Search
2024 Volume 3
Article Contents
ARTICLE   Open Access    

Assessment of genetic diversity and identification of core germplasm of Pueraria in Guangxi using SSR markers

  • # Authors contributed equally: Pingli Shi, Yun Zhou

More Information
  • 272 individuals of Pueraria species in Guangxi were divided into two main clusters in all analysis.

    118 alleles were identified and 112 alleles were polymorphic.

    Overall genetic diversity was moderate.

    A core collection of 20 Pueraria accessions was constructed when the samples collected reached 7.35% (20/272).

  • Pueraria, extensively cultivated in Guangxi, China, stands as a pivotal commercial crop and a valuable supplement for human health. Despite its significance, the core germplasm and genetic diversity within Guangxi's Pueraria populations remain largely unexplored. This study delves into the genetic diversity of a comprehensive collection of 272 Pueraria germplasm accessions from Guangxi, utilizing 23 simple sequence repeat (SSR) markers. The average number of SSR alleles per locus was 5.13, ranging from 2 to 11, with four primers (PtSSR121, PtSSR196, PtSSR155, and PtSSR222) consistently producing at least two polymorphic bands, while PtSSR122 yielded an impressive 11 polymorphic bands. The analysis revealed 118 alleles, 112 of which were polymorphic. The average gene flow (Nm) was estimated at 1.7690, and the average predicted heterozygosity per location was 0.1841. Principal component and STRUCTURE cluster analyses corroborated the division of the 272 accessions into two main clusters. However, no significant statistical correlation was observed between geographic and genetic distances. The study identified a moderate level of genetic diversity. A core collection comprising 20 Pueraria accessions that encompass 105 alleles was proposed. These findings provide a theoretical basis for the strategic conservation of Pueraria's genetic resources, laying the groundwork for future breeding programs.
    Graphical Abstract
  • 加载中
  • Supplemental Table S1 Details of sample location of Pueraria species in the present study.
    Supplemental Fig. S1 SSR fingerprinting map analysis of Pueraria germplasms.
    Supplemental Fig. S2 SSR fingerprinting map analysis of 20 Pueraria accessions of core germplasms.
  • [1]

    Wang SG, Zhang SM, Wang SP, Gao P, Dai L. 2020. A comprehensive review on Pueraria: Insights on its chemistry and medicinal value. Biomedicine & Pharmacotherapy 131:110734

    doi: 10.1016/j.biopha.2020.110734

    CrossRef   Google Scholar

    [2]

    Shang X, Cao S, Xiao L, Yan H, Wang Y, et al. 2020. Investigation and collection of Pueraria germplasm resources in Guangxi. Journal of Plant Genetic Resources 21(5):1301−7

    doi: 10.13430/j.cnki.jpgr.20200301002

    CrossRef   Google Scholar

    [3]

    Shang X, Yan H, Cao S, Xiao L, Wang Y, et al. 2019. Genetic diversity analysis of Pueraria in Guangxi based on SCoT markers. Journal of Nuclear Agricultural Sciences 33(7):1311−17

    doi: 10.11869/j.issn.100-8551.2019.07.1311

    CrossRef   Google Scholar

    [4]

    Chen C, Zheng L, Ma Q Zhou WB, Lu Y, et al. 2019. Impacts of domestication on population genetics of a traditional Chinese medicinal herb, Atractylodes macrocephala (Asteraceae). Journal of Systematics and Evolution 57(3):222−33

    doi: 10.1111/jse.12446

    CrossRef   Google Scholar

    [5]

    Chen S, Wu T, Xiao L, Ning D, Li P. 2020b. Genetic diversity of Juglans sigillata Dode germplasm in Yunnan Province, China, as revealed by SSRs. Plant Genetic Resources 18(6):417−26

    doi: 10.1017/S1479262120000441

    CrossRef   Google Scholar

    [6]

    Ambati D, Phuke RM, Vani V, Sai Prasad SV, Singh JB, et al. 2020. Assessment of genetic diversity and development of core germplasm in durum wheat using agronomic and grain quality traits. Cereal Research Communications 48:375−82

    doi: 10.1007/s42976-020-00050-z

    CrossRef   Google Scholar

    [7]

    Chen C, Chu Y, Ding C, Su X, Huang Q. 2020a. Genetic diversity and population structure of black cottonwood (Populus deltoides) revealed using simple sequence repeat markers. BMC Genetics 21(1):1−12

    doi: 10.21203/rs.2.10562/v3

    CrossRef   Google Scholar

    [8]

    Pal S, Revadi M, Thontadarya RN, Reddy DCL, Varalakshmi B, et al. 2020. Understanding genetic diversity, population structure and development of a core collection of Indian accessions of watermelon (Citrullus lanatus (Thunb.) Matsum. and Nakai). Plant Genetic Resources 18(5):359−368

    doi: 10.1017/S1479262120000386

    CrossRef   Google Scholar

    [9]

    Jing X, Xu L, Chen JY, Zeng XQ, Huang ZC, et al. 2010. Genetic diversity of arrowroot (Pueraria L.) varieties revealed by RAPD analysis in Chongqing area. Chinese Agricultural Science Bulletin 26(24):80−82

    Google Scholar

    [10]

    Chen D, Peng R, Li L, Zhang X, Wang Y. 2011. Analysis of genetic relationships of Pueraria thomsonii based on SRAP markers. China Journal of Chinese Materia Medica 36(5):538−41

    doi: 10.4268/cjcmm20110504

    CrossRef   Google Scholar

    [11]

    Guo Y, Cheng C, Huang J, Yang X, Lu J, et al. 2013. ISSR analysis of genetic relationships in Radix Puerariae from different original place. Popular Science & Technology 15(4):134−36

    doi: 10.3969/j.issn.1008-1151.2013.04.053

    CrossRef   Google Scholar

    [12]

    Zhou J, Jie Y, Du X, Xing H, Xiong L. 2013. RAPD analysis on genetic relationship of Kudzu germplasm resources. Crop Research 27(4):347−50

    doi: 10.3969/j.issn.1001-5280.2013.04.12

    CrossRef   Google Scholar

    [13]

    Yuan C, Zhong W, Gong Y, Pu D, Ji P, et al. 2017. Genetic diversity and trait association analysis of Pueraria lobata resources. Journal of Plant Genetic Resources 18(2):233−41

    doi: 10.13430/j.cnki.jpgr.2017.02.009

    CrossRef   Google Scholar

    [14]

    Zhou R, Zhou J, Nan T, Jiang C, Duan HY, et al. 2019. Analysis of genomic SSRs in Pueraria lobata and P. thomsonii and establishment of DNA identity card for different germplasms of P. thomsonii of Jiangxi province. China Journal of Chinese Materia Medica 44(17):3615−21

    doi: 10.19540/j.cnki.cjcmm.20190527.106

    CrossRef   Google Scholar

    [15]

    Wang W, Wu B, Liu Z, Zhou L, Sun X, et al. 2021. Development of EST-SSRs from the ark shell (Scapharca broughtonii) transcriptome and their application in genetic analysis of four populations. Genes & Genomics 43:669−77

    doi: 10.1007/s13258-021-01090-3

    CrossRef   Google Scholar

    [16]

    Kim HR, Sa KJ, Nam-Gung M, Park KJ, Ryu SH, et al. 2021. Genetic characterization and association mapping in near-isogenic lines of waxy maize using seed characteristics and SSR markers. Genes & Genomics 43:79−90

    doi: 10.1007/s13258-020-01030-7

    CrossRef   Google Scholar

    [17]

    Xiao L, Shang X, Cao S, Xie X, Zeng W, et al. 2019. Utilization of simple sequence repeat (SSR) markers developed from a de novo transcriptome assembly in Pueraria thomsonii benth. Acta Botanica Boreali-occidentalia Sinica 39(1):59−67

    doi: 10.7606/j.issn.1000-4025.2019.01.0059

    CrossRef   Google Scholar

    [18]

    Pritchard JK, Stephens M, Donnelly P. 2000. Inference of population structure using multilocus genotype data. Genetics 155(2):945−59

    doi: 10.1093/genetics/155.2.945

    CrossRef   Google Scholar

    [19]

    Jakobsson M, Rosenberg NA. 2007. CLUMPP: a cluster matching and permutation program for dealing with label switching and multimodality in analysis of population structure. Bioinformatics 23(14):1801−6

    doi: 10.1093/bioinformatics/btm233

    CrossRef   Google Scholar

    [20]

    Evanno GS, Regnaut S, Goudet J. 2005. Detecting the number of clusters of individuals using the software structure: a simulation study. Molecular Ecology 14(8):2611−20

    doi: 10.1111/j.1365-294X.2005.02553.x

    CrossRef   Google Scholar

    [21]

    Earl DA, vonHoldt BM. 2012. Structure harvester: a website and program for visualizing structure output and implementing the evanno method. Conservation Genetics Resources 4(2):359−61

    doi: 10.1007/s12686-011-9548-7

    CrossRef   Google Scholar

    [22]

    Rohlf FJ. 2000. NTSYS pc2.1: Numerical taxonomy and multivariate analysis system version 2.1. New York: Applied Biostatistics Inc.

    [23]

    Thachuk C, Crossa J, Franco J, Dreisigacker S, Warburton M, et al. 2009. Core hunter: an algorithm for sampling genetic resources based on multiple genetic measures. BMC Bioinformatics 10(1):243

    doi: 10.1186/1471-2105-10-243

    CrossRef   Google Scholar

    [24]

    Verma H, Borah JL, Sarma RN. 2019. Variability assessment for root and drought tolerance traits and genetic diversity analysis of rice germplasm using SSR markers. Scientific Reports 9(1):16513

    doi: 10.1038/s41598-019-52884-1

    CrossRef   Google Scholar

    [25]

    Gómez-Rodríguez MV, Beuzon C, González-Plaza JJ, Fernández-Ocaña AM. 2021. Identification of an olive (Olea europaea L.) core collection with a new set of SSR markers. Genetic Resources and Crop Evolution 68(1):117−33

    doi: 10.1007/s10722-020-00971-y

    CrossRef   Google Scholar

    [26]

    Adhikari S, Joshi A, Kumar A, Singh NK, Jaiswal JP, et al. 2022. Revealing the genetic diversity of teosinte introgressed maize population by morphometric traits and microsatellite markers. Journal of Plant Biochemistry and Biotechnology 31:720−38

    doi: 10.1007/s13562-021-00710-z

    CrossRef   Google Scholar

    [27]

    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 & Genomics 37:725−35

    doi: 10.1007/s13258-015-0309-y

    CrossRef   Google Scholar

    [28]

    Pappert RA, Hamrick JL, Donovan LA. 2000. Genetic variation in Pueraria lobata (Fabaceae), an introduced, clonal, invasive plant of the southeastern United States. American Journal of Botany 87:1240−45

    doi: 10.2307/2656716

    CrossRef   Google Scholar

    [29]

    Sruthi K, Divya B, Senguttuvel P, Revathi P, Kemparaju KB, et al. 2020. Evaluation of genetic diversity of parental lines for development of heterotic groups in hybrid rice (Oryza sativa L.). Journal of Plant Biochemistry and Biotechnology 29:236−52

    doi: 10.1007/s13562-019-00529-9

    CrossRef   Google Scholar

    [30]

    Heider B, Fischer E, Berndl T, Schultze-Kraft, R. 2007. Analysis of genetic variation among accessions of Pueraria montana (Lour.) Merr. var. lobata and Pueraria phaseoloides (Roxb.) Benth. based on RAPD markers. Genetic Resources and Crop Evolution 54:529−42

    doi: 10.1007/s10722-006-0009-1

    CrossRef   Google Scholar

    [31]

    Ji B, Pei L, Chen S, Dong C, Feng W. 2014. RAPD analysis of germplasm resource in Pueraria lobata. Chinese Journal of Experimental Traditional Medical Formulae 20(6):56−59

    doi: 10.13422/j.cnki.syfjx.2014160056

    CrossRef   Google Scholar

    [32]

    Oh JS, Sa KJ, Hyun DY, Cho GT, Lee JK. 2020. Assessment of genetic diversity and population structure among a collection of Korean Perilla germplasms based on SSR markers. Genes & Genomics 42(12):1419−30

    doi: 10.1007/s13258-020-01013-8

    CrossRef   Google Scholar

    [33]

    Jewett DK, Jiang CJ, Britton KO, Sun JH, Tang J. 2003. Characterizing specimens of kudzu and related taxa with RAPDs. Castanea 68:254−60

    Google Scholar

    [34]

    Sun JH, Li ZC, Jewett DK, Britton KO, Ye WH, et al. 2005. Genetic diversity of Pueraria lobata (kudzu) and closely related taxa as revealed by inter-simple sequence repeat analysis. Weed Research 45:255−60

    doi: 10.1111/j.1365-3180.2005.00462.x

    CrossRef   Google Scholar

    [35]

    Hoffberg SL, Bentley KE, Lee JB, Myhre KE, Iwao K, et al. 2015. Characterization of 15 microsatellite loci in kudzu (Pueraria montana var. lobata) from the native and introduced ranges. Conservation Genetics Resources 7:403−5

    doi: 10.1007/s12686-014-0381-7

    CrossRef   Google Scholar

    [36]

    Bentley KE, Mauricio R. 2016. High degree of clonal reproduction and lack of large-scale geographic patterning mark the introduced range of the invasive vine, kudzu (Pueraria montana var. lobata) in North America. American Journal of Botany 103:1499−507

    doi: 10.3732/ajb.1500434

    CrossRef   Google Scholar

    [37]

    Haynsen MS. 2018. Population Genetics of Pueraria montana var. lobata. Thesis. The George Washington University, US.

    [38]

    Ellstrand NC, Roose ML. 1987. Patterns of genotypic diversity in clonal plant species. American Journal of Botany 74:123−31

    doi: 10.1002/j.1537-2197.1987.tb08586.x

    CrossRef   Google Scholar

    [39]

    Halkett FJ, Simon JC, Balloux FO. 2005. Tackling the population genetics of clonal and partially clonal organisms. Trends in Ecology & Evolution 20:194−201

    doi: 10.1016/j.tree.2005.01.001

    CrossRef   Google Scholar

    [40]

    Stilwell KL, Wilbur HM, Werth CR, Taylor DR. 2003. Heterozygote advantage in the American chestnut, Castanea dentata (Fagaceae). American Journal of Botany 90:207−13

    doi: 10.3732/ajb.90.2.207

    CrossRef   Google Scholar

    [41]

    Ge XJ, Liu MH, Wang WK, Schaal BA, Chiang TY. 2005. Population structure of wild bananas, Musa balbisiana, in China determined by SSR fingerprinting and cpDNA PCR-RFLP. Molecular Ecology 14(4):933−44

    doi: 10.1111/j.1365-294X.2005.02467.x

    CrossRef   Google Scholar

    [42]

    Kiran BU, Mukta N, Kadirvel P, Alivelu K, Senthilvel S, et al. 2017. Genetic diversity of safflower (Carthamus tinctorius L.) germplasm as revealed by SSR markers. Plant Genetic Resources 5(1):1−11

    doi: 10.1017/S1479262115000295

    CrossRef   Google Scholar

    [43]

    Singh M, Bisht IS, Kumar S, Dutta M, Bansal KC, et al. 2014. Global wild annual Lens collection: a potential resource for lentil genetic base broadening and yield enhancement. PLoS ONE 9:e107781

    doi: 10.1371/journal.pone.0107781

    CrossRef   Google Scholar

    [44]

    Nie X, Wang Z, Liu N, Song L, Yan B, et al. 2021. Fingerprinting 146 Chinese chestnut (Castanea mollissima Blume) accessions and selecting a core collection using SSR markers. Journal of Integrative Agriculture 20(5):1277−86

    doi: 10.1016/S2095-3119(20)63400-1

    CrossRef   Google Scholar

    [45]

    Liu F, Zhang N, Liu X, Yang Z, Jia H, et al. 2019. Genetic diversity and population structure analysis of Dalbergia Odorifera germplasm and development of a core collection using microsatellite markers. Genes 10(4):281

    doi: 10.3390/genes10040281

    CrossRef   Google Scholar

    [46]

    Liu Y, Geng Y, Xie X, Zhang P, Hou J, et al. 2020. Core collection construction and evaluation of the genetic structure of Glycyrrhiza in China using markers for genomic simple sequence repeats. Genetic Resources and Crop Evolution 67(7):1839−52

    doi: 10.1007/s10722-020-00944-1

    CrossRef   Google Scholar

    [47]

    Miyatake K, Shinmura Y, Matsunaga H, Fukuoka H, Saito T. 2019. Construction of a core collection of eggplant (Solanum melongena L.) based on genome-wide SNP and SSR genotypes. Breeding Science 69(3):498−502

    doi: 10.1270/jsbbs.18202

    CrossRef   Google Scholar

    [48]

    Li C, Wu J, Li Q, Yang Y, Zhang K. 2022. Development of simple sequence repeat markers from functional genes and establishment of molecular identity for tree peony. Journal of Plant Biochemistry and Biotechnology 31:22−36

    doi: 10.1007/s13562-021-00651-7

    CrossRef   Google Scholar

    [49]

    Wu DL, Thulin M. 2010. Flora of China. Vol. 10. Beijing: Science Press. pp. 244–48.

    [50]

    Lackey JA. 1977. A synopsis of the Phaseoleae (Leguminosae, Papilionoideae). Thesis. Iowa State University, US. https://doi.org/10.31274/rtd-180813-2256

    [51]

    van der Maesen LJG. 1985. Revision of the genus Pueraria DC. with some notes on Teyleria Backer (Leguminiosae). Thesis. Agricultural University of Wageningen, Netherlands. pp. 1–132

    [52]

    Stefanović S, Pfeil BE, Palmer JD, Doyle JJ. 2009. Relationships among phaseoloid legumes based on sequences from eight chloroplast regions. Systematic Botany 34:115−28

    doi: 10.1600/036364409787602221

    CrossRef   Google Scholar

    [53]

    Egan AN, Vatanparast M, Cagle W. 2016. Parsing polyphyletic Pueraria: Delimiting distinct evolutionary lineages through phylogeny. Molecular Phylogenetics and Evolution 104:44−59

    doi: 10.1016/j.ympev.2016.08.001

    CrossRef   Google Scholar

    [54]

    Zeng M, Yan J, Zhang H, Zheng S, Su Z. 2000. Classification and authentication of plant Pueraria DC in China using RAPD. Chinese Traditional and Herbal Drugs 31(8):620−22

    doi: 10.3321/j.issn:0253-2670.2000.08.034

    CrossRef   Google Scholar

    [55]

    Zeng M, Ma Y, Zheng S, Xu J, Di X. 2003. Studies on ribosomal DNA sequence analyses of Radix puerariae and its sibling species. Chinese Pharmaceutical Journal 38(3):173−75

    doi: 10.3321/j.issn:1001-2494.2003.03.005

    CrossRef   Google Scholar

    [56]

    Kimura M, Crow J. 1964. The number of alleles that can be maintained in a finite population. Genetics 49:725−38

    doi: 10.1093/genetics/49.4.725

    CrossRef   Google Scholar

    [57]

    Lewontin RC . 1972. The apportionment of human diversity. In Evolutionary Biology, eds: Dobzhansky T, Hecht MK, Steere WC. New York: Springer. pp. 381–98. https://doi.org/10.1007/978-1-4684-9063-3_14

  • Cite this article

    Shi P, Zhou Y, Shang X, Xiao L, Zeng W, et al. 2024. Assessment of genetic diversity and identification of core germplasm of Pueraria in Guangxi using SSR markers. Tropical Plants 3: e012 doi: 10.48130/tp-0024-0012
    Shi P, Zhou Y, Shang X, Xiao L, Zeng W, et al. 2024. Assessment of genetic diversity and identification of core germplasm of Pueraria in Guangxi using SSR markers. Tropical Plants 3: e012 doi: 10.48130/tp-0024-0012

Figures(5)  /  Tables(3)

Article Metrics

Article views(1449) PDF downloads(230)

ARTICLE   Open Access    

Assessment of genetic diversity and identification of core germplasm of Pueraria in Guangxi using SSR markers

Tropical Plants  3 Article number: e012  (2024)  |  Cite this article

Abstract: Pueraria, extensively cultivated in Guangxi, China, stands as a pivotal commercial crop and a valuable supplement for human health. Despite its significance, the core germplasm and genetic diversity within Guangxi's Pueraria populations remain largely unexplored. This study delves into the genetic diversity of a comprehensive collection of 272 Pueraria germplasm accessions from Guangxi, utilizing 23 simple sequence repeat (SSR) markers. The average number of SSR alleles per locus was 5.13, ranging from 2 to 11, with four primers (PtSSR121, PtSSR196, PtSSR155, and PtSSR222) consistently producing at least two polymorphic bands, while PtSSR122 yielded an impressive 11 polymorphic bands. The analysis revealed 118 alleles, 112 of which were polymorphic. The average gene flow (Nm) was estimated at 1.7690, and the average predicted heterozygosity per location was 0.1841. Principal component and STRUCTURE cluster analyses corroborated the division of the 272 accessions into two main clusters. However, no significant statistical correlation was observed between geographic and genetic distances. The study identified a moderate level of genetic diversity. A core collection comprising 20 Pueraria accessions that encompass 105 alleles was proposed. These findings provide a theoretical basis for the strategic conservation of Pueraria's genetic resources, laying the groundwork for future breeding programs.

    • Kudzu (Pueraria montana var. lobata (Ohwi) Maesen & S. M. Almeida) (2n = 2x = 22) is a semi-woody, perennial liana, that belongs to the Leguminosae family and is widely distributed throughout Asia, including China, Japan, Korea and other regions in Southeast Asia, as well as in North and South America. As an economic crop, it contains puerarin and other functional components and is used in the production of both pharmaceuticals and health foods. Pueraria montana var. thomsonii is another variety that shows higher starch content and thus is called starch kudzu. The roots of both P. montana var. lobata and P. montana var. thomsonii have been long used for treating fever, toxicosis, indigestion, and liver damage from alcohol abuse in traditional Chinese medicine[1], which was recorded in The Divine Husbandman's Classic of Materia Medica (Shen Nong Ben Cao Jing) compiled in the Eastern Han Dynasty (25−250 AD). China is probably the origin and distribution center of Pueraria species; however, for a long time, the identification and the breeding of germplasm resources has not received enough attention. Guangxi is a hotspot of Pueraria genetic resources in China. Kudzu is a traditional crop cultivated in Guangxi, with abundant germplasm resources at elevations of 100−199 m[2]. At present, the cultivation area of kudzu and starch kudzu in Guangxi accounts for 20% of the whole country[3]. However, the genetic diversity and core germplasm of the Pueraria species in Guangxi are not well understood.

      With the development of urban society and excessive mining, many germplasm resources are facing the risk of loss or extinction. Genetic diversity provides a basis for the improvement of the crop for different desirable traits, evolutionary capability, species survival, management of germplasm collections, and breeding programs[46]. Therefore, it is necessary to fully understand the genetic diversity and genetic information of Pueraria core germplasm resources of the representative individuals, which can protect key genetic resources and shorten the breeding process[7,8]. Most recently, RAPD (random amplified polymorphic DNA), ISSR (inter-simple sequence repeat), SRAP (sequence-related amplified polymorphic), SCoT (start condon targeted polymorphism), and SSR (simple sequence repeats) markers have been used to analyze the genetic diversity in Pueraria[3,914]. Genic-SSRs have the most advantages among these five markers because of the more comprehensive genetic information in the genome[15,16]. Genic-SSRs were used to evaluate the diversity of Pueraria, however, the population is just 44 lines[17]. Although genetic analysis of Pueraria on some accessions of Pueraria or some germplasm resources in Guangxi has been done[3], the core germplasm resource and the overall evaluation on the genetic diversity has not yet been systematically evaluated.

      Lack of systematic study of the genetic diversity and the core germplasm resource seriously restricts its efficient management, conservation and further utilization[5]. In the present study, 272 individuals of Pueraria collected in Guangxi were used to estimate the extent of genetic diversity and construct the core germplasm. The findings of this study will be utilized for conservation and management of genetic resources in Guangxi, association mapping, and traits-based kudzu breeding.

    • A total of 272 individuals of Pueraria were collected in Guangxi from September 2017 to April 2019 (Supplemental Table S1). Three to five fresh young leaves of each accession were collected and immediately frozen in liquid nitrogen and stored at −80 °C until DNA isolation.

      Total genomic DNA was extracted from young leaf tissue of individual representative plants of each accession using a Plant DNA Isolation Reagent Kit (TaKaRa, Dalian, China). We measured the concentration and purity of the total DNA using both 1% agarose gel electrophoresis and a Nanodrop instrument (UV-2700). The total DNA extracts were stored at −20 °C until required for experiments.

    • The final concentration of DNA was adjusted to 50 ng/μl for PCR reaction. Based on the transcriptome of P. montana var. lobata, 28 SSR primers were designed and scored in six Pueraria collections from 229 SSRs[17]. Ultimately, 23 polymorphic markers were chosen for genetic diversity analysis (Table 1). SSR amplification was carried out in a thermal cycler by Bio-Rad (MyCycler TM), in a final volume of 20 μl containing: 100 ng of genomic DNA, 10 μl of Taq DNA polymerase mix (TaKaRa, Dalian, China), and 10 μM each, forward and reverse non-fluorescent primers. The program used for PCR amplification was as follows: initial denaturation at 94 °C for 5 min; 30 cycles of denaturation at 94 °C for the 30 s, annealing at 50 °C for 30 s, extension at 72 °C for 30 s, and a final extension at 72 °C for 10 min. Amplified products were separated in 6% non-denaturing polyacrylamide gel electrophoresis (PAGE). The SSR markers amplified at sizes between 100 and 400 bp were converted into '0' and '1' codes denoting 'absence' and 'presence', respectively.

      Table 1.  Amplification results and polymorphism information of 23 SSR primers.

      No.Primer nameSequences (5'-3')Total number
      of lands
      Number of
      polymorphic bands
      Polymorphism
      rate (%)
      1PtSSR36Fw: CTGAGTCTCTGCAAAGCCCA1010100
      Rv: TGTCACTGTGCTCCAACTCC
      2PtSSR98Fw: CATTCGGACCTCCATACCCG111090.9
      Rv: CCGCATCCAACCCTGATCAA
      3PtSSR99Fw: GCTTTCCGCTGCTACCATTC77100
      Rv: GCAACCCCAATGCTTCACAG
      4PtSSR104Fw: CACCCTCCCACCACTACAAC33100
      Rv: GCAATGTCCTCCTCAGCTGT
      5PtSSR108Fw: AGCGTGCCCAACTCAGTTAA33100
      Rv: CGACGGAGAAGGAGGGAATG
      6PtSSR109Fw: CAACCTGGCTTCTGTTGTGC5480
      Rv: CTCTGAAACGCTGGGCAATG
      7PtSSR121Fw: ACACTCAACACTCCACCACC3266.67
      Rv: AGGGTTTCCACCTTGAACCG
      8PtSSR122Fw: GGGGTTTCTTCTCGGCTGAA1111100
      Rv: CACCCCCTTCACGCTTCATA
      9PtSSR130Fw: ATCAGTGTCTACGTGGGGGA5480
      Rv: CACTGCAGCCACAACAACAT
      10PtSSR135Fw: GATCCGCACCCTATCTGTGG88100
      Rv: CTGCGACAGCTCCGATCTTA
      11PtSSR144Fw: TGTTGCTTTGAACACTAACATGCT33100
      Rv: TGCCCTTGTCAGACACAACA
      12PtSSR155Fw: TTCAACATTCCCCCAACCCC22100
      Rv: AAGAAGAGGAACACCAGGCC
      13PtSSR168Fw: GATCCCACCCACCACTTCTG55100
      Rv: GGCTCTAGTTCTGGTGCTGG
      14PtSSR172Fw:TCTCCAAAACAAGAAGGAAACTCC4375
      Rv: TCTTTCCTCTTCTGGTATCCCA
      15PtSSR174Fw: CAAAGAAGAAGCAGCCGCAG66100
      Rv: GTCAATCCCGAAGCACTTGC
      16PtSSR175Fw: CTGAGTCTCTGCAAAGCCCA77100
      Rv: TGTCACTGTGCTCCAACTCC
      17PtSSR186Fw: TGTTGCTTTGAACACTAACATGCT44100
      Rv: TGCCCTTGTCAGACACAACA
      18PtSSR187Fw: TGTTGCTTTGAACACTAACATGCT44100
      Rv: TGCCCTTGTCAGACACAACA
      19PtSSR190Fw: AACTGCAGGAGGAGCATGAC55100
      Rv: GAGCCTCCAGGTTCTTGTCC
      20PtSSR191Fw: GGAAGCATTGCGGTTTGGTT33100
      Rv: TCACATCACATGCTGCCACT
      21PtSSR196Fw: GCAAGAACCTGTGCTCCTCT3266.67
      Rv: TGCCAATGCCATTGTGGTTG
      22PtSSR201Fw: GCCTCTTCCAGCGAGAACTT44100
      Rv: TGATCCTCCCCAACAAGCTG
      23PtSSR222Fw: TGTGCAAGAAGGATGGGTGA22100
      Rv: GGTTGCATTCGGAAGCAACA
      Total118112
      Avarage5.134.8794.91
    • For each SSR locus, Popgene32 version 1.32 was used to analyze the gene frequency, number of allele (Na), effective number of alleles (Ne), polymorphic loci, Nei's genetic distance (D), Shannon–Weaver diversity index (I), Homogeneity test index (H) and gene flow (Nm).

      Genetic structure was inferred by STRUCTURE version 2.3.1[18]. The number of genetic clusters (K) was set from 1 to 20 with a burn-in period of 50000 steps followed by a run with 100000 iterations. Twenty independent runs were undertaken for each K value. Later, three replicates of the analysis were implemented in CLUMPP software[19]. The mean posterior probabilities [Lnp(D)] values of each K were calculated according to Pritchard et al., along with ∆K[18,20] to explore the optimum number of clusters (K). The most likely number of clusters was determined using a structure harvester (http://taylor0.biology.ucla.edu/structureHarvester/)[21]. Cluster analysis by the unweighted pair group method with arithmetic mean (UPGMA) based on the jaccard method was also developed using the NTSYS-pc 2.10e software[22]. A principal component analysis (PCA) was performed using NTSYS 2.10.

      The core collection was developed employing software Core hunter in R package[23]. To assess the core germplasm set, maximum Shannon's diversity index was estimated.

    • Encoding binary digit format for genotyping sequence format to exploit the utility of potential core SSRs to fingerprint Pueraria accessions. The utilization efficiency and 23 primers information are shown in Table 1 & Supplemental Fig. S1. A total of 118 alleles were detected among 272 Pueraria individuals, leading to a mean number of alleles per locus of 5.13 (ranging from two for PsSSR155 and PtSSR222, to 11 for PtSSR98 and PtSSR122). A total of 112 polymorphic alleles (94.91%) was identified with an average of 4.87 effective alleles per locus. Among the 118 alleles, 11 (9.3%) were rare alleles with frequency less than 1% and four of them were found to be only once in one individual. The average of the observed number of alleles (Na) and the effective number of alleles (Ne) were 1.9492 and 1.2841, respectively.

    • The population-level genetic diversity of the Pueraria accessions under study is presented in Table 2. Nei's gene diversity ranged from 0 to 0.5 and Shannon's information index (I) ranged from 0 to 0.6931 across all 23 SSR loci with an average of 0.1778 and 0.2858, respectively. The average value of total expected heterozygosity (Ht) and Nm were recorded at 0.1841 and 1.7690, respectively.

      Table 2.  Genetic characteristics for 112 polymorphic microsatellite loci in 272 individuals of Pueraria species in the present study.

      LocusSample sizeNaNehIHtHsGstNm
      36-127221.01120.0110.03440.01290.01280.006675.8301
      36-227221.01120.0110.03440.01290.01280.006675.8301
      36-327221.04630.04430.10820.05170.05030.027317.8197
      36-427221.00740.00740.02440.00860.00860.0043114.4978
      36-527221.10170.09230.19410.10630.10070.05318.9206
      36-627221.08430.07780.16960.09050.0860.04999.5227
      36-727221.26540.20980.36500.23940.20250.15412.7446
      36-827221.93220.48240.67550.49150.41570.15432.7395
      36-927221.0340.03290.08490.03090.03080.0032156.1603
      36-1027221.00740.00740.02440.00860.00860.0043114.4978
      98-127221.01880.01840.05270.02160.02130.01144.8945
      98-227221.14420.1260.24730.1450.13360.07865.8651
      98-327221.14990.13040.25390.15090.13740.08955.0838
      98-427221.05840.05520.12910.06340.06160.027517.6531
      98-527221.03830.03690.09330.04310.04210.022521.6887
      98-627221.03830.03690.09330.04310.04210.022521.6887
      98-727221.06710.06290.14330.07330.07040.039612.1273
      98-827221.06710.06290.14330.07330.07040.039612.1273
      98-927221.160.13790.26520.15950.14420.09574.7224
      98-1027211.00000.00000.000000
      98-1127221.01490.01470.04360.01610.0160.003166.8934
      99-127221.01880.01840.05270.02160.02130.01144.8945
      99-227221.06150.0580.13430.05920.05920.0007711.3620
      99-327221.2880.22360.38300.20290.18570.08495.3899
      99-427221.20650.17120.31290.18550.17810.0412.0065
      99-527221.140.12280.24240.14220.13040.08355.4886
      99-627221.36430.2670.43750.26220.2610.0043114.6222
      99-727221.53990.35060.53530.34480.34210.007665.5416
      104-127221.0340.03290.08500.03520.03510.0039128.2046
      104-227221.95580.48870.68180.49640.39860.19692.0392
      104-327221.99920.49980.69290.49570.37380.24591.5335
      108-127221.62920.38620.57460.3930.38820.012240.3512
      108-227221.47280.3210.50160.33310.32340.029216.6414
      108-327221.68310.40580.59580.39720.38830.022321.9303
      109-127221.29340.22690.38720.25680.21670.15622.7010
      109-227221.01490.01470.04360.01610.0160.003166.8934
      109-327221.29340.22690.38720.25680.21670.15622.7010
      109-427221.01490.01470.04360.01610.0160.003166.8934
      109-52721100.000000
      121-127221.07530.070.15610.08050.07750.037412.8581
      121-227221.07530.070.15610.08050.07750.037412.8581
      121-32721100.000000
      122-127221.03040.02950.07780.03450.03390.017927.4911
      122-227221.13510.1190.23670.13790.12680.08055.7097
      122-327221.03040.02950.07780.03450.03390.017927.4911
      122-427221.08870.08150.17600.09480.08980.05259.0192
      122-527221.08870.08150.17600.09480.08980.05259.0192
      122-627221.06710.06290.14330.07330.07040.039612.1273
      122-727221.03430.03320.08560.03880.0380.020224.2677
      122-827221.42530.29840.47520.29290.29120.005885.4766
      122-927221.00370.00370.01340.00430.00430.0022230.4989
      122-1027221.51870.34150.52500.36730.31460.14352.9847
      122-1127221.29730.22910.39020.26080.21520.17482.3604
      130-127221.20250.16840.30900.1940.17030.12213.5945
      130-227221.20250.16840.30900.1940.17030.12213.5945
      130-327221.03830.03690.09330.04310.04210.022521.6887
      130-427221.03830.03690.09330.04310.04210.022521.6887
      130-52721100.000000
      135-127221.01490.01470.04380.01720.01710.008856.4956
      135-227221.05870.05540.12960.06470.06240.034613.9494
      135-327221.34770.2580.42640.29310.22950.21711.8032
      135-427221.01110.0110.03430.01180.01180.0013397.0141
      135-527221.31270.23820.40170.27160.2190.19342.0859
      135-627221.01490.01470.04360.01610.0160.003166.8934
      135-727221.77850.43770.62950.47060.18850.59940.3341
      135-827221.96560.49130.68440.49990.15580.68840.2263
      144-127221.03430.03320.08560.03880.0380.020224.2677
      144-227221.92510.48050.67360.4790.47760.0029174.8282
      144-327221.69980.41170.60200.38770.32130.17132.4193
      155-127221.06980.06520.14750.06870.06840.0049101.9746
      155-227221.24830.19890.35070.22810.19360.15122.8079
      168-127221.5010.33380.51620.31240.28250.09564.7296
      168-227221.92540.48060.67360.48920.43210.11663.7876
      168-327221.51130.33830.52140.31810.29040.08695.2557
      168-427221.95690.4890.68210.49540.43280.12623.4613
      168-527221.00740.00730.02430.00750.00750.00012000.0000
      172-127221.23680.19150.34070.21980.18820.14382.9771
      172-227221.01120.0110.03440.01290.01280.006675.8301
      172-32721100.000000
      172-427221.00370.00370.01340.00320.00320.0016310.4992
      174-127221.00740.00740.02440.00860.00860.0043114.4978
      174-227221.0740.06890.15420.07410.07340.009950.0991
      174-327221.12490.1110.22410.12780.11920.06696.9725
      174-427221.00370.00370.01340.00430.00430.0022230.4989
      174-527221.75650.43070.62210.46210.24460.47060.5625
      174-627221.97220.49290.68610.50.20540.58920.3486
      175-127221.00740.00740.02440.00860.00860.0043114.4978
      175-227221.09320.08520.18230.09910.09370.05528.5594
      175-327221.00740.00740.02440.00860.00860.0043114.4978
      175-427221.20690.17140.31320.19650.17350.1173.7725
      175-527221.85040.45960.65220.47690.36540.23371.6394
      175-627221.70470.41340.60380.390.32520.16592.5132
      175-727221.03010.02920.07710.02980.02980.00031480.2742
      186-127221.04210.04040.10050.04620.04530.018127.1466
      186-227221.77260.43590.62760.40590.27390.32521.0373
      186-327221.78480.43970.63160.46980.23250.50510.4899
      186-427221.69770.4110.60130.38080.27930.26641.3765
      187-127221.0420.04030.10020.04490.04440.012240.4142
      187-227221.76410.43310.62470.40210.26550.33970.9718
      187-327221.8750.46670.65940.49050.15710.67960.2357
      187-427221.66360.39890.58830.36870.27790.24651.5286
      190-127221.09930.09030.19080.08330.0820.016429.9125
      190-227221.0460.0440.10760.03850.03770.0224.4900
      190-327221.15350.13310.25800.12110.11660.03713.0124
      190-427221.60240.37590.56340.40760.29960.26511.3862
      190-527221.99990.50.69310.49560.3230.34830.9354
      191-127221.92840.48140.67450.4980.11260.77380.1462
      191-227221.67890.40440.59420.37080.25410.31461.0893
      191-327221.16410.1410.26960.12610.11920.05528.5656
      196-127221.22950.18670.33420.21360.18560.13123.3100
      196-227221.00740.00740.02440.00860.00860.0043114.4978
      196-32721100.000000
      201-127221.00370.00370.01340.00430.00430.0022230.4989
      201-227221.99780.49950.69260.49890.35310.29231.2103
      201-327221.4040.28770.46250.32240.25090.22191.7536
      201-427221.40330.28740.46210.27980.27680.01145.0326
      222-127221.30390.2330.39510.26510.21760.17912.2910
      222-227221.77110.43540.62710.46170.30310.34350.9556
      Mean2721.94921.28410.17780.28580.18410.14350.22041.7690
      St. Dev0.22060.32770.17410.23970.03050.0166
      Na = Observed number of alleles; Ne = Effective number of alleles[56]; h = Nei's (1973) gene diversity; I = Shannon's Information index[57]; Gst = coefficient of gene differentiation; Nm = estimate of gene flow from Gst or Gcs. E.g., Nm = 0.5(1 - Gst)/Gst; Ht = Total expected heterozygosity; Hs = the average expected heterozygosity within subpopulations.
    • The clustering analyses using STRUCTURE under the admixture model suggested the optimum K was two by STRUCTURE HARVEST[21], which divided all sampled individuals into two groups. Correspondingly, the highest of adhoc measure (∆K) analysis[20] gave a sharp peak at K = 2 (Fig. 1). Hence, the true number of groups were considered as two (Pop1 and Pop2). The accessions with a probability of more 80% were considered as pure and assigned to corresponding subgroups while less than 80% were categorized as admixture (Fig. 1). Among 272 genotypes, 259 were pure and 13 Pueraria accessions were admixture. With evidence for several admixtures within cluster I (code_collection number: 30_JCJ-30, 32_JCJ-32, 196_GL-32, 197_GL-33) or cluster II (code_collection number: 12_YZ-12, 26_LC-26, 27_LC-27, 28_HJ-28, 113_GP-21, 149_BS-13, 160_BS-24, 195_GL-31, 270_Y10), subpopulation P1 showed 152 pure (97.5%) and four admixed (2.5%) landraces, P2 had 107 pure (92.2%) and 9 (7.8%) admixed landraces. In addition, all of the 272 individuals could be clustered into one of four groups when K = 4 (Fig. 1). However, within each of the four closely related groups, a few individuals always contained an admixture of introgressed genetic material from another accession.

      Figure 1. 

      Bar plots of all 272 individuals from Pueraria germplasm grouped into two or four genetic clusters with assignment probabilities obtained from STRUCTURE analyses of polymorphisms at 23 simple sequence repeat loci. (a) Distribution of delta K = 1−20. (b), (c) Histogram of the STRUCTURE assignment test when K = 2 or K = 4, respectively. The number represents the code in Supplemental Table S1.

    • Although there was no clear demarcation in the clustering pattern in the present study, the UPGMA dendrogram (Fig. 2) showed that all the accessions were divided into two main clusters at 0.378 similarity coefficient, which showed similar results to structure analysis. Furthermore, 272 accessions were divided into four main clusters at 0.684 similarity coefficient. The minimum similarity is 0.587 for most other accessions (Fig. 2). There was no distinctive trend of accessions in these two clusters according to their place of origin (Fig. 3). For instance, accessions from Longzhou county of Chongzuo (LZ-9 to LZ-13), were covered within these two clusters with no evident bias.

      Figure 2. 

      Cluster diagram based on jaccard by UPGMA analysis calculated from alleles derived from 272 Pueraria accessions. The number represents the code in Supplemental Table S1.

      Figure 3. 

      Geographical distribution of the accessions collected in Guangxi. The number represents the code in Supplemental Table S1. The red and blue numbers represent two clusters of the 20 accessions of core germplasms. The orange squares represent the accessions of Cluster I and blue circles represent the accessions of Cluster II.

      The PCA categorized all the accessions undertaken into two groups, which was in line with the results of UPGMA based phylogenetic tree and model-based STRUCTURE analysis. The first two axes of differentiation explained 89% of the total variation. The first coordinate explained 40% of the variation and the second coordinate explained 49% of the variation (Fig. 4). The results of PCA indicated that the genetic distance does not show a relationship with geographical distribution in this study.

      Figure 4. 

      PCA of Pueraria accessions based on dissimilarity matrix (Jaccard). The number represents the code in Supplemental Table S1. The number represent 272 accessions of Pueraria. The orange circles represent the accessions of Cluster I and blue circles represent the accessions of Cluster II.

    • One hundred and five SSR alleles found in this study could be represented by a core collection of 20 Pueraria accessions with 7.35% sampling proportion (Table 3, Supplemental Fig. S2). When the core selection capacity reached 20, the allele number was 105, so it captured close to 93.75% (105/112) of the total polymorphic loci. The average of the value of Na, Ne, h, I was 1.8898, 1.3716, 0.2359, and 0.3727, respectively. Based on the dendrogram, the germplasm accessions could be divided into two main groups. The value of genetic similarity indices among 20 Pueraria germplasm accessions varied between 0.31 and 0.60, which indicates that there was a relatedly narrow genetic variation within the different Pueraria accessions belonging to the diverse geographic locations across the Guangxi region (Fig. 5). In addition, our COREFINDER analysis highlighted that 10% of the entire core collection was represented by the Pueraria accessions grouped in Cluster I, while Cluster II contribute to the core collection at 90%.

      Table 3.  Summary of the extraction of a core collection.

      Sampling proportionSample numberNaNehINumber of polymorphic lociPercentage of polymorphic lociPercentage of total loci
      5%141.8644 ± 0.34381.3839 ± 0.31160.2413 ± 0.16390.3778 ± 0.224310291.07%86.44%
      7.00%191.8644 ± 0.34381.3779 ± 0.31230.2381 ± 0.16350.3736 ± 0.224210291.07%86.44%
      7.35%201.8898 ± 0.31441.3716 ± 0.30840.2359 ± 0.15980.3727 ± 0.217010593.75%88.98%
      7.70%211.8983 ± 0.30351.3658 ± 0.30630.2333 ± 0.15800.3702 ± 0.213910694.64%89.83%
      8%221.8983 ± 0.30351.3655 ± 0.30490.2333 ± 0.15810.3699 ± 0.214510694.64%89.83%
      10%271.8983 ± 0.30351.3577 ± 0.29910.2297 ± 0.15750.3648 ± 0.215410694.64%89.83%
      15%411.9068 ± 0.29201.3451 ± 0.30550.2208 ± 0.16130.3518 ± 0.220310795.54%90.68%
      20%541.9237 ± 0.26661.3384 ± 0.31060.2161 ± 0.16210.3459 ± 0.219510997.32%92.37%
      30%821.9322 ± 0.25251.3204 ± 0.30520.2060 ± 0.16250.3314 ± 0.221711098.21%93.22%
      40%1091.9407 ± 0.23721.3180 ± 0.31580.2024 ± 0.16640.3249 ± 0.227211199.11%94.07%
      50%1361.9322 ± 0.25251.3146 ± 0.32730.1980 ± 0.17030.3171 ± 0.232711098.21%93.22%
      100%2721.9492 ± 0.22061.2841 ± 0.32770.1778 ± 0.17410.2858 ± 0.239711294.92%
      Na = Observed number of alleles; Ne = Effective number of alleles[56]; h = Nei's (1973) gene diversity; I = Shannon's Information index[57].

      Figure 5. 

      Cluster diagram based on jaccard by UPGMA analysis calculated from alleles derived from 20 Pueraria accessions of core germplasm. The number represents the code in Supplemental Table S1.

    • We detected a total of 118 alleles with 23 SSRs segregating in the 272 Pueraria accessions in Guangxi, with an average of 5.13 alleles per locus. This value is higher than the number of alleles per SSR locus reported in a previous study with the 28 SSRs in the 44 Pueraria accessions from Guangxi[17]. This suggests that expanding the sample size is a powerful strategy for the analysis of genetic diversity in Pueraria germplasm in Guangxi. The number of effective alleles per locus (4.87) obtained in the Guangxi Pueraria accessions appears to be higher than the number of effective alleles per SSR locus found in 184 Pueraria accessions from Jiangxi (1.4503) and other crops, such as the value of 2.26 reported in rice[24], 3.17 in olive[25], but lower than the values of 5[26] or 7.2 in maize[27]. The results also showed that SSR allelic diversity of Pueraria germplasm was moderate (Na = 1.9492, Ne = 1.2841, h = 0.1778). Zhou et al.[14] reported an average of Ne = 1.4503 and h = 0.2865 in a collection of 184 Pueraria accessions from Jiangxi. The number of markers and individuals, the sexual propagules and type of plant material, the population size may be responsible for the level of polymorphism and discrimination power.

    • The overall clustering patterns generated by the STRUCTURE and PCA did not clearly distinguish the sampling areas, which is consistent with the previous results[10,13,17,28]. Few admixtures (13/272) were also detected due to shared ancestry during the breeding process, which is also observed in hybrid rice[29]. Pueraria resources have a low level of genetic differentiation (Nm = 1.7690). The degree of genetic differentiation among populations may decrease due to the existence of large gene flow (Nm > 1). The low genetic differentiation indicated that geographical isolation may not restrict gene exchange among Pueraria species populations in Guangxi. It is susceptible to external factors even though there was a certain correlation between genetic variation and geographical distribution based on RAPD in several studies[12,30,31]. As a result, it is thought that Pueraria species has been cultivated and utilized for a long period in Guangxi since native cultivars of Pueraria still exist in the major regions, which is similar to Perilla in Korea[32]. The selection by humans could be responsible for this clustering pattern and moderate genetic diversity.

      Our results revealed that Pueraria accessions display moderate genetic variation throughout Guangxi, while the UPGMA dendrogram showed that 272 accessions were divided into two main clusters with 37.8% genetic similarity, four main clusters with 68.4% genetic similarity. However, previous studies revealed that Pueraria accessions or species possessed from moderate to the high level of genetic diversity with high clonal reproduction and perennial[3,14,17,28,30,3337]. The inconsistencies observed, except for various taxon sampling and markers, could have originated from the following: 1) the populations were found by sexual propagules could contribute to the maintenance of high genetic variation in clonal populations regardless of recruitment of sexual offspring[38]; 2) introductions from across its multiple native populations into novel habitats from seed stock[37]; 3) clonal populations with fewer genotypes still maintain higher genetic diversity at each locus[39].

      Moreover, Pueraria species, as strictly self-pollinating and clonally persisting clumps plants, have considered heterozygosity (Table 2), like many clonal plants, e.g. Castanea dentata[40] and Musa balbisiana[41]. Our results showed that relatively low Ht (0.1841) and Hs (0.1435), which suggest that accessions were inbred due to little outcrossing during maintenance[42]. Moreover, we could not rule out a case that the existence of ancient clonality and the somatic mutation, which accumulates genetic variation within clonally persisting clumps may account for some of the heterozygosity, especially given rapid mutation of SSR fingerprints.

    • Core germplasm plays a key role in the conservation, management, and utilization of germplasm resources, which is critical for the development of plant breeding. Individuals reflecting genetic information can be selected to build the core germplasm resources. China is the center of distribution of Pueraria, with a long history of growing Pueraria species. However, fewer excellent Pueraria germplasm have been established due to artificial over-mining, lack of conservation, and management of resources. Previous researchers have shown that a sampling proportion between 5% and 30% is enough to include at least 80% of the alleles representing the genetic diversity of the entire collection[43,44]. According to dynamic extracted results, our results revealed that when the samples collected reached 7.35% (20/272) of Pueraria accessions accounted for 105 alleles, accounting for approximately 93.75% of all alleles loci. Interestingly, the retention value of Pueraria core collection genetic diversity was lower than the allele retention values of 100%, 100%, and 97.5% in rosewood, licorice, and eggplant, with sampling ratios reaching 12.4%[45], 16.84%[46] and 12.03%[47], respectively. Pueraria species are abundant in Guangxi, especially in Tengxian and Wuzhou[2]. The most likely reason was that the breeding of a majority of Pueraria accessions in Guangxi was still from layering breeding and self-crossing, and lacked extensive gene exchanges from cross-breeding, which led to a decrease in the ratio of the core collection. Our findings will be useful in breeding programs for the introgression of noble alleles into modern cultivars by exploiting natural genetic variation existing in Pueraria genetic resources. Combined with the analysis of phenotypic diversity (e.g. puerarin, starches) of Pueraria species, we may detect the important polymorphic loci associated with the traits based on correlation analysis, which could provide a foundation for developing the molecular marker-assisted breeding or detection of target genes soon[7].

      Meanwhile, the genetic clusters were not consistent with species delimitation and geographic distribution. For instance, accession number 140 and 68 classified as P. montana var. montana, shares a close relationship with three numbers P. montana var. lobata accessions (29, 243, and 245). Pueraria plants were introduced from different regions, which may result in a certain degree of inconsistency between actual germplasm sources and clustering results[17]. Furthermore, this also implies the complex evolutionary history with the human process blur the relationship among these species.

    • Molecular marker based on SSR can help exploiting and utilizing plant variety resources reliably without the appraiser and environmental factors[48]. The present results include new clues in genetic relationships among Pueraria species based on SSR markers, that is moderate genetic variation and low genetic differentiation play a key role in the species delimitation of Pueraria. Pueraria DC. (Fabaceae, Phaseoleae) comprises ca. 20 species, occurring in tropical and East Asia. Eight species and two varieties have been recorded in China[49], with four groups or three sections as infrageneric classification based on morphological traits[50,51]. However, molecular studies have revealed that Pueraria is not a monophyletic group[52,53]. For example, taxonomically kudzu (P. montana var. lobata) is placed under the genus Pueraria. Pueraria montana var. thomsonii and P. montana var. lobata were treated as varieties for P. montana in flora of China. However, the phylogenetic relationship and classification among these three species are still confused based on various molecular markers and sampling taxon[54,55]. Thus, molecular markers for germplasm identification of kudzu or even Pueraria species may be limited. A wider taxon sampling with higher resolution genetic markers would increase confidence for the phylogenetic relationship among Pueraria species, efforts that are currently underway.

    • In this study, we used 23 pairs of simple sequence repeat primers to evaluate the genetic diversity and construct core germplasm of the 272 individuals of Pueraria species in Guangxi. Our results revealed that Pueraria accessions display moderate genetic variation throughout Guangxi. There was a non-significant relationship between genetic distance and geographical distance. The results could provide the basis for the breeding program of Pueraria. We consider the SSR markers to be a useful tool for both genetic diversity and the core germplasm of Pueraria.

    • The authors confirm contribution to the paper as follows: study conception and design: Yan H; data collection: Cao S, Zeng W, Wu Z; analysis and interpretation of results: Shi P, Zhou Y, Shang X; draft manuscript preparation: Xiao L, Zhou Y. All authors reviewed the results and approved the final version of the manuscript.

    • The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

      • This work was supported by the Guangxi Natural Science Foundation Project (2021JJB130122, 2023GXNSFBA026297, 2021GXNSFBA220026), the National Natural Science Foundation of China (82204563, 31960420), the Guangxi Key R&D Program Project (Guike AB22080090), the Technology Development Project funded from Guangxi Academy of Agricultural Sciences Science (GXAAS) (Guinongke 2023JZ10), and the Special Project for Basic Scientific Research of Guangxi Academy of Agricultural Sciences (Guinongke 2021YT057).

      • The authors declare that they have no conflict of interest.

      • Received 30 November 2023; Accepted 14 March 2024; Published online 23 April 2024

      • 272 individuals of Pueraria species in Guangxi were divided into two main clusters in all analysis.

        118 alleles were identified and 112 alleles were polymorphic.

        Overall genetic diversity was moderate.

        A core collection of 20 Pueraria accessions was constructed when the samples collected reached 7.35% (20/272).

      • # Authors contributed equally: Pingli Shi, Yun Zhou

      • Copyright: © 2024 by the author(s). Published by Maximum Academic Press on behalf of Hainan University. This article is an open access article distributed under Creative Commons Attribution License (CC BY 4.0), visit https://creativecommons.org/licenses/by/4.0/.
    Figure (5)  Table (3) References (57)
  • About this article
    Cite this article
    Shi P, Zhou Y, Shang X, Xiao L, Zeng W, et al. 2024. Assessment of genetic diversity and identification of core germplasm of Pueraria in Guangxi using SSR markers. Tropical Plants 3: e012 doi: 10.48130/tp-0024-0012
    Shi P, Zhou Y, Shang X, Xiao L, Zeng W, et al. 2024. Assessment of genetic diversity and identification of core germplasm of Pueraria in Guangxi using SSR markers. Tropical Plants 3: e012 doi: 10.48130/tp-0024-0012

Catalog

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return