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Article

Genetic Analysis and Construction of a Fingerprint for Licensed Triadica sebifera Cultivars Using SSR Markers

by
Qi Zhou
,
Baiqiang Chen
,
Dongyue Jiang
,
Fei Zhuge
and
Yingang Li
*
Zhejiang Academy of Forestry, 399 Liuhe Road, Hangzhou 310023, China
*
Author to whom correspondence should be addressed.
Plants 2024, 13(13), 1767; https://doi.org/10.3390/plants13131767
Submission received: 22 May 2024 / Revised: 18 June 2024 / Accepted: 24 June 2024 / Published: 26 June 2024
(This article belongs to the Special Issue Molecular Biology of Ornamental Plants, Volume II)
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Abstract

:
Triadica sebifera is an important landscaping tree species because of its colorful autumn leaves. In recent years, some cultivars have been bred and licensed, but it can be difficult to identify them from their morphological traits due to their similar phenotypes. To explore the genetic relationships and construct a fingerprint of the cultivars, the licensed T. sebifera cultivars were analyzed using SSR markers. A total of 179 alleles were identified among the 21 cultivars at 16 SSR loci, and these alleles exhibited a high level of genetic diversity (He = 0.86). The genetic variations mainly occurred among cultivars based on an analysis of molecular variance (AMOVA). According to phylogenetic analysis, principal coordinate analysis (PCoA), and Bayesian clustering analysis, the genetic relationships were independent of geographic distances, which may be mainly due to transplantations between regions. Some cultivars with different leaf colors showed obvious genetic differentiation and may be preliminary candidates for cross-breeding. Finally, the fingerprint for the licensed cultivars was constructed with two SSR markers. The results of this study can provide technical support for the application and legal protection of licensed Triadica sebifera cultivars.

Graphical Abstract

1. Introduction

Triadica sebifera is a native tree species of China and is mainly distributed in areas south of the Qinling Mountains and Huaihe River [1]. The seeds have been used for candles, heating oils, and cocoa butter equivalents since the late 1980s [2,3,4]. The leaves and roots can be used for herbal medicines [5]. In addition, T. sebifera is also an important tree with colored leaves and is often used in landscaping. The colors of T. sebifera leaves in autumn are relatively diverse, such as purple, red, orange, and yellow. In recent years, ornamental T. sebifera breeding has been carried out, and some ornamental colored cultivars have been developed [6,7]. There are 21 licensed T. sebifera cultivars in China with different autumn colors and different leaf traits, and these cultivars are also valuable materials for breeding. In general, the parents and the genetic characteristics of the germplasm are very important for ensuring that the target material or gene is passed on through breeding [8]. However, T. sebifera cultivars have mainly been derived from natural crosses, and their genetic backgrounds and relationships are also unclear, which is not conducive to the selection of desirable parents for cross-breeding [9]. Moreover, some cultivars also have similar phenotypes due to their closely related origins, and it is difficult to distinguish them based on morphological traits with obvious seasonal variations. Hence, there are homonyms (different cultivars with the same name) and synonyms (the same cultivar with different names) [10]. In order to apply and protect these cultivars, several questions remain to be resolved, such as the following: What are the genetic relationships and variations among the different T. sebifera cultivars? How can they be precisely identified without seasonal or locational limitations?
DNA-based molecular markers, including random amplified polymorphic DNA (RAPD) [11], intersimple sequence repeat (ISSR) [12], simple sequence repeat (SSR) [13], and single nucleotide polymorphism (SNP) [14], have been used for genetic analysis over the past several decades. Notably, SSRs are more consistent than RAPD, more polymorphic than ISSRs, and more easily genotyped than SNPs, and have been used in the analysis of genetic relationships and in the genetic identification of plants [15,16,17]. DNA fingerprinting is a molecular approach for identifying different cultivars [18]. To date, a series of fingerprinting databases based on SSRs have been established for plant species such as Ginkgo biloba [19], Ailanthus altissima [20], Asparagus officinalis [21], and Juglans regia [22]. These databases are important for cultivar identification and protection of cultivar rights. To date, studies on T. sebifera have focused mainly on phylogenetic relationships, chemical components, and biological activity [2,23,24]. In terms of molecular markers, several SSR markers have been developed and applied in the genetic analysis of T. sebifera populations [1,25,26,27]. Hence, SSR markers may be an effective method to study the genetic relationship and fingerprinting of T. sebifera cultivars.
In this study, SSR markers were selected for the genetic analysis of T. sebifera cultivars. The aim of this study was not only to explore the genetic diversity and genetic differentiation of T. sebifera cultivars but also to construct molecular fingerprinting for the cultivars. The findings of this study can guide the selection of ideal parents for T. sebifera breeding and can provide a technical basis for the protection of cultivar rights.

2. Results

2.1. Genetic Diversity

A total of 179 alleles were detected among the 16 SSR loci, ranging from 7 to 19. The average expected heterozygosity (He) and Shannon’s information index (I) were 0.86 and 2.12, respectively (Table 1). The SSR08 locus had the highest He (0.96), I (2.85), and polymorphism information content (PIC = 0.94) values, which revealed more abundant genetic information than the other loci. There were fewer alleles detected at SSR03 (7) and SSR14 (8) than at the other loci (9~19), and the level of genetic diversity at these two loci was lower than that at the other loci. There was a high proportion of T. sebifera homozygotes at 16 SSR loci. The observed heterozygosity (Ho) at 12 pairs of SSR loci was 0, while the Ho at the other 4 loci was much lower than the He.
The genetic diversity of the T. sebifera cultivars differed among regions (Table 2). The number of cultivars varied among different regions, and genetic diversity was abundant in regions with a high number of cultivars. There were more cultivars from Jiangsu (8) than from the other areas (2~7), and these cultivars had the highest number of alleles (Na = 6.13), He (0.84), I (1.68), and allelic richness (AR = 3.15) values. There were only two cultivars from Hubei, and their genetic diversity was lower than that of the cultivars from the other areas.

2.2. Genetic Differentiation

Analysis of molecular variance (AMOVA) revealed significant genetic variation among regions, among cultivars and within cultivars (Table 3; p < 0.01). Most of the genetic variations were detected among cultivars (88%). There was only 4% and 8% genetic variation among regions and within cultivars, respectively.
To analyze the genetic relationships among the cultivars, principal coordinate analysis (PCoA) was carried out. The genetic relationships of the T. sebifera cultivars were independent of their origins (Figure 1). Only the cultivars from Hubei (Canlan and Huihuang) showed a close genetic relationship, while there were obvious genetic differences among the cultivars from other shared regions (Anhui, Jiangsu, or Zhejiang). Moreover, Bayesian clustering analysis and phylogenetic tree construction based on the neighbor-joining method were also carried out to analyze the genetic structure (Figure 2). The optimal K value was 4, which indicated that the cultivars were clustered into four groups (Figure S1). Most of the PCoA, Bayesian clustering analysis, and phylogenetic tree results were similar. However, according to the phylogenetic tree, the cultivar ‘Huangjinjia’ showed significant genetic variation and clustered into one group.

2.3. Unique Alleles

The frequencies of the 179 alleles at 16 SSR loci ranged from 0.0238 to 0.4524, and a total of 105 alleles were unique alleles, which were detected in only one cultivar (Table 4). Among the 16 SSR loci, there were 15 unique alleles at SSR08, while SSR03 and SSR14 had only 2 unique alleles. All the T. sebifera cultivars had unique alleles. The cultivar ‘Feiyunzhaoshui’ had the most unique alleles (10) at 10 SSR loci, the cultivars ‘Qiuyan 01’, ‘Qiuhuang 01’, and ‘Puhongjiu’ each had 8 unique alleles, and the cultivar ‘Zimanao’ had only one unique allele (156 bp at SSR12).

2.4. Fingerprinting of Cultivars

To distinguish and identify the T. sebifera cultivars, a fingerprint was constructed based on the alleles at 16 SSR loci. It was not possible to distinguish all the T. sebifera cultivars with only one SSR locus. The PIC of SSR08 and SSR12 were 0.94 and 0.90, respectively, which were higher than those of the other SSR loci. A total of 15 of the 21 T. sebifera cultivars were distinguished when neither SSR08 nor SSR12 was used. In total, the 21 T. sebifera cultivars were successfully distinguished by combining SSR08 and SSR12 (Figure 3).

3. Discussion

Genetic diversity contributes to the ability of a species to adapt to environmental changes and is valuable for evolution and conservation [28,29]. In plant breeding, genetic diversity can be described as the range of genetic characteristics and is an important resource for the development of new cultivars [30]. In this study, the 21 T. sebifera cultivars exhibited high genetic diversity (He = 0.86; I = 2.12). The genetic diversity of T. sebifera was greater than that of forest and fruit tree taxa such as Populus [31], Olea europaea [32], Corylus avellana [33], Prunus avium [34], and Malus pumila [35]. Moreover, there were significant differences in the phenotypic traits of the 21 T. sebifera cultivars, which may be a reflection of the high level of genetic diversity [36]. In general, natural populations may contain abundant genetic diversity [37]. The wild Ipomoea batatas individuals have a higher degree of genetic diversity than the cultivars [38]. However, the genetic diversity of widespread T. sebifera populations (He = 0.491) is much lower than that of T. sebifera cultivars [1]. The main reason may be that a wide range of T. sebifera trees have been destroyed or transplanted, which may lead to a loss of both germplasm and genetic diversity. Cross-breeding of plants, including natural and artificial hybrids, is an important approach for the generation of new cultivars. In plant breeding, large numbers of genetically stable and homozygous individuals are necessary, and complete homozygote breeding materials are rapidly obtained with doubled haploid technology, which is currently a routine part of breeding programs [39,40]. In this study, according to the 16 pairs of SSR markers, most of the cultivars had homozygous genotypes, which may be conducive to the inheritance of target genetic information in cross-breeding. In contrast, Prunus armeniaca [41] and Prunus domestica [9] cultivars have a high proportion of heterozygotes, and the Ho are 0.63 and 0.88, respectively. The main reason may be that T. sebifera cultivars have more inbreeding than P. armeniaca and P. domestica cultivars.
Some factors, such as geographic distance and selection, may influence genetic differentiation among populations [42]. Cultivars from different regions can also show genetic differentiation, and the cluster results of pear cultivars show a good fit with geographic distances [43]. However, the AMOVA in this study showed that most of the genetic variation was among cultivars (88%), and only 4% of the genetic variation was detected among regions. There were also limited genetic variations among regions in Panax ginseng and Cajanus cajan cultivars [44,45]. The main reason may be the gene flow between regions. The cultivars within the same region showed obvious genetic differentiation based on the PCoA, Bayesian clustering analysis, and phylogenetic tree results, indicating that the clustering result is independent of geographic distance. The T. sebifera cultivars from different regions also have similar phenotypes [36]. T. sebifera, an important economic tree species, has been widely distributed and transplanted across different regions over the last century due to its strong adaptability, which may be the reason for the low genetic differentiation among regions [1]. Genetic relationships between parents during cross-breeding may influence outcomes in genome-assisted breeding, and the close genetic distance between hybrid parents may cause hybrid disadvantages, including hybrid inactivation and hybrid decline [46,47]. Currently, T. sebifera is mainly used in landscaping because of its colorful leaves in autumn, and the main trait that is targeted in breeding is leaf color. Several cultivars with different leaf colors, such as ‘Puhongjiu’ (red), ‘Haibinfeihong’ (purple), ‘Huangjinjia’ (yellow), and ‘Zhaoxia’ (orange), exhibit great genetic differences and could be selected by strategic breeding of the parents. In contrast, complex genetic relationships may influence the study of the molecular regulation of leaf color via multiple omics analyses [48,49]. Therefore, cultivars with close genetic relationships, such as ‘Huihuang‘ (yellow) and ‘Zhengyan‘ (purple), may be good research materials for multiple omics analyses aimed at revealing the genetic basis of leaf color.
Given that T. sebifera cultivars are sometimes difficult to distinguish based on phenotypes, the inability to accurately identify the cultivars limits their applied use and the protection of cultivar rights. On the one hand, the main difference among T. sebifera cultivars is autumn leaf color, which is difficult to ascertain in other seasons when the leaves have not yet changed color. On the other hand, the phenotype of plants is controlled by both genetic and environmental factors, and a cultivar planted in different environments may have different morphological traits, which may cause misidentification and confusion [50]. When the T. sebifera cultivars were planted under the same conditions, some of them also had similar phenotypes [36]. Here, molecular fingerprinting is an accurate identification method without seasonal or locational limitations that relies on unique alleles and genotypes. In this study, there were 105 unique alleles identified at 16 SSR loci, which suggests that the genetic information among cultivars is noticeably different. In previous studies, fingerprinting of 10 Trifolium repens cultivars was performed using three representative SSR primers [10], while 24 A. officinalis cultivars were fingerprinted using three SSR primers [21]. However, the geographic origins of 110 rice cultivars cannot be fully identified only with SSR markers [51]. In the present study, just one SSR marker (SSR08 or SSR12) enabled approximately 70% of the cultivars (15) to be distinguished. Finally, the fingerprinting was constructed with only two SSR markers (SSR08 and SSR12). Hence, it can be speculated that the large quantity of polymorphic SSR markers and the high level of genetic differentiation among cultivars are important factors for the successful construction of a fingerprint with a small number of markers.

4. Materials and Methods

4.1. Plant Materials

To date, a total of 21 T. sebifera cultivars have been licensed in China [36] (Table 5; Figure 4). The ‘Puhongjiu’ cultivar was approved by the Zhejiang Forestry Administration, while the other 20 cultivars were approved by the National Forestry and Grassland Administration. The scions of these cultivars were grafted onto two-year-old rootstocks, and the clones of these cultivars were cultivated in the T. sebifera germplasm nursery at Zhejiang Academy of Forestry Base (30°13′ N, 120°01′ E, 19 m above sea level). Young leaves of the 21 cultivars were collected and stored at −80 °C for later use.

4.2. DNA Extraction

Whole-genomic DNA was extracted using the DNeasy Plant Mini Kit (Qiagen, Hilden, NRW, Germany). The DNA was measured with a NanoDrop-1000 spectrophotometer (NanoDrop Technologies, Wilmington, DE, USA) and 1% agarose gel electrophoresis, and DNA with no significant degradation was used for polymerase chain reaction (PCR). Finally, the DNA was diluted to 60 ng/μL and stored at 4 °C for later use.

4.3. Genotyping with SSR Markers

A total of 39 SSR markers were selected from the references and tested via PCR amplification of six random individuals [1,25,27]. Sixteen of these SSR markers yielded clear and polymorphic products, and the upstream primers were fluorescently labeled with 5-Carboxyfluorescein (Table 6). The amplification of all samples at the 16 SSR loci was completed with an ABI Veriti 96 PCR system (Thermo Fisher Scientific, Waltham, MA, USA). The reaction mixtures (20 μL) contained 60 ng of DNA, 10 μL of 2 × TSINGKE Master Mix, and 0.2 μM of each SSR forward and reverse primer. The PCR program involved a predenaturation step of 4 min at 94 °C, followed by 30 cycles at 94 °C for 20 s, the appropriate annealing temperature for 30 s, and 72 °C for 90 s, and an extension step of 1 min at 72 °C. The PCR products of the amplifications were subjected to capillary electrophoresis using an ABI 3730xl instrument (Thermo Fisher Scientific, Waltham, MA, USA), and the fluorescent PCR bands were recognized. Finally, the genotype of each marker was analyzed using Peak Scanner v 1.0 (Thermo Fisher Scientific, Waltham, MA, USA) [52], and the base sizes of the bands were calculated with standard markers. The bands with the same base sizes were the same alleles in each pair of SSR markers. The alleles amplified in only one cultivar were unique alleles.

4.4. Data Analysis

The number of alleles (Na), effective number of alleles (Ne), observed heterozygosity (Ho), expected heterozygosity (He), Shannon’s information index (I), polymorphism information content (PIC), and phylogenetic analysis were calculated using PowerMarker version 3.25 (North Carolina State University, Raleigh, NC, USA) [53]. The allelic richness (AR) was measured with FSTAT version 2.9.3 (University of Lausanne, Lausanne, VD, Switzerland) [54]. Analysis of molecular variance (AMOVA) and principal coordinate analysis (PCoA) were performed with GenAlEx version 6 (Rutgers University, New Brunswick, NJ, USA) [55]. To study the genetic relationships among the cultivars, Bayesian clustering analysis was performed using Structure version 2.3.1 (Stanford University, San Francisco, CA, USA) [56].

5. Conclusions

The 21 licensed T. sebifera cultivars were analyzed using SSR markers, which revealed a high level of genetic diversity. The licensed cultivars showed homozygous genotypes at most SSR loci, which may be conducive to ensuring the inheritance of target genetic information in cross-breeding. Most of the genetic variation identified occurred among cultivars, and the genetic differentiation between them was independent of geographic distance, which may be mainly due to transplantations of this species across regions. T. sebifera cultivars with different leaf colors and obvious genetic differences were deemed to be important parents for cross-breeding. Finally, a fingerprint of the licensed T. sebifera cultivars was constructed with two markers.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/plants13131767/s1, Figure S1. Relationships between the number of clusters (K) and the corresponding ΔK.

Author Contributions

Conceptualization, Y.L. and Q.Z.; methodology, B.C. and Q.Z.; software, B.C. and Q.Z.; validation, Q.Z., B.C. and D.J.; formal analysis, F.Z.; investigation, B.C.; resources, Q.Z.; data curation, Q.Z.; writing—original draft preparation, Q.Z.; writing—review and editing, Q.Z.; project administration, Y.L.; funding acquisition, Y.L. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by Special Support Funds of Zhejiang for Scientific Research Institutes (2021F1065-12, 2024F1065-5) and Key Scientific and Technological Grant of Zhejiang for Breeding New Agricultural Varieties (2021C02070-7).

Data Availability Statement

Data are contained within the article.

Acknowledgments

We are grateful to Xingchen Lu for the help with the laboratory work.

Conflicts of Interest

The authors declare no conflicts of interest.

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Plants 13 01767 g001
Figure 1. Results of principal coordinate analysis (PCoA).
Figure 1. Results of principal coordinate analysis (PCoA).
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Figure 2. Phylogenetic clustering based on the neighbor-joining method (A) and Bayesian clustering at K = 4 (B).
Figure 2. Phylogenetic clustering based on the neighbor-joining method (A) and Bayesian clustering at K = 4 (B).
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Plants 13 01767 g003
Figure 3. Fingerprints of the 21 cultivars based on 2 SSR loci (SSR08 and SSR12).
Figure 3. Fingerprints of the 21 cultivars based on 2 SSR loci (SSR08 and SSR12).
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Figure 4. Differences in leaves among the 21 cultivars.
Figure 4. Differences in leaves among the 21 cultivars.
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Table 1. Results of the genetic analysis of the 16 SSR loci.
Table 1. Results of the genetic analysis of the 16 SSR loci.
LocusSample SizeNaNeHoHeIPIC
SSR0121116.780.000.872.150.84
SSR0221128.020.000.902.280.86
SSR032173.970.000.771.640.72
SSR0421149.380.000.922.450.88
SSR0521107.000.000.882.110.84
SSR0621105.310.000.831.950.79
SSR0721104.410.240.791.810.75
SSR08211816.330.000.962.850.94
SSR0921116.580.000.872.140.83
SSR1021105.880.000.852.020.81
SSR112195.580.000.841.950.80
SSR12211910.260.520.922.640.90
SSR1321107.000.000.882.140.84
SSR142183.900.240.761.690.72
SSR1521106.630.140.872.080.83
SSR1621105.880.000.852.020.81
Mean2111.197.060.070.862.120.82
Na: number of alleles; Ne: effective number of alleles; Ho: observed heterozygosity; He: expected heterozygosity; I: Shannon’s information index; PIC: polymorphism information content.
Table 2. Genetic diversity of the cultivars from different regions.
Table 2. Genetic diversity of the cultivars from different regions.
RegionsSample SizeNaHeIAR
Anhui74.81 0.78 1.44 2.92
Hubei21.75 0.50 0.52 1.75
Jiangsu86.13 0.84 1.68 3.15
Zhejiang43.56 0.73 1.15 2.72
Mean 4.06 0.71 1.20 2.63
AR: allelic richness.
Table 3. AMOVA of 21 cultivars from 4 regions.
Table 3. AMOVA of 21 cultivars from 4 regions.
Source of VarianceVariance ComponentPercentage of Totalp Value
Among regions0.274%<0.01
Among cultivars6.2788%
Within cultivars0.578%
Total7.11100%
Table 4. The unique alleles of the 21 cultivars at different loci.
Table 4. The unique alleles of the 21 cultivars at different loci.
CultivarsUnique Allele Lengths in bp (Locus)Number of Unique Alleles
Zilinglong413 (SSR01); 251 (SSR08); 192 (SSR09); 136 (SSR12); 157 (SSR13)5
Huangjinyi204 (SSR06); 215 (SSR15)2
Hongfeicui134 (SSR05); 232 (SSR08); 190 (SSR11)3
Zimanao156 (SSR12)1
Hongzijiaren238 (SSR07); 169 (SSR08)2
Zhaoxia391 (SSR01); 192 (SSR04); 163 (SSR05); 227 (SSR08); 196 (SSR09); 164 (SSR12); 200 (SSR15)7
Huangjinjia411 (SSR01); 225 (SSR06); 202 (SSR15)3
Huihuang286 (SSR02); 210 (SSR04); 262 (SSR06); 236 (SSR08); 166 (SSR10); 151 (SSR12)6
Canlan396 (SSR01); 298 (SSR02); 202 (SSR04); 170 (SSR05); 249 (SSR06); 230 (SSR08); 208 (SSR15)7
Haibinmenghuan119 (SSR03); 126 (SSR05); 159 (SSR10); 141 (SSR12); 162 (SSR16)5
Haibinfeihong409 (SSR01); 184 (SSR11); 193 (SSR15)3
Qiuyan 01402 (SSR01); 335 (SSR02); 185 (SSR04); 230 (SSR06); 188 (SRR07); 238 (SSR08); 205 (SSR09); 167 (SSR12)8
Haibinzijing280 (SSR02); 173 (SSR08); 157 (SSR10)3
Feiyunzhaoshui195 (SSR04); 241 (SSR06); 218 (SSR08); 194 (SSR09); 153 (SSR10); 167 (SSR11); 160 (SSR12); 170 (SSR13); 178 (SSR14); 177 (SSR16)10
Qiuhuang 01275 (SSR02); 228 (SSR06); 190 (SSR07); 163 (SSR08); 145 (SSR10); 187 (SSR11); 147 (SSR12); 150 (SSR16)8
Haibinwanxia290 (SSR02); 115 (SSR03); 187 (SSR04); 244 (SSR08); 208 (SSR09); 169 (SSR12); 164 (SSR16)7
Ziyan183 (SSR04); 173 (SSR12); 206 (SSR15)3
Puhongjiu278 (SSR02); 189 (SSR04); 192 (SSR07); 200 (SSR08); 184 (SSR09); 175 (SSR12); 159 (SSR14); 166 (SSR16)8
Pudazi195 (SSR07); 171 (SSR08); 179 (SSR12); 154 (SSR16)4
Xuanliheshan241 (SSR07); 167 (SSR08)2
Zhengyan393 (SSR01); 288 (SSR02); 212 (SSR04); 161 (SSR05); 234 (SSR08); 188 (SSR09); 159 (SSR10); 160 (SSR13)8
Total 105
Table 5. The origins, regions, and codes of the 21 cultivars.
Table 5. The origins, regions, and codes of the 21 cultivars.
CultivarsOriginsRegionsCodes of CultivarsAutumn Leaf Color
ZilinglongChuzhou, AnhuiAnhui20180091Purple
HuangjinyiChuzhou, Anhui20180391Yellow
HongfeicuiGuangde, Anhui20150168Red
ZimanaoGuangde, Anhui20170053Purple
HongzijiarenQianxian, Anhui20190340Red
ZhaoxiaQianxian, Anhui20220211Orange
HuangjinjiaXuancheng, Anhui20150167Yellow
HuihuangDawu, HubeiHubei20220215Yellow
CanlanDawu, Hubei20220214Yellow
HaibinmenghuanDongtai, JiangsuJiangsu20180072Red
HaibinfeihongLianyungang, Jiangsu20180075Purple
Qiuyan 01Nanjing, Jiangsu20160107Red
HaibinzijingNanjing, Jiangsu20180073Purple
FeiyunzhaoshuiNanjing, Jiangsu20200119Purple
Qiuhuang 01Xinyi, Jiangsu20160109Yellow
HaibinwanxiaXuzhou, Jiangsu20180074Purple
ZiyanZhenjiang, Jiangsu20160108Red
PuhongjiuPujiang, ZhejiangZhejiangZhe R-SV-SS-006-2018Red
PudaziPujiang, Zhejiang20180397Purple
XuanliheshanPujiang, Zhejiang20190339Purple
ZhengyanSuichang, Zhejiang20220212Red
Table 6. Information on the 16 polymorphic SSR markers.
Table 6. Information on the 16 polymorphic SSR markers.
LocusRepeat MotifPrimer Sequence (5′~3′)Fragment Size (bp)Tm
(°C)		Source
SSR01(AG)10AAACAAGTGAAGTGCCCAT39251[1]
TTAGCCCAGCCCATTATTA
SSR02(AAG)12GGTTTCTTTTGCTCTCTTC27749
CCGGTTACTGCATTTCATA
SSR03(CA)11CCAACAAGTTAGCATCACCT11558[25]
CAACAGAAGTTCCTCAATGT
SSR04(CT)15CTCCAGCAGCTCTTCATCT15258
CGAACCAAGAATTAGGAAAAC
SSR05(AAG)10GCCTTAAAGACATGGGATTC12658
CGATCCATTCTCTCTTGACA
SSR06(CTT)6CTGATGGCAGTTCTTTGAGAT20358
GCCTGTTGTGGAATAGTGG
SSR07(AG)10AACCCGTAAAGGGCTTGC19255[27]
CTGGTTCTCCTGGTTATCTATGC
SSR08(ATT)10AAGGAATGGAGCGAAACGG16355
CCAATTGCGGCCATACTCG
SSR09(CTT)10TCCGATCCAGTCCGTGTTG18455
GTGCGCGTGAGAGTGAATG
SSR10(CTT)9TCTCTCCTTCGCTCAACGG14555
TCCGGGATCGGTGGAATTG
SSR11(CT)9GTTTGTGAAGAGGGGTGAGC17955
AGTTGCTGAAATCCATACCATACC
SSR12(ATT)9TGAACCTCGAACAAAAGTCAG12255
GTCAT(C/A)ATAACTTCGCGGG
SSR13(TAA)7GTCAGCAGGGGAGAGCAAC13855
AATGGACAAAATGGCGCAC
SSR14(AG)16AAGGAACCTGTTTGCTGGG15155
AAGTTCCGTTTCCACACGC
SSR15(CT)9GCC(TG)6GTCAGTCGTCACCATCATCAG20255
CTACGACGACGCAACCAAC
SSR16(ATT)11TCTTCGGGGAAACCGATCC15155
TGCTTTCAAAATGACACGGTTG
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MDPI and ACS Style

Zhou, Q.; Chen, B.; Jiang, D.; Zhuge, F.; Li, Y. Genetic Analysis and Construction of a Fingerprint for Licensed Triadica sebifera Cultivars Using SSR Markers. Plants 2024, 13, 1767. https://doi.org/10.3390/plants13131767

AMA Style

Zhou Q, Chen B, Jiang D, Zhuge F, Li Y. Genetic Analysis and Construction of a Fingerprint for Licensed Triadica sebifera Cultivars Using SSR Markers. Plants. 2024; 13(13):1767. https://doi.org/10.3390/plants13131767

Chicago/Turabian Style

Zhou, Qi, Baiqiang Chen, Dongyue Jiang, Fei Zhuge, and Yingang Li. 2024. "Genetic Analysis and Construction of a Fingerprint for Licensed Triadica sebifera Cultivars Using SSR Markers" Plants 13, no. 13: 1767. https://doi.org/10.3390/plants13131767

APA Style

Zhou, Q., Chen, B., Jiang, D., Zhuge, F., & Li, Y. (2024). Genetic Analysis and Construction of a Fingerprint for Licensed Triadica sebifera Cultivars Using SSR Markers. Plants, 13(13), 1767. https://doi.org/10.3390/plants13131767

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