Genetic Diversity and Population Structure of Bursaphelenchus xylophilus in Central China Based on SNP Markers
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.
†
These authors contributed equally to this work.
Forests 2023, 14(7), 1443; https://doi.org/10.3390/f14071443
Submission received: 30 May 2023
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Revised: 30 June 2023
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Accepted: 12 July 2023
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Published: 13 July 2023
(This article belongs to the Special Issue Development of Nuclear SNP Markers for Tracing Timber)
Abstract
:Hubei, Hunan and Henan Provinces are located in Central China, a region with extensive transport networks and trade. The pine wilt nematode (PWN), Bursaphelenchus xylophilus, the causative agent of pine wilt disease, is spread mainly through human activities. To further understand the genetic structure of PWN in Central China, we studied the genetic information of PWN populations in this region and compared the genetic relationship with strains from Guangdong and Jiangsu provinces. We found that the HB (Hubei) 15, HEN (Henan) 20, HN (Hunan) 07, HN08 and HN10 had significantly more SNPs and homozygotes than other strains from Central China, and their most frequent mutant genotypes also differed from other strains. The clustering results indicated that HB15, HEN 20, HN07, HN08 and HN10 were genetically distinct from other strains and closely related to Guangdong strains. We also observed significant genetic variation among strains in Henan province, suggesting that some of them might have different transmission sources than those from Hubei and Hunan provinces. Introgression analysis identified three possible pathways: (1) Guangdong to Henan; (2) Guangdong to Hunan; and (3) Jiangsu to Hubei. The results provide a basis for tracing the origin and spread of pine wood disease in China.
1. Introduction
The pine wood nematode (PWN; Bursaphelenchus xylophilus) is the causal agent of pine wilt disease (PWD), which poses a hazard to pine forests throughout Europe and Asia. PWD is regulated as a quarantine disease in most countries due to its ecological and economic impacts [1]. Previous studies have indicated that China is one of the countries most affected by PWD, with most provinces being suitable for PWN establishment and spread [2]. As an invasive alien species, the PWN has caused significant losses to pine forests in China. It was first detected in Nanjing, Jiangsu Province in 1982, and since then it has expanded to 731 county-level administrative regions of 19 provinces (National Forestry and Grassland Administration No. 6 of 2022). This suggests that PWNs have a wide distribution in China, and that human activities such as transportation of infested wood and infrastructure construction are the main pathways of its dissemination. However, the transmission routes of the PWN in China are poorly understood due to the difficulties and limitations of monitoring and surveillance. In recent years, many studies have focused on the early diagnosis of PWN, to provide a theoretical basis for its prevention and control [3,4,5].
Studies have shown that the critical time to control biological invasions is in the early stage [6,7], and studying the dispersal path of invasive organisms is crucial to achieving control. Several studies have also demonstrated that the inference of invasion pathways provides information about the biological invasion process, which enables us to understand the ecological characteristics of invasive populations and is helpful for control or eradication [8,9,10,11]. In order to clarify the transmission path and population differentiation of PWNs, relevant studies have used different molecular marker technologies to analyse the population genetic structure of PWNs in some geographical areas [12,13,14,15].
As early as 2007, random amplified polymorphic DNA (RAPD) was used to analyse the genetic variation of the Spanish PWN [16]. Subsequently, Valadas et al. used ISSR molecular markers to analyse the genetic differences among 43 PWN strains from five countries: China, Japan, Korea, the United States and Portugal [17]. With the development of molecular marker technology, single nucleotide polymorphisms (SNP) are considered the most promising molecular markers. They are widely used in many applications for population tracking, molecular genetics and disease diagnosis [18,19,20,21]. In recent years, studies on the population diversity of PWNs using SNPs have also been reported [13,22,23]. Figueiredo et al. used SNP labelling technology to analyse the differences in SNPs in 7 PWN strains from Portugal, China, the United States and Japan. The Portuguese strains were closer to those from China, and the genetic distance between the American and Japanese strains was relatively wide [15]. Several studies have analysed the population diversity of PWN in different regions of China using SNP labelling technology. The genetic structure of the Guangdong Province strain showed that it has high genetic diversity and multiple transmission sources [13]. Population results in East China showed that there was some correlation between each group and geographical origin [22].
Hubei, Hunan and Henan provinces are in Central China, a region with frequent economic and trade activities. Therefore, supervising infected trees poses considerable challenges. Some scholars speculated that Guangdong Province was the initial colonisation and diffusion centre of PWNs in China, and Jiangsu Province was the new diffusion centre [24]. These provinces are close to Hubei, Hunan and Henan, which were classified as epidemic areas in 2000, 2003 and 2009 respectively. Understanding the population structure of PWN by studying their genetic differences is beneficial to the analysis of genetic diversity and is also a necessary step for tracing the origin of PWN. During the 41 years of the invasion of PWN in China, cross-invasion occurred continuously. To better understand the genetic structure of PWNs in Central China, this study used whole genome resequencing and SNP molecular marker technology to analyse the genetic diversity of PWN populations in Central China and explore their genetic relationship with PWN populations in Jiangsu and Guangdong provinces. This has great significance for establishing a PWD tracing system.
2. Materials and Methods
2.1. Isolation and Purification of Nematodes
Infected trees were collected from Henan, Hubei, Hunan, Guangdong and Jiangsu Province. Nematodes were extracted from chopped trees using the Baermann funnel method. Morphological identification of PWNs was carried out according to the characteristics of PWNs [25]. After DNA extraction, molecular identification based on the sequence-characterised amplified regions (SCAR) marker was performed to ensure detection accuracy [26].
After being identified as PWN, approximately 30 individuals were selected and established in the laboratory on Botrytis cinerea growing on potato dextrose agar at 28 °C [27]. When cultured for 5–7 days, nematodes were isolated using the Baermann funnel method and were cleared with 0.05% streptomycin sulphate and sterile water for storage [28]. All the strains were stored in the PWN Strain Resource Bank of the Forest Pathology Laboratory of Nanjing Forestry University.
2.2. Genome Resequencing
The DNA of PWNs was extracted by the CTAB method [26] and stored in the DNA Resource Bank of PWN, Laboratory of Forest Pathology, Nanjing Forestry University. A Nano-drop 2000/2000C (Thermo Fisher, Waltham, MA, USA) was used to detect the DNA concentration and quality. The qualified DNA was sent to Wuhan Future Group Biological Company for high-throughput genome sequencing on an Illumina HiSeq 4000 (150 bp paired-end reads). The average sequencing depth was greater than 40×.
2.3. Identification and Filtration of Mutation Sites
The quality of the raw data was first assessed by FastQC (http://www.bioinformatics.babraham.ac.uk/projects/fastqc/, accessed on 6 May 2022). Filtered reads were aligned to the reference genome of PWN announced in 2022 [29] by BWA (http://bio-BWA.SourceForge.net/BWA.shtml, accessed on 20 May 2022). Samtools (http://samtools.sourceforge.net/samtools.shtml, accessed on 23 May 2022) and Picard were used to remove duplicates. Putative SNPs were called by Freebayes (https://github.com/ekg/freebayes, accessed on 8 June 2022) with minimum coverage (>10), and VCFtools (https://github.com/vcftools, accessed on 1 July 2022) was used for SNP site statistical analysis.
2.4. Genetic Differentiation Analysis
The SNPs with low allele frequency, high linkage disequilibrium and missing rate were filtered by the SNPRelate package (https://www.bioconductor.org/packages/release/bioc/html/SNPRelate.html, accessed on 15 July 2022) of RStudio software (https://www.rstudio.com/). A principal component analysis (PCA) diagram was drawn using the same package mentioned above. PLINK (v1.9) (https://www.cog-genomics.org/plink/, accessed on 23 July 2022) was used to extract the filtered site information to generate a new vcf file for phylogenetic tree analysis. VCF-kit (https://vcf-kit.readthedocs.io/en/latest/, accessed on 23 July 2022) and MEGA (v11.0.11) (https://www.megasoftware.net/, accessed on 23 July 2022) were used to construct phylogenetic trees using the neighbour-joining method.
Treemix software (https://bitbucket.org/nygcresearch/treemix, accessed on 2 August 2022), based on allele frequency as the basis for genetic distance calculation, was used to construct phylogenetic trees and label gene exchanges. Plink was used to calculate allele frequency for all SNP loci in advance, and the parameter “-noss-global” was used to construct a maximum likelihood tree.
3. Results
3.1. Sample Collection
After purification and culture, 50 PWN samples from five provinces in China were obtained: Hubei (HB), Henan (HEN), Hunan (HN), Guangdong (GD) and Jiangsu (JS). Table 1 shows the sample information of the 50 B. xylophilus strains.
3.2. Statistics of SNP Loci
The SNP locus information of the 30 strains from Central China showed that there are 8,333,375 SNP sites in total and the number of SNP sites varied significantly among different strains (Figure 1). HB15, HEN20, HN07, HN08 and HN10 had significantly more SNPs and homozygotes than other strains. HEN15, HEN19, HEN20, HN09, HN10 and HN13 had significantly more missing SNPs than other strains. HEN20 had the highest number of SNPs, whereas HN02 had the lowest. HN10 had the highest number of homozygous, missing and private SNPs, which were 1,033,119, 5,248,382 and 505,849, respectively. HN02 had the lowest number of homozygous SNPs, HEN06 had the lowest number of missing SNPs, and HN09 had the lowest number of private SNPs (Table 2 and Figure 1).
3.3. Statistics of SNP Genotypes
The results of SNP genotyping of strains from Central China showed that there were 12 SNP genotypes: A > C, A > G, A > T, C > A, C > G, C > T, G > A, T > A, T > C and T > G. Comparing the genotype counts among strains, significant differences were found for some genotypes. Specifically, four genotypes (A > G, C > T, G > A, and T > C) were significantly more frequent in the HB15, HEN20, HN06, HN07, HN08, and HN10 strains than in other strains. For the remaining strains, six genotypes (A > G, C > G, C > T, G > A, G > C, T > C) were significantly more frequent than the others. Moreover, the genotype counts of the HB15, HEN20, HN06, HN07, HN08 and HN10 strains were higher than those of the other strains (Figure 2).
3.4. Analysis of Genetic Differentiation
Principal component analysis (PCA) was conducted on 30 strains from Central China, which were divided into four groups (Figure 3a). Group 1 consisted of HB15, HEN20, HN07, HN08 and HN10. Group 2 consisted of HB08, HEN02, HEN04 and HN06. Group 3 contained 16 strains, including 8 from Hubei, 2 from Henan and 6 from Hunan. Group 4 included only Henan strains: HEN06, HEN09, HEN10, HEN14 and HEN15. The PCA results revealed genetic differences among PWN populations in Central China. Henan Province had the highest genetic diversity, as its strains were distributed across all four groups. In contrast, most of the Hubei strains clustered in Group 3, and the Hunan strains were either in Group 1 or Group 3.
To investigate the origin of PWN strains in Central China, PCA was performed on the strains from Central China, Jiangsu Province and Guangdong Province (Figure 3b). The PCA results indicated that the strains from Jiangsu Province were genetically similar to Group 3, whereas the strains from Guangdong Province clustered with Group 1 (Figure 3). The neighbour-joining tree of the 50 PWNs confirmed the PCA results (Figure 4). Both analyses revealed that HB15, HEN20, HN07, HN08 and HN10 were closely related to strains from Guangdong Province, whereas the others were closely related to Jiangsu strains. Moreover, there were significant differences between Henan strains and strains from other provinces, suggesting different sources of invasion. To understand the invasion routes of the PWN in Central China, introgression analysis was conducted and identified three possible pathways: (1) Guangdong to Henan; (2) Guangdong to Hunan; and (3) Jiangsu to Hubei. This implies that Guangdong Province could be a major source of PWN spread to Henan and Hunan provinces, and Jiangsu Province could be a major source of PWN spread to Hubei Province (Figure 5).
4. Discussion
Previous studies have demonstrated that environmental factors can induce founder effects and genetic drift, resulting in diminished or lost genetic diversity and population genetic differentiation among species undergoing migration and dispersal [30,31,32]. Cheng et al. analysed the genetic diversity of PWNs in different regions of China using amplified fragment length polymorphism (AFLP) markers and found that Chinese populations had slightly higher genetic diversity than American populations [33]. They found that Chinese populations were slightly higher than American populations in genetic diversity. However, Ding et al. used SNP markers to examine 181 PWN strains from 16 endemic areas in China and found that the Guangdong population had high genetic diversity and was genetically close to American strains, whereas the genetic diversity of strains in other areas tended to decrease [29]. They concluded that the invasive populations suffered from the loss of genetic diversity due to the founder effect, which was consistent with the findings of Mallez et al. [31,34]. These studies indicated that there was a specific correlation between different clusters and their geographical origin and that SNP molecular marker technology was an effective tool to study the genetic differentiation of the PWN population [15,23]. Based on the population structure analysis of PWNs in China [29], this study revealed the finer population structure in Central China for the first time using SNP molecular markers.
The incidence and distribution of PWD in China are mainly concentrated in Guangdong and Jiangsu provinces, which are in economically developed areas. Central China has a temperate and subtropical monsoon climate with an annual average temperature higher than 15 °C, which is prone to PWD [35]. Moreover, the area is adjacent to Guangdong and Jiangsu provinces and has frequent trade activities with other parts of the country through traffic lines. It is possible that PWNs were introduced into infected pine plants and their products (e.g., cable trays, packing cases) during trade activities. Therefore, PWNs in each epidemic area of Central China may have different epidemic sources, and there is a high possibility of cross-invasion. This is also consistent with our research results that there are multiple clusters in Central China.
This study applied SNP molecular marker technology to analyse the genetic differences in PWN populations in Central China. The results revealed that the number of SNP sites, homozygote number, and genotypes of HB15, HEN20, HN07, HN08 and HN10 were significantly higher than those of other strains, and the number of missing SNP sites differed significantly from those of other strains. The results showed that these strains had distinct SNP loci and genotypes, which was consistent with the clustering results. The clustering results showed that the five strains were genetically distant from other strains. Therefore, we inferred that there were different sources of transmission for the strains in Central China. Ding et al. [29] analysed the population genetic structure of strains from different regions of China, and the results showed that the strains from Henan, Hubei and Hunan were clustered into one group, among which the Hunan strains were distributed in several groups, showing rich genetic diversity. It was also confirmed that the nematode strains in Central China had different transmission sources.
Previous studies suggested that Guangdong Province was the initial colonisation and dispersal centre of PWNs in China, and Jiangsu Province was a new dispersal centre. Therefore, this study analysed the population genetic structure of the strains from Central China and those from Jiangsu and Guangdong Provinces. The PCA and phylogenetic tree demonstrated that HB15, HEN20, HN07, HN08 and HN10 were closely related to the strains from Guangdong Province, whereas other strains were closely related to those from Jiangsu Province. The introgression results also confirmed that the Henan and Hunan isolates originated from the Guangdong strains. The result may be due to the genetic difference between isolated taxa from the Henan and Hubei populations. Therefore, we hypothesised that there were three main transmission routes for the strains from Central China (Guangdong to Henan; Guangdong to Hunan; Jiangsu to Hubei) and that the HB15, HEN20, HN07, HN08 and HN10 strains invaded from Guangdong. This was consistent with the results of Ding et al. [29], indicating that the Hunan strains migrated from the Guangdong strains. This study further analysed population genetic diversity in Central China. There was evidence of multiple invasions and cross-invasions in the epidemic areas of Hubei, Henan and Hunan, which indicated that the regulation of infected wood was insufficient.
Wang et al. used SNPs to analyse the genetic differentiation of PWN populations in East China [22] and found correlations among different groups and geographical regions. However, the genetic differentiation within each group was not significant. In contrast, this study revealed that there was genetic differentiation among PWNs from Central China, and the genetic diversity of Henan strains was higher. Some Henan strains were not only genetically differentiated from those in Guangdong Province but also genetically distant from those in Jiangsu Province, which might result from genetic drift and founder effects during the invasion of PWNs. Huang et al. reported significant genetic differences among PWN populations in Guangdong Province [13]. Ding et al. suggested that the Guangdong strain had similar genetic variation to the American strain and speculated that it might originate from foreign invasion [15,29,36]. We hypothesised that the Henan strain might also be influenced by foreign invasion because it had a distinct genetic structure. However, there was a lack of many foreign strains to verify this possibility.
5. Conclusions
By analysing the genetic diversity of PWNs from Central China, we found that some strains had higher genetic similarity to those from Guangdong. In addition, the Henan strain has rich genetic diversity and genetic differences from other strains, suggesting that there may be different transmission sources. During the 41 years of PWN invasion, many different geographical strains were formed in the genetic structure of PWN populations in China. Based on the genetic differences, we investigated the genetic structure of PWN populations in different endemic areas of China, which provided a theoretical basis for exploring the reproduction route, population genetic structure, and formulating quarantine strategies of PWN.
Author Contributions
A.Y. designed the study, conducted the experiment, performed the data analysis and wrote the article; X.D. guided the research design, article writing and data analysis, and revised the manuscript; Y.F. and T.C. collected the samples; J.Y. guaranteed the integrity of the entire study and approved the final version of the manuscript. All authors have read and agreed to the published version of the manuscript.
Funding
This project is supported by the National Key Research and Development Project 2021YFD1400903 (J.Y.).
Data Availability Statement
The original contributions presented in the study are included in the article; further inquiries can be directed to the corresponding author.
Acknowledgments
We thank all forestry bureaus that kindly provided nematode samples in China.
Conflicts of Interest
The authors declare no conflict of interest.
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Figure 1.
Homozygosity, SNP count, missing SNPs and private SNPs distributions of the SNPs found in 30 strains. Note: HB is Hubei strain, HEN is Henan strain, and HN is Hunan strain. The same is below.
Figure 1.
Homozygosity, SNP count, missing SNPs and private SNPs distributions of the SNPs found in 30 strains. Note: HB is Hubei strain, HEN is Henan strain, and HN is Hunan strain. The same is below.
Figure 2.
Box plots of SNP genotypes among 30 B. xylophilus strains.
Figure 3.
Principal component analysis (PCA) of population genetic structure: (a) PCA results of 30 strains based on 1312 SNP markers; (b) PCA results of 50 strains based on 2244 SNP markers. Note: GD is Guangdong strain; JS is Jiangsu strain.
Figure 3.
Principal component analysis (PCA) of population genetic structure: (a) PCA results of 30 strains based on 1312 SNP markers; (b) PCA results of 50 strains based on 2244 SNP markers. Note: GD is Guangdong strain; JS is Jiangsu strain.
Figure 4.
Phylogenetic trees of all 50 strains based on the neighbour-joining method. Note id is shown in parentheses.
Figure 4.
Phylogenetic trees of all 50 strains based on the neighbour-joining method. Note id is shown in parentheses.
Figure 5.
Introgression analysis revealed possible B. xylophilus migration routes.
Table 1.
Sample information of 50 B. xylophilus strains.
| Strain No. | Origin | Host | Sampling Date |
|---|---|---|---|
| GD02 | Qingcheng District, Qingyuan City, Guangdong Province | Pinus massoniana | 01/2015 |
| GD04 | Huiyang District, Huizhou City, Guangdong Province | P. massoniana | 01/2015 |
| GD08 | Boluo County, Huizhou City, Guangdong Province | P. massoniana | 01/2015 |
| GD10 | Huangpu District, Guangzhou City, Guangdong Province | P. massoniana | 01/2015 |
| GD14 | Dongguan, Guangdong Province | P. massoniana | 01/2015 |
| GD17 | Tianhe District, Guangzhou City, Guangdong Province | P. massoniana | 01/2015 |
| GD23 | Meijiang District, Meizhou City, Guangdong Province | P. massoniana | 11/2015 |
| GD26 | Fengshun County, Meizhou City, Guangdong Province | P. massoniana | 08/2017 |
| GD30 | Haifeng County, Shanwei City, Guangdong Province | P. massoniana | 08/2017 |
| GD32 | Dongyuan County, Heyuan City, Guangdong Province | P. massoniana | 08/2017 |
| HB01 | Chibi, Xianning City, Hubei Province | P. massoniana | 04/2015 |
| HB02 | Changyang County, Yichang City, Hubei Province | P. massoniana | 04/2015 |
| HB03 | Enshi City, Enshi Prefecture, Hubei Province | P. massoniana | 04/2015 |
| HB05 | Huangpi District, Wuhan City, Hubei Province | P. massoniana | 04/2015 |
| HB06 | Huangpi District, Wuhan City, Hubei Province | P. massoniana | 04/2015 |
| HB07 | Huangpi District, Wuhan City, Hubei Province | P. massoniana | 04/2015 |
| HB08 | Yiling District, Yichang City, Hubei Province | P. massoniana | 11/2015 |
| HB10 | Yidu District, Yichang City, Hubei Province | P. massoniana | 11/2015 |
| HB12 | Zengdu District, Suizhou City, Hubei Province | P. massoniana | 08/2017 |
| HB15 | Luotian County, Huanggang City, Hubei Province | P. massoniana | 08/2017 |
| HEN02 | Xin County, Xinyang City, Henan Province | P. massoniana | 08/2015 |
| HEN03 | Xin County, Xinyang City, Henan Province | P. massoniana | 08/2015 |
| HEN04 | Xin County, Xinyang City, Henan Province | P. massoniana | 08/2015 |
| HEN06 | Xichuan County, Nanyang City, Henan Province | P. massoniana | 10/2017 |
| HEN09 | Xichuan County, Nanyang City, Henan Province | P. massoniana | 11/2017 |
| HEN10 | Xichuan County, Nanyang City, Henan Province | P. massoniana | 11/2017 |
| HEN14 | Xixia County, Nanyang City, Henan Province | Pinus tabuliformis | 01/2018 |
| HEN15 | Xixia County, Nanyang City, Henan Province | P. massoniana | 10/2018 |
| HEN19 | Xin County, Xinyang City, Henan Province | P. massoniana | 10/2018 |
| HEN20 | Xin County, Xinyang City, Henan Province | P. massoniana | 10/2018 |
| HN01 | Cili County, Zhangjiajie City, Hunan Province | P. massoniana | 03/2015 |
| HN02 | Linxiang City, Yueyang City, Hunan Province | P. massoniana | 03/2015 |
| HN03 | Yunxi District, Yueyang City, Hunan Province | P. massoniana | 03/2015 |
| HN04 | Hengnan County, Hengyang City, Hunan Province | P. massoniana | 03/2015 |
| HN06 | Cili County, Zhangjiajie City, Hunan Province | P. massoniana | 08/2015 |
| HN07 | Taoyuan County, Changde City, Hunan Province | P. massoniana | 08/2016 |
| HN08 | Taoyuan County, Changde City, Hunan Province | P. massoniana | 08/2016 |
| HN09 | Lingling District, Yongzhou City, Hunan Province | P. massoniana | 03/2019 |
| HN10 | Shaoyang County, Shaoyang City, Hunan Province | P. massoniana | 03/2019 |
| HN13 | Lingling District, Yongzhou City, Hunan Province | P. massoniana | 03/2019 |
| JS01 | Liuhe District, Nanjing City, Jiangsu Province | P. massoniana | 12/2014 |
| JS02 | Runzhou District, Zhenjiang City, Jiangsu Province | P. massoniana | 12/2014 |
| JS06 | Binhu District, Wuxi City, Jiangsu Province | P. massoniana | 12/2014 |
| JS09 | Xuyi County, Huai‘an City, Jiangsu Province | P. massoniana | 01/2015 |
| JS11 | Haizhou District, Lianyungang City, Jiangsu Province | Pinus densiflora | 01/2015 |
| JS12 | Yizheng, Yangzhou City, Jiangsu Province | P. massoniana | 01/2015 |
| JS15 | Changshu City, Suzhou City, Jiangsu Province | P. massoniana | 02/2015 |
| JS19 | Runzhou District, Zhenjiang City, Jiangsu Province | P. massoniana | 10/2017 |
| JS31 | Lishui District, Nanjing City, Jiangsu Province | P. massoniana | 10/2017 |
| JS50 | Jintan District, Changzhou City, Jiangsu Province | P. massoniana | 10/2017 |
Table 2.
Summary of SNPs found in 30 B. xylophilus strains.
| Strain No. | SNP Count | Homozygous | Missing | Specific SNP Count |
|---|---|---|---|---|
| HB01 | 81,723 | 46,927 | 1,367,096 | 6644 |
| HB02 | 110,859 | 75,239 | 1,189,888 | 6547 |
| HB03 | 128,236 | 34,233 | 957,159 | 2695 |
| HB05 | 90,192 | 45,461 | 1,318,256 | 2291 |
| HB06 | 91,540 | 57,049 | 965,177 | 2290 |
| HB07 | 121,555 | 69,319 | 1,038,514 | 9634 |
| HB08 | 85,121 | 33,889 | 1,277,353 | 2195 |
| HB10 | 230,409 | 66,327 | 954,380 | 7499 |
| HB12 | 156,058 | 92,696 | 978,007 | 8018 |
| HB15 | 747,217 | 620,622 | 1,364,706 | 10,220 |
| HEN02 | 179,367 | 39,985 | 890,120 | 6229 |
| HEN03 | 115,598 | 73,932 | 1,032,402 | 13,361 |
| HEN04 | 161,298 | 59,004 | 863,933 | 6837 |
| HEN06 | 387,158 | 74,788 | 670,407 | 31,052 |
| HEN09 | 265,742 | 52,417 | 848,798 | 23,215 |
| HEN10 | 215,398 | 41,954 | 816,941 | 6011 |
| HEN14 | 111,679 | 61,536 | 918,903 | 4289 |
| HEN15 | 125,089 | 47,713 | 5,169,332 | 1586 |
| HEN19 | 220,318 | 37,791 | 5,087,640 | 5128 |
| HEN20 | 748,317 | 620,041 | 5,223,813 | 3968 |
| HN01 | 123,168 | 86,716 | 946,562 | 1316 |
| HN02 | 38,277 | 14,055 | 1,572,950 | 716 |
| HN03 | 124,574 | 91,984 | 928,412 | 1906 |
| HN04 | 58,334 | 25,275 | 1,484,882 | 2413 |
| HN06 | 373,296 | 26,536 | 1,482,689 | 80,083 |
| HN07 | 894,621 | 704,750 | 747,078 | 24,547 |
| HN08 | 878,428 | 611,007 | 837,408 | 12,192 |
| HN09 | 130,567 | 23,176 | 5,151,805 | 396 |
| HN10 | 1,210,980 | 1,033,119 | 5,248,382 | 505,849 |
| HN13 | 128,256 | 64,496 | 5,140,161 | 1855 |
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Yang, A.; Ding, X.; Feng, Y.; Chen, T.; Ye, J. Genetic Diversity and Population Structure of Bursaphelenchus xylophilus in Central China Based on SNP Markers. Forests 2023, 14, 1443. https://doi.org/10.3390/f14071443
AMA Style
Yang A, Ding X, Feng Y, Chen T, Ye J. Genetic Diversity and Population Structure of Bursaphelenchus xylophilus in Central China Based on SNP Markers. Forests. 2023; 14(7):1443. https://doi.org/10.3390/f14071443
Chicago/Turabian StyleYang, Aixia, Xiaolei Ding, Yuan Feng, Tingting Chen, and Jianren Ye. 2023. "Genetic Diversity and Population Structure of Bursaphelenchus xylophilus in Central China Based on SNP Markers" Forests 14, no. 7: 1443. https://doi.org/10.3390/f14071443
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