Plan
Comptes Rendus

Molecular biology and genetics/Biologie et génétique moléculaires
Molecular genetic diversity and population structure in Lycium accessions using SSR markers
Comptes Rendus. Biologies, Volume 333 (2010) no. 11-12, pp. 793-800.

Résumé

This study was conducted to assess the genetic diversity and population structure of 139 Lycium chinense accessions using 18 simple sequence repeat (SSR) markers. In total, 108 alleles were detected. The number of alleles per marker locus ranged from two to 17, with an average of six. The gene diversity and polymorphism information content value averaged 0.3792 and 0.3296, with ranges of 0.0793 to 0.8023 and 0.0775 to 0.7734, respectively. The average heterozygosity was 0.4394. The model-based structure analysis revealed the presence of three subpopulations, which was consistent with clustering based on genetic distance. An AMOVA analysis showed that the between-population component of genetic variance was less than 15.3%, in contrast to 84.7% for the within-population component. The overall FST value was 0.1178, indicating a moderate differentiation among groups. The results could be used for future L. chinense allele mining, association mapping, gene cloning, germplasm conservation, and designing effective breeding programs.

Métadonnées
Reçu le :
Accepté le :
Publié le :
DOI : 10.1016/j.crvi.2010.10.002
Mots clés : Lycium chinense Mill., Molecular diversity, Population structure, Simple sequence repeats (SSRs), SSR, PIC, RAPD, SCAR, AMOVA, UPGMA

Wei-Guo Zhao 1, 2 ; Jong-Wook Chung 1 ; Young-Il Cho 1 ; Won-Hee Rha 1 ; Gi-An Lee 3 ; Kyung-Ho Ma 3 ; Sin-Hee Han 4 ; Kyong-Hwan Bang 4 ; Chung-Berm Park 4 ; Seong-Min Kim 1 ; Yong-Jin Park 1, 5

1 Department of Plant Resources, College of Industrial Science, Kongju National University, Yesan 340-702, Republic of Korea
2 Sericultural Research Institute, Chinese Academy of Agricultural Sciences, Jiangsu University of Science and technology, Zhenjiang Jiangsu 212018, China
3 National Agrobiodiversity Center, National Institute of Agricultural Biotechnology, RDA, Suwon 441-707, Republic of Korea
4 Department of Herbal Crop Research, National Institute of Horticultural & Herbal Science, RDA, Suwon 369-873, Republic of Korea
5 Legume Bio-Resource Center of Green Manure (LBRCGM), Kongju National University, Yesan 340-702, Republic of Korea
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     title = {Molecular genetic diversity and population structure in {\protect\emph{Lycium}} accessions using {SSR} markers},
     journal = {Comptes Rendus. Biologies},
     pages = {793--800},
     publisher = {Elsevier},
     volume = {333},
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     year = {2010},
     doi = {10.1016/j.crvi.2010.10.002},
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%A Wei-Guo Zhao
%A Jong-Wook Chung
%A Young-Il Cho
%A Won-Hee Rha
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%A Sin-Hee Han
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%A Chung-Berm Park
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Wei-Guo Zhao; Jong-Wook Chung; Young-Il Cho; Won-Hee Rha; Gi-An Lee; Kyung-Ho Ma; Sin-Hee Han; Kyong-Hwan Bang; Chung-Berm Park; Seong-Min Kim; Yong-Jin Park. Molecular genetic diversity and population structure in Lycium accessions using SSR markers. Comptes Rendus. Biologies, Volume 333 (2010) no. 11-12, pp. 793-800. doi : 10.1016/j.crvi.2010.10.002. https://comptes-rendus.academie-sciences.fr/biologies/articles/10.1016/j.crvi.2010.10.002/

Version originale du texte intégral

1 Introduction

The genus Lycium L. (Solanaceae) comprises approximately 70 species of spiny shrubs and small trees. The fruit of the Lycium species are all red in color, with very similar physical appearance and anatomical structure. Most species occur in arid and subarid regions, but some occur in subsaline regions or along the seacoast [1–3]. Lycium chinense Mill. and Lycium barbarum are perennial foliage plants endemic to Korea, Japan, and China and are widely used for medicinal purposes with a history of almost 2000 years’ use [4,5]. Lycii fructus, Lycii folium, and Lycii cortex of L. chinense contain betaine, rutin, tocopherols, chlorogenic acid, kukoamine A, b-sitosterol, and various fatty acids [6–8]. These plants, especially L. chinense, have been used to replenish the vital essence of the liver and kidney and to improve eyesight. Chinese physicians also prescribe them to strengthen muscles and bones [9].

L. chinense is well known as a key medicinal plant, and knowledge of germplasm genetic diversity and population structure are critical for its utilization in genotype identification and genetic improvement [10]. Traditionally, L. chinense genotypes have been authenticated by morphological and histological analyses. Recently, chemical analysis methods such as high-performance liquid chromatography have also been used for different Lycium species, but these have failed to distinguish closely related species due to similar chemical compounds [11]. Peng et al. [12] established a Fourier-transform infrared spectroscopy method to identify seven species and three varieties of Lycium. With the rapid development of modern biological methods, identification of species relationships using traditional anatomical and physiochemical methods is being supplemented by DNA fingerprinting techniques. In recent years, DNA-based molecular markers, such as random amplified polymorphic DNA (RAPD), sequenced characterized amplified regions (SCAR), and chloroplast and internal transcribed spacer DNA sequences [4,13–18] have been used to authenticate the species and analyze genetic variation. Due to their high polymorphism, co-dominance, and reproducibility, microsatellite or simple sequence repeat (SSR) markers have proved to be highly efficient molecular tools for marker-assisted selection, the analysis of genetic diversity, population genetic analysis, tracking desirable traits in large-scale breeding programs, as anchor points for map-based gene cloning strategies, and for other purposes in various species. However, so far, only a minor attempt has been made to isolate and characterize L. chinense SSRs [19]. It is important to understand genetic variation and genetic structure for conservation and sustainable use of Lycium species. In the present study, we used the Structure software program [20] to evaluate the genetic diversity and population structure of 139 accessions of L. chinense using a set of 18 newly developed microsatellite markers. The information may provide a more rational basis for expanding the gene pool and for identifying materials harboring valuable alleles to improve L. chinense.

2 Materials and methods

2.1 Plant materials

One-hundred and thirty nine L. chinense accessions, originating from four different countries, were obtained from the National Genebank of the Rural Development Administration (RDA-Genebank), Republic of Korea. The samples were mainly from the Republic of Korea (120) and China (17). A description of the accessions used in this study is shown in Table 1.

Table 1

List of the 139 Lycium chinense accessions used in this study and their model-based groupings.

S. no. Cultivar name or collection region Origin Model-based Subpopulationa
1 Yuseong1 Korea Admixture
2 Yuseong2 Japan S1
3 Cheongyangjaerae Korea S2
4 Jinbujaerae Korea S2
5 Jindojaerae Korea S2
6 Keumsanjaerae Korea S2
7 Haenamjaerae Korea S3
8 Collected from China China S3
9 Collected from China China S1
10 Collected from China China S3
11 Myeonan Korea S1
12 Bulro Korea S1
13 Cheongdae Korea S1
14 Jangmyeong Korea S1
15 Cheongun Korea S1
16 Cheongyang6 Korea S1
17 Cheongyang7 Korea S2
18 CL129-145 Korea S1
19 CL124-23 Korea S1
20 CL129-161 Korea S1
21 CL7-20 Korea S2
22 CL32 Korea S1
23 CB01185-27 Korea S1
24 Collected from China China S3
25 Collected from China China S3
26 Collected from China China S3
27 Collected from China China S2
28 Collected from China China S1
29 Collected from China China S1
30 Collected from China China S2
31 Collected from China China S3
32 CL2-32 Korea S2
33 CL105-84 Korea S1
34 CL15-106 Korea S1
35 CL31-83 Korea Admixture
36 CL37-4 Korea Admixture
37 CL42-17 Korea S1
38 CL123-575 Korea S2
39 B0148-10 Korea S1
40 CL54-36 Korea S1
41 CL54-82 Korea Admixture
42 CL58-83 Korea S2
43 CL47-157 Korea S2
44 CL57-157 Korea S1
45 CB01191-53 Korea S2
46 CL60-1 Korea S1
47 CL70-21 Korea S2
48 CL70-177 Korea S2
49 CL81-30 Korea S1
50 CB01193-23 Korea Admixture
51 CB01128-120 Korea Admixture
52 CB01188-333 Korea S1
53 Yuseong2(S)60Co32kr-3 Korea S1
54 CL3-21 Korea S2
55 CL31-15 Korea S3
56 CL32-13 Korea S2
57 CB04329-114 Korea S1
58 CB04329-13 Korea S1
59 99148-10 Korea S2
60 C0148-94 Korea S1
61 D0148-72 Korea S2
62 B0148-43 Korea S1
63 B0148-78 Korea S1
64 Y0148-2 Korea S3
65 CL129-45 Korea S1
66 CB00146-176 Korea S3
67 CB00148-46 Korea S1
68 CB01200-162 Korea S1
69 CB00153-8 Korea S1
70 CL137-65 Korea S2
71 CB00156-101 Korea S3
72 CB00159-140 Korea S1
73 CB00171-1 Korea S2
74 CB00169-37 Korea S1
75 CB00169-109 Korea S1
76 CL138-92 Korea S2
77 CB00171-189 Korea S2
78 CB00169-324 Korea S1
79 CL129-16 Korea S2
80 CB00164-206 Korea S1
81 CB00130-345 Korea S1
82 CL137-65 Korea S2
83 CL137-39 Korea S1
84 Collected from Mongolia Mongolia S3
85 Landrace1 (Chengyang) Korea Admixture
86 Landrace2 (Chengyang) Korea S2
87 Landrace3 (Kongju) Korea S2
88 Landrace4 (Kongju) Korea S2
89 Landrace5 (Boryeong) Korea S2
90 Landrace6 (Wando) Korea S2
91 Landrace7 (Munkyeong) Korea S2
92 Landrace8 (Munkyeong) Korea S2
93 Landrace9 (Sancheong) Korea S2
94 Landrace10 (Sancheong) Korea S3
95 Landrace11 (Yeongcheon) Korea S2
96 Landrace12 (Yeongcheon) Korea S2
97 Landrace13 (Geochang) Korea S2
98 Landrace14 (Goseong) Korea S2
99 Landrace15 (Pyeongchang) Korea S2
100 Landrace17 (Pyeongchang) Korea S2
101 Collected from China China S1
102 Collected from China China S3
103 CB01191-53 Korea S2
104 CB01191-36 Korea S1
105 CB01204-287 Korea S1
106 CB01210-12 Korea S1
107 CB01208-228 Korea S2
108 Collected from China China S3
109 Collected from China China S3
110 CB02214-11 Korea S1
111 Collected from China China S3
112 Collected from China China S3
113 CB03282-102 Korea S1
114 CB02214-111 Korea S1
115 CB02214-131 Korea S1
116 CB01185-20 Korea S1
117 CB03286-89 Korea Admixture
118 CB03289-172 Korea S2
119 CBP03310-250 Korea S1
120 Cheongyang8 Korea S1
121 Cheongyang9 Korea S3
122 CBP03302-5 Korea S1
123 99797 Korea Admixture
124 99892 Korea S1
125 Cheongyang13 Korea S1
126 Cheongyang14 Korea S1
127 CBP05400-2 Korea Admixture
128 CBP05400-4 Korea S1
129 Hwaboon Korea S1
130 99148-10 Korea S2
131 99412-1 Korea S2
132 B0148-43 Korea S1
133 B0148-78 Korea S1
134 D0148-62 Korea S2
135 D0148-72 Korea S2
136 C0148-74 Korea S1
137 C0148-120 Korea S1
138 Y0148-2 Korea S3
139 Y0148-24 Korea S3

a As defined by the program STRUCTURE.

2.2 SSR genotyping

A set of 18 highly polymorphic microsatellite markers enriched using a modified biotin–streptavidin capture method as described earlier [19] was used for the present study (Table 1). A three-primer system [21] including a universal M13 oligonucleotide (TGTAAAACGACGGCCAGT) labeled with one of the fluorescent dyes (6-FAM, NED, or HEX) was used, which allows PCR products to be triplexed during electrophoresis. A special forward primer composed by the concatenation of the M13 oligonucleotide and the specific forward primer was used with the normal reverse primer for SSR PCR amplification. Primer sequences and PCR amplification conditions for each set of primers have been described previously [19]. SSR alleles were resolved on an ABI PRISM 3100 DNA sequencer (Applied Biosystems, Foster City, CA, USA) using GENESCAN 3.7 software and were sized precisely using GeneScan 500 ROX (6-carbon-X-rhodamine) molecular size standards (35–500 bp) with GENOTYPER 3.7 software (Applied Biosystems).

2.3 Data analysis

The number of alleles, gene diversity (GD), heterozygosity (H), and polymorphism information content (PIC) per locus as well as the genetic distance were calculated with the PowerMarker 3.25 program [22]. The unweighted pair group method with an arithmetic mean (UPGMA) tree from shared allele frequencies was constructed using the MEGA 4.0 program [23], which is embedded in PowerMarker.

The possible population was analyzed using the Structure 2.2 model-based program [20] with a burn-in of 10,000, a run length of 150,000, and a model allowing for an admixture and correlated allele frequencies. Five runs of Structure were performed by setting the number of populations (K) from 1 to 12, and an average likelihood value, L(K), was calculated for each K across all runs. The model choice criterion to detect the most probable value of K was ΔK, which is an ad hoc quantity related to the second order change of the log probability of data with respect to the number of clusters inferred by Structure [24].

The molecular variance for model-based subgroups, FST, and the correlation of alleles within subpopulations were calculated using an analysis of molecular variance (AMOVA) approach in the Arlequin 3.11 program [25].

3 Results

3.1 SSR polymorphism

The 18 SSR markers revealed 108 alleles among the 139 L. chinense accessions representing the four countries (Table 1). The SSR loci diversity data are summarized in Table 2. The allelic richness per locus varied widely among the markers, ranging from two (GB-LCM-029; GB-LCM-111; GB-LCM-119; GB-LCM-199) to 17 (GB-LCM-022) alleles (average, six alleles). The frequency of major alleles per locus varied from 0.254 (GB-LCM-167) to 0.959 (GB-LCM-092). The allelic frequency database showed that rare alleles (frequency < 0.05) comprised 63.9% of all alleles, whereas intermediate (frequency of 0.05–0.50) and abundant alleles (frequency > 0.50) comprised 23.1 and 13.0% of all detected alleles, respectively. These results indicated the presence of a relatively large proportion of rare alleles, and most alleles were at a low frequency among the L. chinense accessions studied (Fig. 1). The high frequency of rare alleles (36.3%) among L. chinense accessions (especially among Korean accessions) indicates that they make a greater contribution to the overall genetic diversity of the collection. Hence, it is important to include rare alleles to maximize the genetic variation in the gene bank collections and to utilize them for breeding. The values for heterozygosity ranged from 0.00 at GB-LCM-037 to 1.00 at GB-LCM-025 with an average of 0.439. The average gene diversity and PIC values were 0.3792 and 0.3296, with a range from 0.0793 (GB-LCM-092) to 0.8023 (GB-LCM-167) and from 0.0775 (GB-LCM-092) to 0.7734 (GB-LCM-167), respectively.

Table 2

Total number of alleles and the genetic diversity index for 18 simple sequence repeat (SSR) loci in the 139 Lycium chinense accessions.

Locus GeneBank accession Primers NG NA MAF NRa GD H PIC
GB-LCM-004 FJ487889 F: ACATTTTGAATCTCCCCGT 4 4 0.801 2 0.3307 0.3971 0.2960
R: GGGAATCAAGATCAATAGTCA
GB-LCM-022 FJ487891 F: AAGACAGCACGCCAAAAA 21 17 0.788 15 0.3716 0.2793 0.3629
R: TGTATGATCCCTAAGTCCCG
GB-LCM-025 FJ487892 F: TGGATGGTCTATGCATGTTG 2 3 0.500 1 0.5142 1.0000 0.3962
R: AGCCACCCCCAACTAAAA
GB-LCM-029 FJ487893 F: CTGCTTAAACGATTGCCG 2 2 0.939 0 0.1148 0.1223 0.1082
R: CAAGCCACCAAACCTTCA
GB-LCM-037 FJ487894 F: GTGTGTGGGGTCTGAGC 3 3 0.563 1 0.4954 0.0074 0.3763
R: GAAAGAGCCCAATGCAAA
GB-LCM-044 FJ487895 F: TCTCCTTCGGACCCATTT 8 7 0.817 5 0.3111 0.1655 0.2816
R: CAAAGTCACAACGTCGCA
GB-LCM-075 FJ487896 F: CTCCTGAATACCCTGGGC 19 16 0.597 13 0.5632 0.6855 0.5048
R: TTGGCATAAGGTGCTCGT
GB-LCM-087 FJ487897 F: TTATCGTTGATGGTGGGG 7 7 0.903 6 0.1818 0.1799 0.1769
R: AGAAGAAGCAGCAGCACG
GB-LCM-092 FJ487898 F: TTTGGAATGAAACGACGG 5 3 0.959 2 0.0793 0.0410 0.0775
R: GGATCCACAGATTCATCACC
GB-LCM-104 FJ487899 F: GCCAAAAGAAGGAATGGG 3 3 0.814 1 0.3056 0.3723 0.2631
R: ACACCCCCGAGACTTAGC
GB-LCM-111 FJ487900 F: AATGTACATCGCCCCCA 2 2 0.888 0 0.1982 0.2230 0.1785
R: CGAGCTAAATCTCGAGGG
GB-LCM-119 FJ487901 F: GATTCAGGCCGAATGAGA 2 2 0.511 0 0.4998 0.9784 0.3749
R: GATTCGGAGCCTGCTTTT
GB-LCM-120 FJ487902 F: CGTGACTAGTGCCCGAAC 6 7 0.928 6 0.1366 0.1367 0.1331
R: CACATGGCGTATGGACAA
GB-LCM-145 FJ487903 F: CCTGAGAGCTGATGTGGC 4 3 0.547 1 0.5190 0.8898 0.4100
R: TGTATGATCCCACTCGCC
GB-LCM-166 FJ487904 F: CTTGAAGATGGAGGAAAGCA 6 4 0.489 1 0.5569 0.9474 0.4580
R: AGGAGGAGAAGGGGGAAG
GB-LCM-167 FJ487905 F: CCATTTGCACCACAAAGG 28 15 0.254 11 0.8023 0.8551 0.7734
R: CCCAAAATTAAAGGGGCA
GB-LCM-199 FJ487907 F: GATGTTGGTCTTGGGCTG 2 2 0.885 0 0.2037 0.2302 0.1830
R: TAAGGGCCCTCTTCAACG
GB-LCM-217 FJ487908 F: CTGCTTAAACGATTGCCG 14 8 0.470 4 0.6418 0.3985 0.5785
R: GAGCAAGCGCAACACTTT
Total 138 108 69
Mean 7.7 6 3.8 0.3792 0.4394 0.3296

a Alleles with a frequency less than 5%.

Fig. 1

Allele frequency histograms for the 108 alleles in the 139 Lycium chinense accessions.

3.2 Population structure analysis

Effective conservation and management strategies for L. chinense accessions require a fundamental understanding of their population structure. The model-based clustering method was performed using all 139 accessions and 18 SSR markers [20]. At this level, individual proportions of membership in each group, estimated using the multi-allele data set, suggested the existence of some population structure. Estimated likelihood values for a given K in five independent runs yielded consistent results, but the distribution of L(K) did not show a clear mode for the true K (Fig. 2) due to expected behaviour when factors such as inbreeding and departures from Hardy–Weinberg equilibrium are present [26]. These factors could lead to an overestimation of the number of K populations. Thus, another ad hoc quantity (ΔK) was used to overcome the difficulty of interpreting the real K values [24]. ΔK was developed and tested under different simulation routines in which real population structure was present. ΔK showed a clear peak at the true value of K. In this study, the highest value of ΔK for the 139 accessions was K = 3 (Fig. 3), which was consistent with clustering based on the genetic distance (Fig. 4), so we choose a value of K = 3 for the final analysis. The relatively small value of the alpha parameter (α = 0.099) indicates that most accessions originated from one primary ancestor, with a few admixed individuals [26]. As shown in Fig. 4, most of the accessions were clearly classified into one of the three subpopulations (S1–S3) including 65, 51, and 23 L. chinense accessions, respectively (Table 3). S1 consisted of 65 accessions, originating from three different countries but predominantly from Korea (60) and China (4). S2, with 51 accessions, consisted predominantly of Korean accessions (49), whereas the remaining accessions were from China (2). S3 consisted of 23 accessions, predominantly from China (11) and Korea (11) (Table 1). In addition to the accessions that were clearly assigned to a single population, i.e., greater than 70% of their inferred ancestry was derived from one of the model-based populations, 10 accessions (8.2%) in the sample were categorized with admixed ancestry (Fig. 4).

Fig. 2

(Log) Likelihood of the data (n = 139), L(K), as a function of K (the number of groups used to stratify the sample). For each K value, five independent runs (blue diamonds) were considered and data were averaged over the replicates.

Fig. 3

Values of ΔK, with its modal value detecting a true K of the three groups (K = 3).

Fig. 4

Model-based ancestry for each of the 139 accessions based on the 18 simple sequence repeat (SSR) markers used to build the Q matrix.

Table 3

Comparisons among model-based populations with regard to average genetic diversity and population differentiation.

Inferred group Diversity F ST f
n a NAb GDc Hd PICe 1 2 Overall
1 65 3.4 0.3350 0.4321 0.2902
2 51 3.4 0.3107 0.4152 0.2679 0.2616
3 23 4.7 0.4863 0.5222 0.4276 0.0849 0.1050
Overall 139 6.0 0.3792 0.4394 0.3296 0.1178

a The number of accession.

b Average number of allele.

c Gene diversity.

d Heterozygosity.

e Polymorphic information content.

f For AMOVA-based estimates, P < 0.005 for 100 permutations for all population comparisons.

4 Genetic diversity and differentiation in model-based populations

The amount and organization of genetic diversity differed (Table 3). Among the three model-based populations, the S3 subgroup contained a higher allelic richness and an average of 4.7 alleles per locus, while S1 and S2 had the same alleles. S3 also had the highest genetic diversity and PIC (gene diversity = 0.4863; PIC = 0.4276), followed by S1

The overall AMOVA analysis revealed that 15.3% of the variation was due to differences among subpopulations, and the remaining 84.7% was due to differences within subpopulations. Pairwise estimates of FST indicated a different degree of differentiation among the three model-based populations, with values ranging from 0.0849 (between S1 and S3) to 0.2616 (between S1 and S2) (Table 3). The overall FST value was 0.1178, indicating moderate differentiation among the three groups.

5 Discussion

Traditional Chinese medicine has been used for thousands of years in China. Authentication of Chinese medicinal materials is an old but important issue. L. chinense is a key medicinal plant; pharmacological studies have demonstrated that it has a large variety of beneficial effects, such as reducing blood glucose and serum lipids, anti-aging, immunomodulating, anticancer, and anti-fatigue activities, and improvements in male fertility [8,27,28], but it is difficult to distinguish among the species using traditional morphological and histological analyses. Cheng et al. [13] investigated L. barbarum sold on the Taiwan market using RAPD analysis, and only two RAPD fingerprinting types were outlined, revealing low genetic diversity among the samples. Zhang et al. [14] developed the RAPD technique to distinguish L. barbarum from related Lycium species. Sze et al. [17] applied the SCAR marker to authenticate L. barbarum and its adulterants. Nevertheless, SSRs have become one of the most widely used molecular markers for various plant studies in recent years. In this study, we identified the genetic diversity and population structure of L. chinense accessions. The SSR loci newly developed by our group [19] were polymorphic and detected an average of 6.0 alleles per locus, with an average PIC value of 0.3296. The major allele frequency distribution was analyzed at each locus (Table 2). A high proportion of rare alleles might be of adaptive significance, so the capture and preservation of rare alleles and genotypes is an important objective of any conservation strategy [10]. The correlation analysis revealed that allelic richness was significantly and positively associated with the PIC value (r =  0.54, P < 0.05).

The SSRs revealed considerable genetic diversity in the 139 accessions with diverse origins (Fig. 5); the similarity coefficient levels ranged from 0.4287 to 1.0000, with an average value of 0.7614. The high level of genetic variation observed in this study among the different accessions revealed by SSRs reflected a high level of polymorphism at the DNA level.

Fig. 5

Unrooted neighbor-joining tree based on a Nei's genetic distance matrix among 139 accessions. The colors correspond to the model-based populations.

The Structure program implements a model-based clustering method for inferring population structure using genotype data consisting of unlinked markers (Pritchard et al. [20]). The model does not assume a particular mutation process, and in most cases, the estimated log probability of the data does not provide a correct estimate of the number of clusters, K [24]. We observed in our simulations that as the real K is reached, L(K) continues to increase slightly at larger Ks plateaus, and the variance between runs increases (Fig. 2). The distribution of L(K) did not show a clear mode for the true K, but we found that ΔK did show a clear peak at the true value of K [24] (Fig. 3).

The model-based structure analysis used here revealed the presence of three populations (S1–S3). When clustering based on genetic distance and structure analyses based on the model were compared, similar patterns of accession groupings were discovered (Figs. 3 and 4). The degree of admixture, alpha (α = 0.0999), was inferred from the data. When alpha is close to zero, most individuals are essentially from one population or another, whereas when alpha is greater than one, most individuals are admixed [24]. The distribution of the 139 accessions, which shared at least 70% ancestry within one of the three inferred groups, is summarized in Table 1. In addition to the groups identified by this analysis, 8.2% of accessions showed evidence of mixed population ancestry. The mixture is likely the result of breeding, domestication history, and resource exchange, which have had large effects on diversity structure. The independent population histories of the groups have also shaped the gene pools. Because genetic variability is present in breeding programs, human-mediated gene flow may exist within a population due to breeding, resulting in a large amount of variation attributed to differences within groups (84.7%) rather than among the three inferred groups. A moderate differentiation existed among the three groups. The genetic diversity in each model-based population was also measured (Table 3). Within the subpopulation had lower allele number than among the population, but S3 had the highest genetic diversity and PIC.

Assessing genetic diversity and population structure is an essential component of germplasm characterization and conservation. The results derived from genetic diversity analyses could be used for designing effective breeding programs aimed at broadening the genetic bases of accessions.

Conflict of interest statement

We declare that we have no financial and personal relationships with other people or organizations that can inappropriately influence our work, there is no professional or other personal interest of any nature or kind in any product, service and/or company that could be construed as influencing the position presented in, or the review of, the manuscript entitled, “Molecular genetic diversity and population structure in Lycium accessions using SSR markers”.

Acknowledgements

This study was supported by the agenda project (#200901OFT072045008) from the Rural Development Administration (RDA), the Republic of Korea.


Bibliographie

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