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Article

The Diversity of Melia azedarach L. from China Based on Transcriptome-Developed SSR Marker

by
Jinfeng Cai
,
Xiaoming Yang
,
Wanwen Yu
,
Peng Xiang
,
Shuqing Zhang
and
Guibin Wang
*
Co-Innovation Center for Sustainable Forestry in Southern China, College of Forestry, Nanjing Forestry University, Nanjing 210037, China
*
Author to whom correspondence should be addressed.
Forests 2022, 13(7), 1011; https://0-doi-org.brum.beds.ac.uk/10.3390/f13071011
Submission received: 24 May 2022 / Revised: 22 June 2022 / Accepted: 25 June 2022 / Published: 27 June 2022
(This article belongs to the Section Genetics and Molecular Biology)
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Versions Notes

Abstract

:
Melia azedarach L. is a native tree species that can be used in a comprehensive way and is widely distributed in all provinces south of the Yellow River in China. Genetic diversity analysis of different M. azedarach germplasm sources is an important basic work for the selection, evaluation, and genetic improvement of M. azedarach germplasm resources. In this study, 100 pairs of SSR primers were designed and synthesized based on M. azedarach transcriptome data, and 16 pairs of reliable SSR primers were finally selected. The developed primers were used to analyze the genetic diversity of M. azedarach from 15 sources in 10 provinces in East, Central, and South China. The results showed that the frequency of the M. azedarach transcriptome SSR loci was high, and the distribution density was high. There were 15 sources of M. azedarach genetic diversity at a moderate level, and genetic variation was mainly present within the sources. The present study further enriches the existing SSR marker database of the M. azedarach family and can provide a reference for genetic diversity analysis and molecularly assisted breeding of M. azedarach plants at the genomic level.

1. Introduction

Melia azedarach L. is a native tree species that can be used comprehensively and is widely distributed in all provinces south of the Yellow River in China. Its roots, bark, flowers, and fruits can be used as medicine, and it is the source of one of the highly effective and low-toxic broad-spectrum plant-derived pesticides with the reputation of being a ‘natural pesticide’ [1,2]. M. azedarach has a wide natural distribution area, with the geographical and climatic factors of the distribution area being quite different. In addition, M. azedarach has been in a wild and unimproved state for a long time, and the ecological environment and natural selection have had a great impact on it, forming a variety of geographical ecological types with rich genetic basis [3]. As a multifunctional and synthesizable tree species, the investigation of the genetic diversity of M. azedarach germplasm resources has become an urgent need.
The genetic diversity and population genetic structure of M. azedarach have been studied earlier using molecular markers such as RAPD, AFLP, ISSR, and SRAP [3,4,5,6]. However, compared to other species, research on the molecular biology of M. azedarach has been relatively slow, and in particular, data on the M. azedarach genome and transcriptome are relatively scarce. In addition, the development of molecular markers for M. azedarach is lagging behind, and no SSR primers have been reported specifically for M. azedarach itself. Therefore, the lack of suitable molecular markers for the molecular evaluation of genetic diversity of M. azedarach germplasm has affected the genetic improvement of M. azedarach to a certain extent. The lack of molecular markers has severely restricted the development and utilization of germplasm resources of M. azedarach. The development and utilization of molecular markers is essential for the exploration of genetic diversity in M. azedarach.
With the rapid development of high-throughput sequencing technology, there are obvious advantages of using expressed sequences from transcriptome sequencing to develop molecular markers [7,8]. Transcriptome sequencing has high sequencing sensitivity and does not require specific probes, especially for species for which reference genomic information is not yet available, and can quickly and efficiently present a wide range of transcriptome information with high accuracy [9,10]. EST-SSR markers developed from transcriptome data have the characteristics of co-dominant inheritance, high polymorphism, good reproducibility, clear bands, convenient statistics, and cost saving. Moreover, since EST-SSRs are derived from expressed gene regions, they can directly reflect the diversity of related genes and are, therefore, widely used in plant genetic breeding and germplasm resource conservation and development [11,12,13,14].
Based on this, the goals of this research were as follows: (1) to develop reliable SSR primers based on the transcriptome data; (2) to combine these primers with characterization to investigate the genetic diversity of M. azedarach in 15 origins in parts of East, Central, and South China; (3) to verify the reliability of developing SSR primers from transcriptome data; and (4) to enrich the existing SSR marker database resources. This study will provide a reference for genetic diversity analysis and molecular assisted breeding of M. azedarach at the genome level.

2. Materials and Methods

2.1. Plant Materials

M. azedarach for transcriptome sequencing was obtained from a four-year old in the tree garden of Nanjing Forestry University. The apical young leaves were collected, snap frozen in liquid nitrogen, and then stored at −80 °C and used for subsequent transcriptome sequencing.
A total of 15 natural seeds of M. azedarach from 10 provinces, including Jiangsu, Shandong, Henan, Zhejiang, Anhui, Jiangxi, Hubei, Hunan, Guangdong, and Guangxi, were selected for analysis of their genetic diversity (Figure 1). Three to sixteen single plants were collected from each seed source; details of the sampling sites can be found in Table S1. The single plants selected for sampling were required to be robust, free of obvious pests and diseases, and spaced more than 200 m apart. The young leaves of the compound leaf tips were collected, dried rapidly on silica gel, transported to the laboratory, and stored in an ultra-low temperature refrigerator at −80 °C.

2.2. Transcriptome Sequencing and Annotation

Total RNA of M. azedarach was extracted using the TRIzol reagent (Invitrogen Scientific, Inc., Carlsbad, CA, USA) in accordance with the manufacturer’s protocol. High-throughput sequencing was performed using the Illumina HiSeq-2500 platform. The number and length of the obtained raw sequences were counted, and reads with RNA-seq junctions, low quality (more than 50% of the reads with base quality values less than 5), and those containing more than 10% of unknown base N were excluded. The splicing assembly was then performed using Trinity software [15]. Using 10 × 10−5 as the threshold, the assembled libraries were compared with the public database for BLASTX to obtain functional annotation information. The Illumina raw sequencing data were submitted to the National Center for Biotechnology Information (NCBI) Short Reads Archive (SRA) database under accession number SRS7762065.

2.3. SSR Loci Identification and Primer Design

MISA [16] was used to detect simple repeats in transcriptome sequences, search to identify SSR loci, and count the density distribution of different SSR types in the transcriptome. Parameters were set to at least ten repeats or more for single nucleotides, at least six repeats or more for dinucleotides, and at least five repeats for tri-, tetra-, penta-, and hexanucleotides.
Batch design of the screened SSRs was performed using Primer 3 software [17]. One hundred pairs of SSR primers with expected PCR amplification products of 100–400 bp in length were randomly selected and synthesized by Nanjing Prime Tech Biotechnology Co. (Nanjing, China).

2.4. SSR Primer Screening and PCR Amplification

DNA of one single plant from each of the six seed sources, Nanjing, Jiangsu Province (NJ); Dongying, Shandong Province (DY); Shantou, Guangdong Province (ST); Nanning, Guangxi Province (NN); Jingzhou, Hubei Province (JZ); and Yueqing, Zhejiang Province (YQ), was selected as the template. PCR amplification was performed on 100 pairs of primers, and initial screening and polymorphism screening were performed by 2% agarose gel electrophoresis and polyacrylamide gel electrophoresis (PAGE), respectively, and primers with clear band amplification and obvious band differences were selected as specific primers. The selected primers were used for genetic diversity analysis of all samples of M. azedarach.

2.5. Genetic Diversity Analysis of M. azedarach

A matrix was constructed for the original data, and GenAlEx 6.5 software [18] was used to calculate the number of observed alleles (Na), the number of effective alleles (Ne), the observed heterozygosity (Ho), the expected heterozygosity (He), Shannon information Index (I), coefficient of inbreeding between populations (Fit), coefficient of inbreeding within populations (Fis), and coefficient of differentiation between populations (Fst). Polymorphism information content (PIC) was calculated using Popgene 1.32 software. Molecular analysis of variance (AMOVA) was performed using Arlequin 3.5 software [19]. According to the genetic distance, the NTSYSpc software was used for cluster analysis by unweighted paired arithmetic mean method (UPGMA), and the Mantel test was performed on the geographic distance and genetic distance.

3. Results

3.1. Transcriptome Sequencing and Annotation

A total of 56,373,816 raw reads were obtained from transcriptome sequencing. After data quality control, a total of 55,805,916 clean reads were obtained, with a data volume of 8.6 G. Q30 high-quality sequences accounted for 94.89% of the total, and Q20 sequences accounted for 98.06%. The GC content was 44.42%, and the base error rate was 0.01%, indicating that the transcriptome data obtained were of relatively high quality and could satisfy the subsequent bioinformatics analysis. A total of 20,077 unigenes were obtained by de novo assembly using Trinity software. Unigenes had a minimum splice length of 201 bp, a maximum splice length of 9487 bp, an average length of 1431.82 bp, and an N50 length of 1955 bp (Figure 2A).
The annotation analysis of the transcriptome data against the six major databases showed that 15,936 (79.37%) unigenes were successfully annotated in at least one database (Figure 2B). Among them, more sequences were annotated in the NCBI non-redundant (NR) database, with 15,861 successfully annotated sequences accounting for 79.00% of the total unigenes, and the Clusters of Orthologous Groups (COG) database had the least number of successfully annotated sequences, with only 6133 sequences accounting for 30.55%.

3.2. Gene Ontology (GO) Enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) Pathway Analysis of Unigenes

To further understand the distribution of gene functions, the GO annotation statistics were performed on the assembled unigenes. A total of 11,736 Unigenes were annotated into 47 functional gene regions of 3 functional modules: Biological process (BP), Molecular function (MF), and Cellular component (CC) (Figure 3). The sequences with the highest percentage in the BP class region were genes involved in cellular processes (3952), followed by genes of metabolic processes (3820), while genes concerning cell death, growth and biological clock were almost unexpressed. In the CC region, cells (4168) and cell parts (4004) were the most expressed, while cellular coplasms, cell junctions, cell-like nuclei, and receptors were almost unexpressed. Among the MF regions, the most represented were genes related to catalytic activity (5443) and binding capacity (5696).
Analysis of the major KEGG metabolic pathways showed that 6886 and approximately 34.3% of unigenes were grouped into 20 biological metabolic pathways. The top three categories were ‘Translation’, ‘Carbohydrate metabolism’, and ‘Folding, sorting and degradation’ (Figure 4).

3.3. Transcriptome SSR Primer Design and Screening

The 20,077 unigenes sequences in the M. azedarach transcriptome were searched using MISA software, and 4116 unigenes sequences were found to contain 5469 SSR loci. The frequency of occurrence of SSR was 20.50%, the frequency of SSR distribution was 27.24%, and the average distribution distance was 8.74 kb (Table 1).
Primers were designed for 5469 SSR loci using Primer 3.0 software; a total of 2229 primer pairs were designed, and 100 primer pairs were randomly selected for synthesis. The PCR amplification of the above primers was carried out using six M. azedarach DNA templates, and these primers were primed using a 1% agarose gel, resulting in 72 primer pairs showing a single band with clear bands and an amplification efficiency of 72%. These 72 primer pairs were screened for polymorphism using PAGE gels, and 16 of them showed polymorphism (Table S2). The primer polymorphism ratio was 22.22%, which could be used for genetic diversity analysis of all samples of M. azedarach.
Sixteen pairs of SSR primers were used to PCR amplify 106 M. azedarach individuals to detect polymorphisms (Table 2). In total, 56 allelic (Na) fragments were detected at 16 SSR loci, with an average of 3.5 alleles per locus and a variation range of 2 to 5 alleles. The number of effective alleles (Ne) varied from 1.195 to 4.42, with the least and most frequent being primer KL-P4-14 and primer KL-P3-8, respectively. The Shannon’s diversity index (I) varied from 0.301 to 1.535 with an average of 0.861, indicating a high abundance of the 16 SSR marker pairs. The linkage disequilibrium test showed that there was no linkage disequilibrium among the 16 SSR loci, indicating that each locus was independent of each other and could be used for subsequent genetic diversity detection analysis.

3.4. Analysis of Genetic Diversity among M. azedarach Germplasms

Genetic diversity analysis of M. azedarach in the main production areas of China was performed using the 16 pairs of SSR primers described above. The number of alleles (Na) and effective alleles (Ne) ranged from 1.938 to 3.063 and 1.546 to 2.204, with mean values of 2.459 and 1.944, respectively. The highest number of Na and effective alleles (Ne) were both in the Pizhou, Jiangsu Province, and the lowest were both in the Xinyang, Henan Province (Table 3).
The Shannon diversity index (I) of M. azedarach varied from 0.464 to 0.822 with a mean value of 0.687, the largest and smallest being in Pizhou and Xinyang, respectively. The I was higher than the mean value in Pizhou, Nanchang, Lin’an, Nanjing, Taixing, Jingzhou, and Dongming, while the rest were lower than the mean value. The rest of the origins were below the average value.
Combining the Ne, I and He, M. azedarach germplasm variation was abundant in the Jiangxi Nanchang, Zhejiang Lin’an, and Jiangsu Pizhou provenances, while variation was relatively low in the Henan Xinyang and Anhui Hefei provenances.

3.5. Genetic Differentiation and Gene Flow of M. azedarach Germplasm

We analyzed the distribution of the amount of variation on each SSR locus between and within populations based on F-statistical methods to measure the magnitude of genetic differentiation between populations (Table 4). The mean Fst value for all loci was 0.096, indicating a moderate level of differentiation. Gene flow (Nm) varied widely among loci with a mean value of 3.155, indicating that there is more gene flow between M. azedarach seed sources, which can effectively reduce population genetic differentiation due to genetic drift. Molecular of variance analysis (AMOVA) on the information of 16 loci showed that 9% of the genetic variation was from interspecific and 91% from intraspecific, indicating a high level of genetic variation in M. azedarach within the species (Table S3).

3.6. Genetic Distance and Clustering Analysis among the Seeds

To further analyze the degree of genetic differentiation of M. azedarach, Nei’s genetic distance was calculated among the seed sources (Table S4). The range of variation in genetic distance among M. azedarach seed sources ranged from 0.030 to 0.269, indicating that different degrees of genetic variation existed among M. azedarach seed sources, but the overall degree of variation was small. The smallest genetic distance was between NJ, Jiangsu, PZ and DM, Shandong, and the largest genetic distance was between the sources in NC, Jiangxi and ST, Guangdong.
The Mantel test was performed for geographic and genetic distances using NTSYSpc software. The results showed some correlation between the two, but the correlation was not significant (r = 0.687, p = 0.999), indicating that the genetic differentiation between M. azedarach seeds was not related to geographical distance, but mainly to their own genetics (Figure 5A).
By performing UPGMA clustering of genetic distances, the results showed that, when a threshold value of 0.07 was taken, the 15 M. azedarach seed sources were grouped into three major classes, with the Guangdong Shantou and Guangxi Nanning sources clustered into one class, the Jiangxi Jinggangshan and Jiangxi Nanchang sources clustered into one class, and the remaining sources clustered into one class (Figure 5B).

4. Discussion

4.1. Development of a Transcriptome-Based SSR Marker for M. azedarach

Analysis of SSR loci revealed that the frequency of occurrence of M. azedarach transcriptome SSRs was 20.50%, with an average distribution distance of 8.74 kb in this study. The average distance was lower than that of Eucommia ulmoides with 26.13 kb [20] and Pinus koraiensis with 17.38 kb [21], indicating that M. azedarach has a higher SSR distribution density and is more abundant. The SSR distribution distance was similar to that of Zanthoxylum bungeanum Maxim with 7.2 kb [22] and Glyptostrobus pensilis with 7.59 kb [23]. However, it was higher than that of Phoebe zhennan with 3.37 kb [24] and Camellia sinensis with 3.68 kb [25]. Plant species, genome size, sequencing site, and search conditions may contribute to this difference [26].
After comparing the annotations with the six major public databases, the unannotated unigenes accounted for 20.63%, probably due to the short query length or the lack of characteristic protein structural domains. This could also be due to the fact that these sequences are non-coding RNA sequences, limited by the fact that too little information on the transcriptome data is publicly available, or due to the existence of some novel genes [27]. Relevant research showed that SSR lengths up to 20 bp in length are highly polymorphic and are ideal marker sites [28]. The average length of M. azedarach SSR sequences developed in this study was 19.41 bp, of which the number of SSRs between 12 and 20 bp was 2748 (50.25%), indicating that most of the M. azedarach transcriptome SSRs are potentially highly polymorphic.

4.2. Genetic Diversity of M. azedarach in the Main Source Locations in China

The magnitude of desired heterozygosity is proportional to gene richness and is an important evaluation parameter reflecting the genetic diversity of a species population. The average expected heterozygosity was 0.68 for perennials, 0.65 for heterozygotes and 0.61 for wind-borne plants [29]. The genetic diversity of the 15 seed sources of M. azedarach in this study was at a moderate level, slightly lower than in previous related studies [30]. The different sources of SSR used, the number of primers, and the small sample size collected from some of the seed sources in this study may have contributed to these differences.
Genetic variation in natural populations is the result of a combination of gene flow and selection. Spatial segregation of populations of the same species, mutations, selection differences due to environmental factors, genetic drift, and blockage of gene flow may lead to spatial heterogeneity of population genetic structure, thus promoting population differentiation [31]. The mean value of the genetic differentiation coefficient (Fst) for the degree of genetic differentiation in the M. azedarach population in this study was 0.096, showing a moderate level of genetic differentiation among M. azedarach provenances according to the theory of Wright et al. [32]. AMOVA showed that genetic variation was mainly present within the provenance, accounting for 91%, and only 9% of the genetic variation was from between provenances, indicating a high level of genetic variation among M. azedarach individuals within the provenance. Therefore, when selecting and breeding good germplasm resources of M. azedarach, we should not only pay attention to the selection of the origin, but also not neglect the selection of the family line and the selection of good single plants within the origin. Only by combining the two organically can we select germplasm resources that are more adaptable to the environment and have better survival ability. Moreover, further increasing the number of individuals in the sampled populations and keeping the numbers constant would make the results more reliable. We will take this into account in future research.

4.3. Gene Flow in M. azedarach from Different Seed Locations

The continuity of species’ range affects their differentiation among populations, whereas heterosis maintains high genetic variation within populations [33]. M. azedarach is widely distributed continuously, has bisexual flowers, is predominantly heterozygous, relies on wind- and insect-borne pollination, and has frequent intra-group gene exchange. Together with the spread of feeding on M. azedarach fruits in winter and spring by birds such as grey magpies, guillemots, bulbuls, and grey starlings, this to some extent enhances the genetic exchange between populations, thus weakening the genetic differentiation between populations. The results of this study also indicated higher gene flow between origins (Nm = 3.155) and reduced genetic differentiation between origins. There was some correlation between genetic and geographical distances between M. azedarach provenances, but the correlation was not significant, indicating that genetic differentiation between M. azedarach provenances is not much related to geographical distance, but mainly to its own genetics.
Exploring the genetic diversity of species by developing SSR has been implemented in many species such as Etlingera elatior [34], Lablab purpureus [35], and Ananas comosus [36]. This method is stable and reliable, and the results of this study confirmed this. The development of SSR primers from transcriptome data to explore the genetic diversity of M. azedarach is particularly important in the absence of a reference genome.

5. Conclusions

In conclusion, genetic diversity analysis of 15 M. azedarach seed sources using 16 pairs of polymorphic primers screened showed that M. azedarach genetic diversity was at a moderate level. The frequency of the M. azedarach transcriptome SSR loci was high, and the distribution density was large, indicating that the development of SSR primers for M. azedarach using transcriptome data is reliable. There was a high level of genetic variation among individuals within the M. azedarach germplasm, and genetic variation was mainly present within the germplasm. Only by combining the selection of germplasm and the selection and breeding of good single plants within the germplasm can we select germplasm resources that are more adaptable to the environment and more viable. These results provide important reference information for molecular assisted breeding and lay a foundation for genetic diversity of M. azedarach.

Supplementary Materials

The following supporting information can be downloaded at: https://0-www-mdpi-com.brum.beds.ac.uk/article/10.3390/f13071011/s1, Table S1: Sample number and sampling information of Melia azedarach; Table S2: 16 couples polymorphic SSR primers information of M. azedarach L.; Table S3: Molecular variance analysis (AMOVA) result of 15 Melia azedarach L. provenances; Table S4: Genetic distance and geographic distance of 15 Melia azedarach L. provenances.

Author Contributions

Data curation, J.C.; Formal analysis, J.C., X.Y. and P.X.; Funding acquisition, G.W.; Investigation, J.C., W.Y. and S.Z.; Writing—original draft, J.C.; Writing—review & editing, G.W. All authors have read and agreed to the published version of the manuscript.

Funding

This work was kindly supported by the research projects of the Jiangsu Agricultural Science and Technology Innovation Fund (CX (16) 1005).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

All transcriptomic sequence data used in this research have been deposited in the National Center for Biotechnology Information (NCBI) Short Reads Archive (SRA) database under accession number SRS7762065.

Acknowledgments

We thank the Co-Innovation Center for Sustainable Forestry in Southern China for allowing us to use their research facilities.

Conflicts of Interest

The authors declare no conflict of interest.

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Forests 13 01011 g001 550
Figure 1. Distribution map of M. azedarach seed collection sites. The blue dots indicated the locations sampled. DY: Dongying, Shandong Province; DM: Dongming, Shandong Province; PZ: Pizhou, Jiangsu Province; TX: Taixing, Jiangsu Province; NJ: Nanjing, Jiangsu Province; HF: Hefei, Anhui Province; XY: Xinyang, Henan Province; NC: Nanchang, Jiangxi Province; JGS: Jinggangshan, Jiangxi Province; CS: Changsha, Hunan Province; JZ: Jingzhou, Hubei Province; LA: Linan, Zhejiang Province; YQ: Yueqing, Zhejiang Province; ST: Shantou, Guangdong Province; NN: Nanning, Guangxi Province.
Figure 1. Distribution map of M. azedarach seed collection sites. The blue dots indicated the locations sampled. DY: Dongying, Shandong Province; DM: Dongming, Shandong Province; PZ: Pizhou, Jiangsu Province; TX: Taixing, Jiangsu Province; NJ: Nanjing, Jiangsu Province; HF: Hefei, Anhui Province; XY: Xinyang, Henan Province; NC: Nanchang, Jiangxi Province; JGS: Jinggangshan, Jiangxi Province; CS: Changsha, Hunan Province; JZ: Jingzhou, Hubei Province; LA: Linan, Zhejiang Province; YQ: Yueqing, Zhejiang Province; ST: Shantou, Guangdong Province; NN: Nanning, Guangxi Province.
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Forests 13 01011 g002 550
Figure 2. Length distribution (A) and annotation statistics (B) of unigenes. KEGG: Kyoto Encyclopedia of Genes and Genomes; GO: Gene Ontology; COG: Clusters of Orthologous Groups; NR: NCBI non-redundant.
Figure 2. Length distribution (A) and annotation statistics (B) of unigenes. KEGG: Kyoto Encyclopedia of Genes and Genomes; GO: Gene Ontology; COG: Clusters of Orthologous Groups; NR: NCBI non-redundant.
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Forests 13 01011 g003 550
Figure 3. Functional classification of GO enrichment for assemble unigenes of M. azedarach.
Figure 3. Functional classification of GO enrichment for assemble unigenes of M. azedarach.
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Forests 13 01011 g004 550
Figure 4. Function classification of unigenes of M. azedarach in KEGG category.
Figure 4. Function classification of unigenes of M. azedarach in KEGG category.
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Figure 5. Mantel test between genetic distance and geographic distance of M. azedarach provenances (A) and UPGMA dendrogram of M. azedarach provenances (B). DY: Dongying, Shandong Province; DM: Dongming, Shandong Province; PZ: Pizhou, Jiangsu Province; TX: Taixing, Jiangsu Province; NJ: Nanjing, Jiangsu Province; HF: Hefei, Anhui Province; XY: Xinyang, Henan Province; NC: Nanchang, Jiangxi Province; JGS: Jinggangshan, Jiangxi Province; CS: Changsha, Hunan Province; JZ: Jingzhou, Hubei Province; LA: Linan, Zhejiang Province; YQ: Yueqing, Zhejiang Province; ST: Shantou, Guangdong Province; NN: Nanning, Guangxi Province.
Figure 5. Mantel test between genetic distance and geographic distance of M. azedarach provenances (A) and UPGMA dendrogram of M. azedarach provenances (B). DY: Dongying, Shandong Province; DM: Dongming, Shandong Province; PZ: Pizhou, Jiangsu Province; TX: Taixing, Jiangsu Province; NJ: Nanjing, Jiangsu Province; HF: Hefei, Anhui Province; XY: Xinyang, Henan Province; NC: Nanchang, Jiangxi Province; JGS: Jinggangshan, Jiangxi Province; CS: Changsha, Hunan Province; JZ: Jingzhou, Hubei Province; LA: Linan, Zhejiang Province; YQ: Yueqing, Zhejiang Province; ST: Shantou, Guangdong Province; NN: Nanning, Guangxi Province.
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Table
Table 1. SSR search result in M. azedarach transcriptome.
Table 1. SSR search result in M. azedarach transcriptome.
ItemNumber
Number of searching sequences20,077
Length of searching sequences/bp47,805,499
Number of SSR5469
Number of sequences with SSRs4116
Number of sequences with more than one SSRs763
Occurrence frequency of SSR (%)20.50
Occurrence frequency of unigenes (%)27.24
Average distance/kb8.74
Table
Table 2. Genetic parameters of 16 polymorphic locus.
Table 2. Genetic parameters of 16 polymorphic locus.
SSR LociObserved
Number of
Alleles (Na)		Effective Number of Alleles (Ne) Shannon’s
Information
Index (I)		Observed
Heterozygosity (Ho) 		Expected
Heterozygosity (He)		Polymorphic
Information
Content (PIC)
KL-P2-531.2910.4360.7900.2260.523
KL-P2-653.0591.2420.2840.6770.613
KL-P2-952.8661.1910.4000.6550.653
KL-P3-252.5511.0730.5470.6110.641
KL-P3-752.3981.0540.2420.5860.598
KL-P3-854.4201.5350.3790.7780.654
KL-P3-1121.6340.5760.6630.3900.385
KL-P3-1221.8400.6490.6740.4590.423
KL-P4-232.0170.7210.4740.5070.486
KL-P4-1031.7960.6590.3470.4450.478
KL-P4-1421.1950.3010.8420.1640.399
KL-P5-254.1931.4820.5160.7660.692
KL-P5-421.6200.5710.6950.3850.356
KL-P5-1521.2200.3250.8000.1810.324
KL-P1-642.6291.0690.2950.6230.458
KL-P1-632.2360.8860.1260.5560.421
Mean3.52.3100.8610.5050.5000.507
Table
Table 3. Parameters of genetic diversity of 15 M. azedarach sources.
Table 3. Parameters of genetic diversity of 15 M. azedarach sources.
Original AreaObserved Number of Alleles (Na)Effective Number of Alleles (Ne)Shannon’s Information Index (I)Observed
Heterozygosity (Ho)		Expected
Heterozygosity (He)
NJ2.8132.1150.7770.6060.464
PZ3.0632.2040.8220.4060.478
DM2.5001.9550.7150.5630.443
TX2.6882.0950.7700.4770.470
NC2.6252.1890.8190.5630.506
XY1.9381.5460.4640.2500.295
HF1.9381.7290.5020.5000.324
DY2.3131.8740.6660.5090.427
ST2.4381.8830.6590.3440.402
NN2.4381.8020.6370.3830.389
CS2.1881.8800.6510.6460.424
JZ2.5632.0180.7550.5750.471
LA2.8752.1820.8090.5570.484
YQ2.3131.8640.6400.4060.401
JGS2.1881.8190.6230.5630.400
mean2.4591.9440.6870.4900.425
Note: NJ: Nanjing, Jiangsu Province; PZ: Pizhou, Jiangsu Province; DM: Dongming, Shandong Province; TX: Taixing, Jiangsu Province; NC: Nanchang, Jiangxi Province; XY: Xinyang, Henan Province; HF: Hefei, Anhui Province; DY: Dongying, Shandong Province; ST: Shantou, Guangdong Province; NN: Nanning, Guangxi Province; CS: Changsha, Hunan Province; JZ: Jingzhou, Hubei Province; LA: Lin’an, Zhejiang Province; YQ: Yueqing, Zhejiang Province; JGS: Jinggangshan, Jiangxi Province.
Table
Table 4. F-statistics of genetic differentiation and gene flow of 16 SSR locus of M. azedarach.
Table 4. F-statistics of genetic differentiation and gene flow of 16 SSR locus of M. azedarach.
SSR LociInbreeding
Coefficient (Fis)		Total Population
Inbreeding Coefficient (Fit)		Gene
Differentiation
Coefficient (Fst)		Gene Flow
(Nm)
KL-P2-50.0340.0900.0663.538
KL-P2-6−0.0760.1270.1022.203
KL-P2-9−0.121−0.0570.0484.958
KL-P3-20.1190.2840.1481.441
KL-P3-7−0.108−0.0130.0942.410
KL-P3-80.2420.3190.0514.700
KL-P3-110.1090.1610.0455.293
KL-P3-12−0.0220.1230.1411.519
KL-P4-20.2140.2870.0584.048
KL-P4-10−0.0720.0600.1821.125
KL-P4-140.0530.1240.1032.184
KL-P5-20.3970.4450.0298.401
KL-P5-40.1390.2180.1102.014
KL-P5-15−0.0320.0350.0862.657
KL-P1-6−0.0190.1890.2040.977
KL-P1-6−0.182−0.1190.0773.012
Mean0.0420.1420.0963.155
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Cai, J.; Yang, X.; Yu, W.; Xiang, P.; Zhang, S.; Wang, G. The Diversity of Melia azedarach L. from China Based on Transcriptome-Developed SSR Marker. Forests 2022, 13, 1011. https://0-doi-org.brum.beds.ac.uk/10.3390/f13071011

AMA Style

Cai J, Yang X, Yu W, Xiang P, Zhang S, Wang G. The Diversity of Melia azedarach L. from China Based on Transcriptome-Developed SSR Marker. Forests. 2022; 13(7):1011. https://0-doi-org.brum.beds.ac.uk/10.3390/f13071011

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Cai, Jinfeng, Xiaoming Yang, Wanwen Yu, Peng Xiang, Shuqing Zhang, and Guibin Wang. 2022. "The Diversity of Melia azedarach L. from China Based on Transcriptome-Developed SSR Marker" Forests 13, no. 7: 1011. https://0-doi-org.brum.beds.ac.uk/10.3390/f13071011

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