, Lee JK, Lee MH, Chung CS, Nitta M, Okuno K, Kim TS, Cho EG, Rao VR (2008) Evaluation of genetic diversity and relationships within an on-farm collection of Perilla frutescens (L.) Britt. using microsatellite markers. Genet Resour Crop Evol 55:523–535

    Article  CAS  Google Scholar 

  • Park YJ, Lee JK, Kim NS (2009) Simple sequence repeat polymorphisms (SSRPs) for evaluation of molecular diversity and germplasm classification of minor crops. Molecules 14:4546–4569

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  • Park JY, Ramekar RV, Sa KJ, Lee JK (2015) Genetic diversity, population structure, and association mapping of biomass traits in maize with simple sequence repeat markers. Genes Genom 37:725–735

    Article  CAS  Google Scholar 

  • Powell W, Morgante M, Andre C, Hanafey M, Vogelet J, Tingey S, Rafalski A (1996) The comparison of RFLP, RAPD, AFLP and SSR (microsatellite) markers for germplasm analysis. Mol Breed 2:225–238

    Article  CAS  Google Scholar 

  • Prasad M, Varshney RK, Roy JK, Balyan HS, Gupta PK (2000) The use of microsatellites for detecting DNA polymorphism, genotype identification and genetic diversity in wheat. Theor Appl Genet 100:584–592

    CAS  Google Scholar 

  • Pritchard JK, Wen W (2003) Documentation for STRUCTURE software: version 2. Available from http://pritch.bsd.uchicago.edu

  • Rao VR, Hodgkin T (2002) Genetic diversity and conservation and utilization of plant genetic resources. Plant Cell Tissue Organ Cult 68:1–19

    Article  Google Scholar 

  • Rohlf FJ (1998) NTSYS-pc: numerical taxonomy and multivariate analysis system. Version: 2.02. Exter Software, Setauket

    Google Scholar 

  • Sa KJ, Kim JA, Lee JK (2012) Comparison of seed characteristics between the cultivated and the weedy types of Perilla species. Hortic Environ Biotechnol 53(4):310–315

    Article  Google Scholar 

  • Sa KJ, Choi SH, Ueno M, Park KC, Park YJ, Ma KH, Lee JK (2013) Identification of genetic variations of cultivated and weedy types of Perilla species in Korea and Japan using morphological and SSR markers. Genes Genom 35:649–659

    Article  CAS  Google Scholar 

  • Sa KJ, Choi SH, Ueno M, Lee JK (2015) Genetic diversity and population structure in cultivated and weedy types of Perilla in East Asia and other countries as revealed by SSR markers. Hortic Environ Biotechnol 56:524–534

    Article  Google Scholar 

  • Senior ML, Murphy JP, Goodman MM, Stuber C (1998) Utility of SSRs for determining genetic similarities and relationships in maize using an agarose gel system. Crop Sci 38:1088–1098

    Article  Google Scholar 

  • Song JY, Lee JR, Oh S, Kim CY, Bae CH, Lee GA, Ma KH, Choi YM, Park HJ, Lee MC (2012) Assessment of genetic diversity and fatty acid composition of Perilla (Perilla frutescens var. frutescens) germplasm. Korean J Plant Res 25(6):762–772

    Article  Google Scholar 

  • Tan M, Yan M, Wang L, Wang L, Yan X (2012) Research progress on Perilla frutescens. Chin J Oil Crop Sci 34:225–231

    CAS  Google Scholar 

  • Wang S, Guo F (2012) Genetic diversity of Perilla frutescens from Yunnan based on ISSR. Chin J Oil Crop Sci 34:372–376

    CAS  Google Scholar 

  • Wang R, Yu Y, Zhao J, Shi Y, Song Y, Wang T, Li Y (2008) Population structure and linkage disequilibrium of a mini core set of maize inbred lines in China. Theor Appl Genet 117:1141–1153

    Article  CAS  PubMed  Google Scholar 

  • Wei Z, Li H, Feng B, Lin T, Lin W (2015) Studies on the germplasm resource investigation and utilization of Perilla frutescens (L.) in Guizhou. Seed 34:58–60

    Google Scholar 

  • Xia XC, Reif JC, Melchinger AE, Frisch M, Hoisington DA, Beck D, Pixley K, Warburton ML (2005) Genetic diversity among CIMMYT maize inbred lines investigated with SSR markers: II. Subtropical, tropical midaltitude, and highland maize inbred lines and their relationships with elite U.S. and European maize. Crop Sci 45:2573–2582

    Article  CAS  Google Scholar 

  • Yook MJ, Lim SH, Song JS, Kim JW, Zhang CJ, Lee EJ, Ibaragi Y, Lee GJ, Nah G, Kim DS (2014) Assessment of genetic diversity of Korean Miscanthus using morphological traits and SSR markers. Biomass Bioenergy 66:81–92

    Article  CAS  Google Scholar 

  • Yu H, Qiu JF, Ma LJ, Hu YJ, Li P, Wang JB (2016) Phytochemical and phytopharmacological review of Perilla frutescens L. (Labiatae), a traditional edible-medicinal herb in China. Food Chem Toxicol. https://doi.org/10.1016/j.fct.2016.11.023

    Article  PubMed  Google Scholar 

  • Zeven AC, de-Wet JMJ (1982) Dictionary of cultivated plants and their regions of diversity. Centre for Agricultural Publishing and Documentation, Wageningen, p 34

    Google Scholar 

  • Zhang X, Wu W, Zheng YL, Chen L, Cai Q (2009) Essential oil variations in different Perilla L. accessions: chemotaxonomic implications. Plant Syst Evol 281:1–10

    Article  CAS  Google Scholar 

  • Zhang JB, Yan JJ, Zhang YW, Ma X, Bai SQ, Wu YQ, Dao ZX, Li DX, Zhang CB, Zhang Y, You MH, Yang FY, Zhang J (2013) Molecular insights of genetic variation in Erianthus arundinaceus Populations native to China. PLoS One 11:e80388. https://doi.org/10.1371/journal.pone.0080388

    Article  CAS  Google Scholar 

  • Zhang J, Chen T, Wang J, Chen Q, Luo Y, Zhang Y, Tang H (2016) Genetic diversity and population structure in cherry [Cerasus pseudocerasus (Lindl). G. Don] along Longmenshan Fault Zones in China with newly developed SSR markers. Sci Hortic 212:11–19

    Article  Google Scholar 

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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 a Grant from the Regional Subgenebank Support Program (PJ012923012017) of the Rural Development Administration (RDA).

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Correspondence to Ju Kyong Lee.

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Ma, S.J., Sa, K.J., Hong, TK. et al. Genetic diversity and population structure analysis in Perilla crop and their weedy types from northern and southern areas of China based on simple sequence repeat (SSRs). Genes Genom 41, 267–281 (2019). https://doi.org/10.1007/s13258-018-0756-3

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  • DOI: https://doi.org/10.1007/s13258-018-0756-3

Keywords

  • Perilla frutescens
  • Genetic similarity
  • SSR marker
  • Geographical location
  • Polymorphic information content
  • Population structure