Genetic relationship analysis and molecular fingerprint identification of the tea germplasms from Guangxi Province, China.

The tea plant (Camellia sinensis) is an evergreen woody plant with a high economic value. Guangxi Province is adjacent to the origin center of the tea plant in southern China. It has abundant germplasm resources and is a historically important tea-producing province. However, there is little information about the genetic diversity, genetic introgression, and fingerprints of the tea germplasms from Guangxi Province. Here, we constructed a phylogenetic tree of 126 tea accessions from Guangxi Province using 20 SSR markers. This tree classified these tea accessions into three subgroups containing 19, 47, and 60 members, respectively. High genetic similarity was observed among the three subgroups, and the genetic diversity of the populations was ranked as follows: subgroup 3 > subgroup 2 > subgroup 1. Furthermore, we analyzed the genetic relationships among 168 tea accessions from Guangxi Province and neighboring provinces. The results of the population structure analysis were highly consistent with the clustering results, and genetic introgression was observed. We identified six SSRs as the core marker set, because they could sufficiently distinguish between all 126 tea accessions. The results provide a crucial theoretical basis for utilization and protection of tea germplasms from Guangxi Province, and will help improve the breeding and popularization of elite tea cultivars.

Introduction

The tea plant (Camellia sinensis (L.) O. Kuntze) is an evergreen woody crop that has important health, economic, and cultural benefits (Li , Xia ). Due to self-incompatibility and long-term cross-pollination, a high degree of heterogeneity and abundance of genetic variations have been observed in tea plants (Seth , Xia ). Guangxi Province is located in the middle and south subtropical monsoon climate zone and borders the southeast edge of the Yunnan-Guizhou Plateau (He , Niu ). Due to its proximity to the center of origin and because it crosses two tea plant areas in southwest and south China, abundant tea germplasm resources have formed under the diverse climate and ecological conditions (Qiao ). The local tea plant resources have great potential for the breeding of new varieties, and accurate analyses of population structure and genetic diversity is generally a prerequisite for the genetic improvement of tea plants (Zhou ). Research on tea germplasm resources is usually carried out at the morphology, biochemical composition, protein, and molecular levels (Mukhopadhyay ). The molecular identification and evaluation of germplasm resources has become one of the most widely used methods because it is a rapid and reliable technique (Kottawa-Arachchi ).

Molecular markers are effective tools that can be used to analyze germplasm kinship and detect germplasm resource diversity (He , Lynch and Milligan 1994). A number of molecular markers have been successfully developed and used in genetic and genomic studies on tea plants. These markers include random amplified polymorphic DNA (RAPDs), amplified fragment length polymorphisms (AFLPs), cleaved amplified polymorphic sequences (CAPS), simple sequence repeats (SSRs), single nucleotide polymorphisms (SNPs), and insertion/deletion polymorphisms (InDels) (Liu , Muto , Wambulwa , Xia ). Among them, SSRs have multiple advantages, including co-dominant inheritance, multiple allelic characteristics, reproducibility, high abundance, and widespread distribution in the plant genome (Liu ).

The rapid development of high-throughput sequencing methods has meant that SSR markers have been continually applied in tea plant research, including genetic diversity and population structure analyses, origin and evolutionary analyses, marker-assisted selection, and the construction of genetic linkage maps (Fang , Liu , Taniguchi ). Previously, the genetic diversity of Guangxi tea germplasms was analyzed based on expressed sequence tag-derived SSR (EST-SSR) markers, and the results demonstrated that Guangxi tea germplasms are abundant and can strongly inbreed with C. sinensis var. pubilimba (Qiao , Zhou ). Furthermore, SSR markers are appropriate tools for fingerprinting tea germplasms. For instance, polymerase chain reaction (PCR)-based DNA markers were able to authenticate a fraudulent consignment of 44 Japanese green tea cultivars (Kaundun and Matsumoto 2004). In addition, eight well-chosen SSR markers were used as a recommended core marker set to fingerprint 128 Chinese clonal tea plants (Tan ). Liu identified five highly polymorphic SSR markers as a core marker set for distinguishing tea accessions, and all 80 tea accessions they investigated could be fully distinguished from one another based on the core marker set.

In this study, 20 SSR markers were used to analyze the genetic variations among 168 tea accessions or cultivars that collected from Guangxi Province and nine adjacent provinces. The aims of this study were (1) to establish a phylogenetic tree and evaluate the population structure and genetic diversity of Guangxi tea accessions, which will improve understanding about genetic origins and relationships among tea plant accessions/cultivars; (2) to select a core set of markers that can be used to identify Guangxi tea plants. The results will contribute to the identification of tea cultivars and protect the rights of tea plant breeders and farmers.

Materials and Methods

Plant materials and DNA extraction

A total of 168 tea accessions or cultivars, consisting of 126 tea accessions collected from different areas of Guangxi Province and 42 cultivars from nine adjacent provinces, were used in this study. The sampling number and original geographic distribution of the tested tea accessions/cultivars are marked on the map of China (Supplemental Fig. 1). Detailed information about these materials including the accession or cultivar name, location, and the number of markers amplified, is available in Supplemental Table 1. Young leaves from the different tea accessions were collected and promptly frozen in liquid nitrogen, and then stored in a refrigerator at –80°C prior to DNA isolation.

Genomic DNA was extracted from the leaves using the improved cetyltrimethylammonium bromide (CTAB) method (Clarke 2009). The concentration and quality of the DNA samples were measured using Nanodrop 2500 (Thermo Fisher Scientific, USA) and agarose gel electrophoresis, respectively. The concentrations of these DNA samples were diluted to 30–50 ng/μL and then they were subjected to subsequent PCR amplification.

Polymerase chain reaction amplification and product detection

A total of 20 high-quality SSR markers were obtained from previous studies (Liu ), and detailed information regarding their repeat unit, location and primer sequences is available in Supplemental Table 2. The PCR amplification was performed in a 10 μL reaction volume (5 μL 2 × Taq Plus Master Mix, 3 μL ddH2O, 0.5 μL forward and reverse primers, and 1 μL template DNA). The thermal cycling conditions were as follows: 5 min at 94°C for predegeneration, 35 cycles of 30 s at 94°C for denaturation, 30 s at 55–60°C for annealing, and 45 s at 72°C for extension, followed by a final extension of 5 min at 72°C (S1000TM Thermal Cycler, Bio-Rad, USA).

The PCR products that contained SSR fragments were separated on a capillary automated DNA fragment analyzer (Fragment AnalyzerTM 96, AATI, USA). Preparation of the reaction reagents, PCR product mixtures, and protocols was carried out according to the methods outlined in a previous study (Liu ). PROSizeTM 2.0 software (AATI, USA) containing the Fragment AnalyzerTM 96 system was used to intuitively select strong and clear DNA fragments for scoring.

Genetic diversity analysis

PowerMarker 3.25 and Popgene (v1.32) software were used to provide basic summary statistics. Basic summary statistics included the major allele frequency (MAF), number of alleles (Na), Shannon’s information index (I), genetic diversity (GD), observed heterozygosity (Ho), expected heterozygosity (He), and the polymorphism information content (PIC).

Phylogenetic analysis

PowerMarker 3.25 was used to calculate Nei’s genetic distances based on 20 SSR markers. A dendrogram was constructed based on Nei’s genetic distance and the neighbor-joining (NJ) algorithm using MEGA7.0, with a bootstrap value of 1,000 repetitions as the default setting.

Population structure analysis

The Structure 2.3.4 program package was used to analyze the population genetic structure of the tea accessions. The optimum number of subgroups (K) was evaluated based on the mean likelihood values calculated using the ΔK method and the ln P (K) values, which were obtained from the website (http://taylor0.biology.ucla.edu/structureHarvester/). Ten independent runs were performed with a burn-in of 10,000 iterations followed by 10,000 Markov Chain Monte Carlo (MCMC) replications for each K value (ranging from K = 2 to 9). The most likely number of subgroups (K) and the best K value were estimated according to a previous study (Pritchard ).

Core marker set selection

To obtain a reliable and practical core marker set, we applied the following screening criteria: (1) the amplified products had a higher resolution without nonspecific amplification; (2) the molecular markers were highly polymorphic and their polymorphism information content (PIC) values were greater than 0.8; and (3) the difference in fragment size between two adjacent alleles was greater than 3 bp to allow separation by the capillary automated DNA fragment analyzer.

Results

Molecular marker allelic number and polymorphism

Among the 168 tea accessions, 20 SSR markers were identified by PCR and detected by capillary electrophoresis. The amplification results indicated that all the markers showed good polymorphism among the tea samples. At least 15 out of the 20 SSR markers could amplify fragments from all the tea accessions (Supplemental Fig. 2, Supplemental Table 1). The MAF for the 20 markers ranged from 0.187 (CsL58) to 0.658 (CsL82), with an average of 0.329; the Na per locus ranged from 3 (CsL15) to 12 (CsL69), with an average of 8.500 alleles; the I ranged from 0.920 (CsL15) to 2.103 (CsL21), with an average of 1.740 alleles; the GD ranged from 0.546 (CsL82) to 0.862 (CsL58), with an average of 0.777 alleles; the Ho ranged from 0.298 (CsL68) to 0.877 (CsL37), with an average of 0.558 alleles; the He ranged from 0.548 (CsL82) to 0.865 (CsL58), with an average of 0.780 alleles; and the allelic diversity of PIC ranged from 0.478 (CsL15) to 0.847 (CsL58), with an average of 0.748 alleles. Nineteen markers representing 95.0% of the markers had a PIC value greater than 0.5 (Supplemental Table 3). The results indicated that most of the markers were highly polymorphic and will provide a valuable resource for further genetic relationship analyses of tea plants.

Phylogenetic and population structure analysis

Based on Nei’s genetic distance and the neighbor-joining method, a phylogenetic tree of 126 tea accessions from Guangxi Province was constructed using MEGA7.0 (Fig. 1). We then analyzed the phylogenetic and population structure of the 126 tea accessions to further understand the genetic relationships among tea accessions across the different regions in Guangxi Province. All 126 tea accessions clustered into three subgroups (1 to 3), which had 19, 47, and 60 members, respectively, that were mostly consistent with their genetic background or original location (Fig. 1). For example, most of the tea strains collected from ‘Xingxiangcha’ and ‘Dongtantuzhucha’ in eastern Guangxi were clustered together, most of tea strains collected from ‘Fengxiangping’ and ‘Lingyun’ in the west were clustered together, and the majority of tea strains from ‘Guixiang’ and ‘Bantang’ were clustered together. Although the samples clustering results were consistent with the sampling areas, many samples clustering discrepancies with their collection locations. This result may have been caused by the introduction of other varieties to the different regions in Guangxi Province.

Fig. 1.

Sampling information and clustering dendrograms for the 126 tea accessions from Guangxi Province: (A) geographic distribution and sampling number of the tea accessions; and (B) neighbor-joining phylogenetic tree of the 126 tea accessions based on 20 SSRs using Nei’s genetic distance. Tea accessions from Guangxi Province: yellow, tea accessions sampled in Guilin, Liuzhou, and Hechi; green, tea accessions sampled in Baise, Nanning, Laibin, and Chongzuo; purple, tea accessions sampled in Hezhou, Wuzhou, Guigang, and Yulin; and gray, tea accessions sampled in Qinzhou, Fangchenggang, and Beihai.

A population structure analysis of the 126 tea accessions from Guangxi Province based on the 20 SSR markers was conducted and the results were highly consistent with the phylogenetic tree (Supplemental Fig. 3). Pairwise comparisons of the genetic distances showed that there were strong statistical correlations with the phylogenetic tree results, demonstrating that the grouping results from the phylogenetic analysis were highly reliable.

Population genetic diversity

The genetic distances and similarities of subgroup 1, subgroup 2, and subgroup 3 were also calculated (Fig. 2A). The genetic distance data among the three inferred populations were consistent with the estimated data. The genetic distance between subgroup 2 and subgroup 3 was the smallest (0.102), whereas the genetic distance between subgroup 1 and subgroup 3 was the largest (0.373). A phylogenetic tree containing the three populations was constructed, and it showed that subgroup 1 was far from the other two populations (Fig. 2B). A genetic diversity analysis of the three subgroups was also performed (Table 1). The genetic diversity within subgroup 3 was the highest, with Na, I, Ho, He, GD, and PIC values of 7.450, 1.647, 0.576, 0.761, 0.754, and 0.724, respectively. The genetic diversity of the populations, ranked from high to low, was as follows: subgroup 3 > subgroup 2 > subgroup 1.

Fig. 2.

Pairwise genetic distances and the dendrogram for the three inferred populations of the tea accessions from Guangxi Province: (A) genetic distance (below diagonal) and similarity (above diagonal) of subgroups 1, 2, and 3; and (B) dendrogram among the subgroups based on Nei’s genetic distance.

Table 1.

Genetic diversity of the classified three subgroups

Subgroup	SS	Na	I	Ho	He	GD	PIC
Subgroup 1	19	6.700	1.556	0.458	0.745	0.726	0.693
Subgroup 2	47	7.350	1.612	0.568	0.757	0.749	0.715
Subgroup 3	60	7.450	1.647	0.576	0.761	0.754	0.724

SS, sample size; Na, the number of alleles; I, shannon’s information index; Ho, observed heterozygosity; He, expected heterozygosity; GD, genetic diversity; PIC, polymorphism information content.

Genetic relationship analysis

To study the genetic exchange among the 126 tea accessions from Guangxi Province and the 42 tea cultivars from nine adjacent provinces, we conducted a genetic relationship analysis based on these 168 tea accessions/cultivars. As a result, the 168 tea samples were clustered into three main groups (α, β, and γ), and group α was further divided into subgroups α-1 and α-2 (Fig. 3A). Among these, group α contained most of the tea accessions from Guangxi, a majority of the Guangxi tea accessions were in subgroup α-1, and subgroup α-2 contained 73 Guangxi tea accessions and several tea cultivars from the other provinces (2 from Anhui, 2 from Hunan, 2 from Fujian, 1 from Sichuan, and 1 from Guizhou). Group β contained 24 members from Guangxi, 11 from Yunnan, 1 from Guangdong, and 1 from Guizhou Province, respectively. Subgroup γ contained different numbers of tea cultivars from other provinces, such as Fujian (6), Zhejiang (3), Guangdong (3), Hubei (2), Anhui (2), Hunan (2), Sichuan (1), Yunnan (1) Provinces, and the remaining 10 members from Guangxi Province were clustered into subgroup γ.

Fig. 3.

Phylogenetic and population structure analysis of 168 tea accessions or cultivars. (A) Neighbor-joining phylogenetic tree based on 20 SSR markers for the 126 tea accessions from Guangxi and 42 tea cultivars from nine other adjacent provinces using Nei’s genetic distance. Red dots represent tea cultivars obtained outside Guangxi Province. (B) Graphical method to detect the number of K groups using lnP (K) and delta K. The K number was set from 2 to 9. (C) Q-plot of the population structure when K = 3, where the colors (blue, gray, and red) represent the three subgroups. Tea accessions or cultivars showing more than one color may have genetic introgression resulting from hybridization.

A population structure analysis was performed on the 168 tea accessions based on 20 markers. The maximum likelihood parameter was analyzed using ΔK = 4 (Fig. 3B). The 168 tea accessions were clustered into four populations (I, II, III, and IV) (Fig. 3C). By comparison, the classification of most tea accessions in subgroups I, II, III, and IV, based on population structure analysis, corresponded to the clustering analysis for groups β, γ, α-1, and α-2, respectively. Furthermore, most of the tea accessions in subgroup IV were from Guangxi Province (66), and 1 tea cultivar was from Hunan Province. A series of strains, including Tianexiandulong (3), Xingxiangcha (8), and Dongtantuzhucha (2) from Guangxi were grouped into subgroup III. Subgroup I contained 17 tea accessions from Guangxi, 12 from Yunnan, 2 from Guangdong, 1 from Guizhou, and 1 from Anhui. The remaining tea accessions from Guangxi (26), Fujian (8), Zhejiang (4), Anhui (3), Hunan (3), Hubei (2), Guangdong (2), Sichuan (2), and Guizhou (1) were grouped into subgroup II. Combining the results of the cluster and population structure analyses, indicated that the strains from the Hepu, Bantang, and Lingshan regions showed genetic introgression with the tea cultivars from Yunnan, Guangdong, and Guizhou. Furthermore, the strains from Wutong and Guixiang showed genetic introgression with tea cultivars from Fujian, Hunan, Hubei, Sichuan, Anhui, and Zhejiang.

Fingerprinting of tea plant accessions

Based on our results, the SSR markers were evaluated for their ability to distinguish tea germplasms. Ultimately, six markers (CsL06, CsL21, CsL37, CsL60, CsL65, and CsL69) were selected as the core marker set, and their allele sizes and frequencies are summarized (Fig. 4). The 126 tea accessions were distinguished by analyzing the size of the DNA fragments amplified by the core marker set. As a result, a unique fingerprinting for each tea accession was established based on marker order and allele size (Table 2), and their fingerprint codes could be further transformed into two-dimensional barcodes based on the website (https://cli.im/). For instance, one of the most important national clonal tea cultivars, C. sinensis var. ‘Guihong 3’ had ten unique amplified fragments from the core marker set. The six markers and their corresponding fragment sizes were CsL06 (158 bp and 175 bp), CsL21 (216 bp and 237 bp), CsL37 (197 bp and 214 bp), CsL60 (339 bp), CsL65 (290 bp and 306 bp), and CsL69 (319 bp), which demonstrated a unique fingerprint profile based on marker order and fragment sizes (Fig. 5A). Similarly, the national clonal tea cultivar, C. sinensis var. ‘Guilv 1’ and C. sinensis var. ‘Yaoshanxiulv’ had ten and eleven unique amplified fragments, respectively (Fig. 5C, 5E). The two-dimensional barcodes for C. sinensis var. ‘Guihong 3’, C. sinensis var. ‘Guilv 1’, and C. sinensis var. ‘Yaoshanxiulv’ were established, and contained their scientific name, germplasm type, cultivation region, and fingerprinting code (Fig. 5B, 5D, 5F). These results showed that the core marker set could be used to construct fingerprints of various tea accessions from Guangxi Province.

Fig. 4.

Evaluation of the fingerprinting power of six core markers in the 126 tea accessions from Guangxi Province. (A) Transferability and polymorphism detected by six core markers among the 126 tea accessions, where M represents the marker ladder. (B) Allele sizes and frequencies of the 126 tea accessions based on genotyping data, where ‘0’ indicates that no DNA fragment has been amplified.

Table 2.

Fingerprinting codes for the 126 tea plant accessions from Guangxi Province based on six SSR markers

Sample	Fingerprinting code	Sample	Fingerprinting code
1	A157–180 B214–231 C199–216 D364 E300–308 F352	24	A177–187 B216–227 C200–213 D0 E288–306 F307
2	A178–187 B216–239 C198–226 D336 E288 F290–307	25	A161–180 B216–229 C207–223 D341–356 E286 F307
3	A154–178 B214–237 C196–223 D348 E303 F306	26	A154–162 B215–229 C207–220 D340 E278–291 F307
4	A153–178 B223–227 C200–205 D362–370 E299 F306–318	27	A177 B216–244 C208–229 D356 E286–304 F306–324
5	A153–162 B218–227 C206–228 D340–355 E279–289 F305–320	28	A158–182 B217–229 C208–218 D338 E291–308 F320
6	A160 B225 C198–223 D349–357 E288–299 F300	29	A157–185 B212–234 C198–208 D351–365 E286 F301
7	A156–175 B222–246 C195–204 D348–355 E281 F305	30	A179–188 B218–242 C197–218 D348 E286–295 F322–334
8	A162–175 B222–247 C208–216 D347–353 E288–311 F309–320	31	A156 B228 C197 D332 E278–293 F308–320
9	A155–171 B214–239 C196–225 D347 E286–311 F305–319	32	A159–182 B210–218 C199–219 D331–354 E294 F308–321
10	A172–187 B211 C206–227 D347 E286–296 F293–305	33	A156 B218–245 C202 D354 E275 F308
11	A154–188 B224–248 C198–208 D340 E301 F305–323	34	A171–180 B218–242 C220 D335–345 E286–300 F306
12	A159–184 B222–227 C198–211 D341–355 E306 F307	35	A159–187 B228 C192–206 D350–360 E275–291 F307–330
13	A153–180 B215–233 C207 D340 E295 F308–335	36	A165–169 B214–237 C196–205 D341–355 E285 F309–319
14	A158–187 B223–232 C199–208 D332 E291–300 F319	37	A166–180 B214–237 C198–211 D0 E289 F295
15	A156–178 B214–238 C198–209 D340–349 E291 F305	38	A156–169 B218–235 C205 D328 E278–288 F303
16	A173–199 B216–231 C197–215 D348 E286 F307	39	A180–187 B219–243 C205–224 D355 E281–293 F306
17	A158–187 B212–236 C200–212 D342 E284 F313–347	40	A169–175 B212–236 C210–221 D339 E275–286 F302–309
18	A188 B227 C208–222 D340 E276–291 F322	41	A157 B216–226 C198–204 D347 E289 F302–320
19	A156–186 B217–241 C201–208 D347 E285–295 F307	42	A161–182 B216–240 C209–223 D348 E291 F304–314
20	A156 B224–239 C197 D330–349 E291–300 F308–320	43	A158–176 B214–237 C211–224 D355 E290 F314
21	A158–187 B216–232 C194–213 D340 E299 F307–319	44	A163–176 B230–257 C195–210 D348–364 E288–306 F301
22	A158–175 B216–237 C197–214 D339 E290–306 F319	45	A157 B210 C199 D335–353 E299 F307
23	A162–182 B214–220 C205–229 D362 E286–306 F293–307	46	A157 B219–226 C204–222 D359 E285–295 F303–319
47	A154–182 B217–229 C203–208 D338–348 E288–295 F291–312	70	A164–188 B218 C201–218 D349 E290 F347
48	A174–184 B217–229 C197–204 D338 E288–296 F305–320	71	A187 B214–237 C205 D348 E284 F319
49	A176–188 B222–247 C194–203 D348 E291 F282–305	72	A161 B214 C197–208 D354 E300–308 F300–307
50	A150–188 B218–241 C207–231 D328 E291 F309–335	73	A149–164 B211 C202–222 D353 E288 F312
51	A162 B210 C198–224 D350 E274–306 F306–323	74	A161–174 B218–243 C196–210 D338–357 E284 F347
52	A155–165 B220–243 C207–221 D360 E288 F314–326	75	A171 B213–237 C194–204 D0 E284 F318
53	A173–189 B216–231 C210–235 D348–355 E284–295 F301–307	76	A157–167 B224–249 C196–217 D359 E276–295 F279–296
54	A188 B210–234 C199–212 D329 E281 F295–307	77	A174 B230–256 C194–207 D355 E285 F300
55	A152 B214–233 C200–214 D334–348 E275–295 F308–320	78	A157 B213–222 C193–202 D354 E281–288 F303
56	A171–184 B214–237 C197–205 D359 E281–290 F300	79	A149 B0 C193–199 D353–360 E259 F312–338
57	A161–188 B228–253 C197 D331–358 E288 F293–322	80	A155 B214 C203–220 D325 E287 F318
58	A155 B222–247 C196 D328–359 E294 F301–324	81	A153–182 B230 C195–214 D359 E260 F315
59	A153–180 B213–236 C201–219 D328 E282–290 F303	82	A151–188 B230–256 C194–214 D358 E261–276 F311–342
60	A153–181 B226–232 C204 D328 E289 F281	83	A160–190 B211–234 C199–209 D350 E286 F307
61	A161 B233 C198–214 D354 E289–300 F295	84	A151–162 B228 C209–231 D347 E275–297 F295–322
62	A155–166 B214–239 C196–203 D330–348 E291 F295	85	A153–172 B216–241 C209–221 D327–349 E280–305 F309–324
63	A173 B209–216 C203–223 D335 E276–291 F306–323	86	A163–169 B212–226 C210–224 D350 E290 F321
64	A159–187 B226 C198–209 D335 E295 F307–325	87	A170–187 B223–248 C212–224 D335 E295–306 F321
65	A154–188 B216–229 C205–229 D0 E295–303 F322	88	A150–189 B218–241 C200–210 D355 E287–315 F322
66	A176 B214–240 C208–230 D348 E285–310 F290–304	89	A181–187 B210–224 C202–212 D339–350 E296–310 F323
67	A155–169 B214–232 C193–200 D347 E290 F307–317	90	A170–185 B211–237 C198–208 D338–349 E285–307 F321
68	A154 B214–237 C198–208 D347 E292–311 F347	91	A170–181 B217–242 C196–213 D359 E285 F296–309
69	A172–185 B217 C207–213 D348 E278–288 F292–313	92	A174 B223 C206–225 D355 E287 F308
93	A154–180 B231–253 C196–214 D352–366 E270–286 F307–326	110	A167–175 B211–234 C199–227 D349 E283 F301
94	A163 B231–246 C198–214 D350–364 E269–287 F317–326	111	A161–182 B233 C208–229 D358 E285 F340
95	A166–176 B211–231 C195–213 D351–362 E266 F316–326	112	A169–191 B214–237 C210–234 D347 E296–314 F322
96	A164 B212 C193–213 D360 E265 F299–304	113	A160–187 B210–229 C197–203 D329 E306 F307
97	A167–178 B231 C198–216 D355–372 E283 F317–327	114	A175 B214–237 C196–207 D339–355 E284 F300
98	A155–195 B246 C196–215 D355–372 E261 F300–332	115	A159 B223–241 C209–221 D340 E275–289 F313
99	A152–165 B0 C191–210 D361–371 E261 F298–329	116	A187–193 B213–237 C208–215 D347 E286–311 F297–321
100	A166–186 B221–240 C196–214 D352–370 E267–287 F314	117	A170–190 B212–235 C212–235 D341 E291–307 F295
101	A184 B0 C212–217 D355 E272 F298–314	118	A170–189 B212–234 C213–235 D347 E285–309 F314
102	A175–185 B236 C200–218 D355 E264–273 F307	119	A158–167 B228 C209–230 D338–349 E280–287 F306–320
103	A162–175 B0 C202–218 D0 E0 F296	120	A169–187 B234 C201–230 D348 E292 F305–329
104	A168–188 B228–253 C211 D339 E286–308 F320–333	121	A188–209 B0 C196–203 D340–355 E0 F308–326
105	A193 B217–234 C203–209 D348 E300 F330–337	122	A159–172 B222–246 C204–212 D347 E284 F305–327
106	A188–202 B213–237 C209–214 D343–365 E290 F317–324	123	A160 B226–246 C204–213 D332–358 E291 F322
107	A158–175 B217–241 C204–225 D334–354 E298 F296–302	124	A161–173 B224 C209–225 D350–358 E292 F322
108	A175–184 B216–236 C199–222 D350–358 E271 F301	125	A173–187 B211–234 C200–208 D340 E307–327 F314
109	A157 B214–237 C213–235 D344 E287 F298	126	A188 B212–237 C210–231 D334–359 E284–294 F315–334

Note: A, B, C, D, E and F represent the marker CsL06, CsL21, CsL37, CsL60, CsL65, and CsL69, respectively. “–” was used between two bands amplified by one marker.

Fig. 5.

Sizes of DNA fragments and the fingerprinting codes of three national tea cultivars based on the six core markers. The dye-labeled PCR products were separated on a Fragment analyzerTM 96 and visualized using PROsize2.0 software. (A, C, E) Gel pictures of the amplified DNA fragments. (B, D, F) DNA fingerprinting codes for the three national tea cultivars: ‘Guihong 3’, ‘Guilv 1’, and ‘Yaoshanxiulv’. Pictures of the DNA fingerprinting codes can be scanned by computers or a mobile device. The retained information is contained in the picture, including the scientific name, germplasm type, cultivation region, and fingerprinting code.

Discussion

In recent years, high-quality assembled tea plant genome sequences have been published (Wei , Xia ), which greatly improves the efficiency of functional and comparative genomics research. Subsequently, genome-wide SSR markers have been successfully developed, and many SSR markers with high polymorphism and good stability have been verified (Liu ). The obtained polymorphic bands not only indicate the polymorphism among primers, but also reflect the degree of genetic variation in a population (Liu and Muse 2005). In this study, SSR markers were used to analyze the genetic diversity, phylogenetic relationships, population structure, and fingerprints of 126 tea accessions from Guangxi Province. The results indicate that some tea accessions in Guangxi show genetic introgression with tea cultivars from neighboring provinces. The 126 tea accessions were clustered into three subgroups, and the order for genetic diversity of the populations was subgroup 3 > subgroup 2 > subgroup 1. In addition, six markers were used to construct fingerprint, and the unique fingerprint profiles of these 126 tea accessions were determined. These findings will be useful for breeding programs and the cultivar/accession identification of tea plant resources in Guangxi Province and other regions.

The phylogenetic tree can not only reflect the pedigree relationship, but can also reflect the geographic distance between plant species and cultivars (Yao ). The 126 tea accessions were grouped into three major clusters based on Nei’s genetic distance. The results demonstrated that most tea plant resources with similar geographical backgrounds were clustered together. Notably, the genetic information about germplasms in a new environment may change after frequent artificial introductions (Tan ). After clustering, we found that the strains from Tianexiandulong and Dongtantuzhucha in northern Guangxi were clustered together with Xingxiangcha in eastern Guangxi. This finding might be explained by parental line exchanges among breeders in various regions. Population structure analyses using different methods have become increasingly reliable and could be used as a reference. For example, Niu analyzed the population structure of 415 tea varieties has been analyzed using three different approaches (Structure, PCA, and UPGMA methods). To further verify their phylogenetic relationships, we analyzed the population structure of the 126 tea accessions and the results for subgroups A, B, and C were highly similar to the NJ clustering results, which showed that the clustering results were highly reliable (Supplemental Fig. 3).

The self-incompatibility and cross-pollination abilities of tea plants mean that tea germplasm resources show high genetic diversity (Charlesworth ). Studying the genetic diversity of tea plants could not only increase our understanding about their evolutionary history and environmental adaptability, but could also contribute to the protection and utilization of tea germplasm resources (Balasaravanan ). Specifically, the average Na number was 8.500, which is significantly higher than that reported in other studies on tea plant collections (Liu , 2019). Analogously, higher average He (0.780) and PIC values (0.748) were produced across our set of genotypes compared to those from previous studies (Liu , 2019). These results indicate that the markers can be reliably used to reveal the genetic diversity level and verify that the tea accessions collected from Guangxi have relatively high genetic diversity. A major reason for this diversity could be that Guangxi Province is adjacent to Yunnan Province, which is considered to be the original center for tea plants and the distribution center for species diversity (Meegahakumbura ). Additionally, geographical isolation may be another important reason for their complex genetic backgrounds. The kinship of different resources is determined by their genetic background, they are more similar if their genetic backgrounds are closer (Hunter ). Under normal circumstances, local tea varieties in the same area have a closer kinship. It is worth mentioning that the average genetic diversities of individuals among the three subgroups were different. This result may be due to differences in genetic background and environmental factors (Shiva ). Generally, the population genetic diversity level is related to population size, and larger populations generally have higher levels of genetic diversity (Lei ).

Analyzing the phylogeny and population genetic structure can accelerate the development of breeding strategies (Niu ). Therefore, we analyzed the genetic relationships among the 168 tea accessions/varieties (126 from Guangxi Province and 42 from nine adjacent provinces) using two different methods (NJ and Structure). The clustering and population structure analysis of the tea accessions and varieties showed clear separation based on geographic origin. Based on our results, we found that some tea accessions from Guangxi Province showed genetic introgression with tea cultivars from neighboring provinces. Genetic exchange between cultivars is mostly determined by human activities rather than natural factors. Our results suggest that breeders may have introduced seeds from other provinces or crossbred their plants with tea varieties from other provinces, which would lead to gene flow. However, due to the limited spatial distribution of seeds and pollen among distant populations, Guangxi tea and local varieties in neighboring provinces have a certain geographical structure.

Currently, a problem that frequently occurs with introduced species is either confusion or fraud, which greatly damages the patent and economic benefits to owners. For tea germplasm resources, it is particularly important to identify their cultivars or varieties. Compared to traditional morphological and biochemical identification, DNA identification has the advantages of being efficient and unaffected by environmental factors, and it has gradually become the most effective method for plant variety identification (Liu ). The construction of fingerprints generally follows these principles: distinguishing all samples with as few primers as possible, reducing the experimental cost, reducing the heavy experimental work, and increasing the detection efficiency (Liu , Rakshit ).

At present, single molecular marker fingerprinting technology is widely associated with various plant resources, but researchers prefer to use a marker set (composed of two or more markers) to analyze and evaluate of plants because they can obtain more accurate and reliable DNA fingerprints. For example, Awasthi constructed 15 mulberry material DNA fingerprint libraries using 19 RAPD markers and four ISSR markers, and Tan constructed fingerprints for 128 Chinese clonal tea cultivars using a core marker set containing eight SSR markers. In this study, 6 out of 20 SSR markers were selected as the core marker set and molecular fingerprints for the 126 tea accessions from Guangxi Province were constructed. The fingerprints of the 126 tea accessions reported here are valuable data that can be used by new breeding programs and other genetic studies on tea germplasms.

Author Contribution Statement

RG performed the DNA isolation, SSR genotyping, PCR amplification, testing of the amplified fragments, data analysis, and drafted the manuscript. XBX and XZM were involved in DNA isolation and PCR amplification. SRL and YLA provided SSR markers, and coordinated the data analysis. CJ, RL, CZ, and MYC were involved in DNA isolation and amplification of fragments. CLW and SRL were involved in the collection of tea plant materials, experimental design, and manuscript preparation. All authors read and approved the final manuscript.

Supplemental Figures

Supplemental Table 1

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Supplemental Table 3