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Article

Genetic Diversity and Population Structure of Acanthopagrus latus in the South China Sea

by
Cheng-He Sun
1,2,*,
Qun Zhang
2,* and
Chang-Hu Lu
1
1
College of Life Sciences, Nanjing Forestry University, Nanjing 210037, China
2
Department of Ecology and Institute of Hydrobiology, Jinan University, Guangzhou 510632, China
*
Authors to whom correspondence should be addressed.
Animals 2025, 15(9), 1295; https://doi.org/10.3390/ani15091295
Submission received: 30 March 2025 / Revised: 27 April 2025 / Accepted: 29 April 2025 / Published: 30 April 2025
(This article belongs to the Section Animal Genetics and Genomics)
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Simple Summary

The Yellowfin seabream Acanthopagrus latus (Houttuyn, 1782) belongs to the order Spariformes and family Sparidae and is widely distributed in the Indo-Northwest Pacific, extending from the Persian Gulf in the west to the Philippines in the east, to Japan in the north, and to Australia in the south. Here, we analyzed the genetic structure of four wild A. latus populations in the South China Sea to provide insights into their genetic resources. Four populations were analyzed using whole-genome resequencing. Principal component analysis, phylogenetic tree construction, and population structure analysis revealed that individuals from different geographical populations were mixed and not clustered according to geographical location, indicating extensive gene exchange between populations; however, this does not rule out the impact of stock enhancement in the South China Sea in recent years.

Abstract

The yellowfin seabream Acanthopagrus latus (Houttuyn, 1782) belongs to the order Spariformes and family Sparidae and is tender, rich in fat, and nutritious, making it a high-value seafood variety along the China coast. The fry can be used for large-scale aquaculture in both seawater and freshwater. Due to environmental pollution and excessive fishing, natural A. latus populations have declined significantly in recent years, which severely depletes genetic diversity. Here, we analyzed the genetic structure of four wild A. latus populations in the South China Sea to provide insights into the genetic resources. In particular, we analyzed 40 samples from four sampling sites through whole-genome resequencing, yielding 515 Gb of raw data and 132,505,081 single nucleotide polymorphisms. Population structure, phylogenetic, and principal component analyses revealed that the four populations were homogeneous and did not show clustering based on geographical origin, indicating extensive admixture. This could be attributed to recent enhancement and release activities in the South China Sea. This information may help to protect and utilize A. latus germplasm resources.

1. Introduction

The Yellowfin seabream Acanthopagrus latus (Houttuyn, 1782) is widely distributed in the Indo-Northwest Pacific, extending from the Persian Gulf in the west to the Philippines in the east, Japan in the north, and Australia in the south [1,2]. Classified in the perciform family Sparidae, A. latus is a small- to medium-sized fish found in shallow, warm-temperate waters, with a typical body length of 200–300 mm [3,4,5]. It has a wide salt tolerance range and can survive in seawater with a salinity of 0.5~4.3‰. Occasionally, it enters estuaries or freshwater areas, and young fish mostly inhabit gentle, semi-saline waters within the bay [5,6,7]. It is omnivorous and benthic carnivorous, feeding mainly on polychaetes, mollusks, crustaceans, echinoderms, and other small fish species. This species does not present long-distance migratory behavior but exhibits noticeable reproductive migration. Approximately 2 months before the spawning period, it begins to move from nearshore brackish water to high-salinity deep-sea regions and returns to nearshore areas after spawning. The reproductive period is from October to January, and the peak spawning period is from November to December. From January to February of the following year, many juvenile fish appear at the intersection of ports and brackish water, and many natural fry can be caught in this period [1,2,5].
The flesh of A. latus is rich in fat and has high nutritional value. Therefore, it is considered a high-value seafood variety along the coast of China [6,7]. After domestication, the fry can be used for large-scale aquaculture in both seawater and freshwater. In the 1980s, advances in artificial breeding techniques for Chinese A. latus promoted the development of the saltwater and brackish freshwater aquaculture of this species. In some countries and regions with developed aquaculture industries, A. latus has become the main aquaculture species [6,7]. However, its breeding cycle is relatively long, and it takes 1.5–2 years to reach the market specification of approximately 250 g. In recent years, owing to environmental pollution and excessive fishing, natural A. latus populations have declined significantly, resulting in the severe depletion of genetic diversity [6,7]. The breeding population of A. latus also faces serious challenges associated with breeding germplasms owing to inbreeding and small-scale parental artificial breeding. Genetic diversity in natural populations can be influenced by the proliferation and release of aquatic organisms [8]; for example, the release of Chinese shrimp fry from the Shandong Peninsula has a complementary effect on their wild resources [9]. Regarding actual breeding and release activities, unmanaged stocking could have adverse effects on the genetic structure, species diversity, and ecosystem structure.
Analysis of long-term changes in the diversity and structure of A. latus populations is necessary to restore and protect resources. Population genetic analyses of A. latus are limited to analyses of the mitochondrial control region. Liu et al. [10] evaluated genetic polymorphism in the mitochondrial D-loop gene of three populations of A. latus in Xiamen, Zhuhai, and Haikou. Xia et al. [11] analyzed genetic diversity in a population of A. latus along the coast of South China based on the D-loop region, showing that the population could be divided into two groups with the Qiongzhou Strait as the boundary.
Whole-genome resequencing is an efficient method for obtaining genetic information from samples [12,13,14]. The main goal is to evaluate individuals (usually from different populations or regions) of a species with published reference genome sequences based on whole-genome sequencing [15]. This approach can provide insight into interspecific differences at the whole-genome sequence level and reveal the role of genetic variation in biological processes, molecular structures, and cellular components [12,16]. Population resequencing typically involves the whole-genome resequencing of multiple individuals for population-level analyses [17]. The accuracy and precision of genetic variation detected using population resequencing are higher than those using individual resequencing [18]. Therefore, population resequencing is an important tool in genomics and is frequently used to study changes in gene and genotype frequencies within a population, providing a basis for resolving various issues, such as environmental adaptability [19].
In this study, whole-genome resequencing data from four populations are used to evaluate the genetic structure of A. latus using various approaches, including principal component, population structure, and phylogenetic analyses. The aim of this study is to provide scientific data for the development, utilization, and conservation of A. latus genetic resources.

2. Materials and Methods

2.1. Sample Collection and DNA Extraction

In this study, a total of 40 samples (ten samples from four different wild A. latus populations, with samples collected randomly) were purchased from local fishers with commercial fishing permits. Samples were collected from Pingtan, Fujian (PT), Yangjiang, Guangdong (YJ), Anpu, Guangxi (AP), and Fangchenggang, Guangxi (FCG) (Table 1; Figure 1). Fresh samples were identified based on morphological features and numbered according to the geographical location. The back muscles of the fish were then dissected, cut, and immersed in anhydrous ethanol at −20 °C for storage. All specimens in this study were collected in accordance with Chinese laws. Specimen collection was reviewed and approved by the Animal Ethics Committee of Jinan University (No. jnu20230109.2). The phenol–chloroform method [20] was used for genomic DNA extraction, and the extracted DNA samples were stored at −80 °C. The concentration and mass of the total genomic DNA were determined using a NanoDrop 2000 spectrophotometer (Thermo Fisher Scientific, Waltham, MA, USA).

2.2. Whole-Genome Resequencing

Sequencing was performed by Wuhan BENAGEN Technology Co., Ltd. (Wuhan, China) Filtering and quality control were also completed by Wuhan BENAGEN Technology Co., Ltd., (Wuhan, China) and performed in accordance with the company’s standardized procedures. High-quality, minimally degraded DNA with good continuity was randomly sheared. Using the standard protocol provided by the second-generation sequencing company, a DNA library (350 bp) was constructed for Illumina sequencing for each sample [21]. After DNA library construction, double-terminal sequencing was performed on the Illumina HiSeq 2000 platform (Illumina, San Diego, CA, USA) with a read length of 150 bp. Raw reads were filtered to obtain high-quality sequences for subsequent analyses.

2.3. Data Analysis

Genome sequencing data after quality control were compared with the reference genome for A. latus (RefSeq: GCF_904848185.1, https://www.ncbi.nlm.nih.gov/datasets/genome/GCF_904848185.1/, accessed on 23 December 2023) using BWA-MEM2 v2.2 [22], with default parameters. After quality control, the BWA-MEM algorithm was used to compare sequencing reads with the reference genome of A. latus. The mapping results were sorted, and duplicate reads were removed using samtools v1.9. The HaplotypeCaller, CombineGVCFs, GenotypeGVCFs, and SelectVariables modules in GATK v4.2.0 were used to obtain single nucleotide polymorphism (SNP) and insertion/deletion information, with standard parameters. Subsequently, the mutation information was filtered using the VariantFiltration module. Annovar and SnpEff were used to annotate the SNPs [23,24].
MEGA v5.0 [25] was used to create alignments. Based on the neighbor-joining method, a p-distance–based phylogenetic tree was constructed. Using EIGENSOFT v5.01 [26], principal component analysis (PCA) was conducted based on SNP data to evaluate the clustering of samples. The population structure was analyzed using ADMIXTURE v1.3.0 [27], setting the number of subgroups (K-value) to 1–10 for clustering and using cross-validation to determine the optimal number of subgroups based on the valley value of the cross-validation error rate. Pairwise genetic relationships between individuals were estimated using GCTA [28], and linkage disequilibrium was analyzed using Plink v1.07 [29]. Using VCFtools v0.1.16 [30], we calculated various population genetic indicators (Ti/Tv, heterozygosity, and homozygosity) according to the specified window (100 kb) and step size (10 kb).

3. Results

3.1. Sequencing Data Statistics

Using the Illumina HiSeq 2000 platform, a double-terminal sequencing library was constructed and sequenced based on 40 A. latus specimens. In total, 515 Gb of raw sequencing data were obtained. After quality control (Table 2), the clean reads accounted for 99.11% of all sample data. The average Q30 value (i.e., the proportion of bases with a Phred mass value greater than 30 relative to the total bases) was 93.61%. Therefore, the resequencing data in this study exhibited good quality and high accuracy and could be used for further analyses.
For the 40 samples, the average proportion of reads that matched the reference sequence was 97.21%, the average sequencing depth was 14.13×, and the average sequencing coverage was 99.42%. Specific information regarding resequencing data mapping is presented in Table 3.

3.2. SNP Detection

In total, 132,505,081 SNPs were detected in the 40 samples. The number of transitions ranged from 1,712,297 to 6,368,531, and the number of transversions ranged from 982,734 to 3,994,927. The transition-to-transversion ratio was 1.59–1.74. The number of heterozygous mutations was 1,925,968–6,760,257, and the number of homozygous mutations was 734,866–3,603,201 (Table 4).

3.3. Phylogenetic Analysis and PCA

All samples of A. latus clustered together (Figure 2), irrespective of geographical origin (indicated by different colors). Although most individuals in the PT population clustered in a single branch, individuals in the AP and YJ populations were interspersed. PCA of the A. latus samples indicated that most individuals in the four populations clustered together, while the explanatory power of the first two principal components was very low. This was consistent with the results of the phylogenetic analysis (Figure 3). Both the FCG and PT populations included individuals that did not cluster well with other individuals.

3.4. Population Structure and Linkage Disequilibrium Analysis

Cross-validation error rates for the number of clusters (K) between one and 18 exhibited an upward trend (Figure 4). The cross-validation error rate was the smallest when K = 2. The optimal number of clusters was K = 1, indicating that the samples shared one gene library. The population structure analysis results (Figure 4) were consistent with the phylogenetic tree and PCA results. All individuals belonged to a single, large South China Sea population and showed a high degree of kinship among individuals (Figure 5). The linkage disequilibrium (LD) (Figure 6), based on pairwise r2 values, indicated that LD coefficients for the populations decreased in the following order: FCG > PT > AP > YJ. The decay rates from fast to slow were as follows: YJ > AP > PT > FCG.

4. Discussion

In this study, our goals included determining the genetic diversity and population structure of A. latus in the South China Sea. The average sequencing depth and coverage were high, indicating that our sequencing data could accurately reflect genetic variation in A. latus. The transition-to-transversion ratio for SNPs was >1.5, indicating that A. latus exhibits transformational reversal bias, as observed in most vertebrates [31]. Admixture analysis suggested that the four populations were “completely admixed”. Clustering analyses, PCA, and phylogenetic analyses suggested that the four populations could be collectively referred to as the South China Sea population.
Genetic diversity is the foundation for species survival, adaptation, and evolution [32,33]. High genetic diversity within a species leads to an enhanced ability to adapt to the environment and improves evolutionary potential [34]. Extensive enhancement and release activities have been conducted in the South China Sea in recent years [3,4,5]. The fact that all four populations were from the South China Sea may have resulted from these activities.
He et al. [35] conducted a comprehensive genetic diversity analysis of eight populations of A. latus along the coast of South China, using mitochondrial control region sequences. They detected high overall genetic diversity and identified the populations east and west of the Qiongzhou Strait as two management units, consistent with previous results. Many provinces and cities in China have attempted to breed and release A. latus; however, recent reports on the genetic diversity of natural populations of A. latus are limited [35]. Genetic information on changes in the wild germplasm resources of this species after release is also lacking, hindering management and conservation.
Many artificially bred fry have entered natural waters. If the genetic diversity of the released population is significantly lower than that of the wild population, large-scale breeding and release activities are likely to reduce the overall genetic diversity of the species, thereby affecting its sustainable development. This can occur if the released population is derived from a small number of parents or if the contribution rate of gametes from different parents to offspring is imbalanced [36], in which case the genetic diversity of the released population will generally be lower than that of the wild population. In addition, fitness is highly correlated with genetic diversity, and the fitness of a released population is influenced by the genetic diversity of its parents [36]. If fry with low fitness are introduced in natural waters, hybridization between the released and wild populations may increase the frequency and expression of harmful recessive genes in the wild population, ultimately leading to species degradation. A decrease in the genetic diversity due to stocking can lead to an increase in harmful recessive gene expression, a decline in certain adaptive traits, and consequent changes in population fitness, resulting in problems such as low survival rates, weak reproductive ability, slow growth, and poor adaptability [37,38]. Therefore, the diversity of parental strains for fry breeding should not be lower than that of wild populations, if possible, and the number of parents subjected to breeding should be maximized to avoid reductions in genetic diversity and fitness. Blankenship and Leber [39] suggested the implementation of genetic monitoring and management of parents and released populations when evaluating the effectiveness of proliferation and release.

5. Conclusions

Overall, there were no significant differences among the four populations of A. latus, indicating significant homogenization. Various techniques (e.g., phylogenetic analysis, clustering, PCA, and kinship analysis) showed that the four populations formed a well-supported cluster. These results suggest that proliferation and release activities have played a crucial role in shaping the genetic structure of A. latus populations. This study provides an important reference for the protection and utilization of germplasm resources of this species.

Author Contributions

Formal analysis, methodology, software, and writing—original draft, C.-H.S.; methodology, supervision, and writing—review and editing, Q.Z.; supervision, and writing—review and editing, C.-H.L. All authors have read and agreed to the published version of the manuscript.

Funding

This research was supported by the Project of Financial Funds of Ministry of Agriculture and Rural Affairs: Investigation of Fishery Resources and Habitat in the Pearl River Basin; Fishery Resources Survey of Guangxi Zhuang Autonomous Region [grant number GXZC2022-G3-001062-ZHZB] and the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD).

Institutional Review Board Statement

All specimens in this study were collected in accordance with relevant Chinese laws. Procedures for the collection and sampling of the specimens were reviewed and approved by the Animal Ethics Committee of Nanjing Forestry University (approval code 2024030, date: 26 March 2024) and Jinan University (approval code 2021012, date: 10 January 2021). All experiments were conducted following animal welfare and care guidelines.

Informed Consent Statement

Not applicable.

Data Availability Statement

Genome assemblies and raw sequence data from SRA were deposited in NCBI’s Assembly database under BioProject accession number PRJNA1175469. https://www.ncbi.nlm.nih.gov/bioproject/PRJNA1175469 (accessed on 21 October 2024).

Acknowledgments

We are very grateful to the editor and reviewers for critically evaluating the manuscript and providing constructive comments for its improvement.

Conflicts of Interest

The authors declare no conflicts of interest.

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Animals 15 01295 g001
Figure 1. Sampling sites for Acanthopagrus latus.
Figure 1. Sampling sites for Acanthopagrus latus.
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Figure 2. Neighbor-joining phylogenetic tree based on whole-genome single-nucleotide polymorphism (SNP) data for Acanthopagrus latus populations.
Figure 2. Neighbor-joining phylogenetic tree based on whole-genome single-nucleotide polymorphism (SNP) data for Acanthopagrus latus populations.
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Figure 3. Principal component analysis (PCA) plot of Acanthopagrus latus samples.
Figure 3. Principal component analysis (PCA) plot of Acanthopagrus latus samples.
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Figure 4. Population structure plots with K = 2 to 4 for Acanthopagrus latus populations.
Figure 4. Population structure plots with K = 2 to 4 for Acanthopagrus latus populations.
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Figure 5. Visualization of G-matrix analysis results for Acanthopagrus latus populations.
Figure 5. Visualization of G-matrix analysis results for Acanthopagrus latus populations.
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Figure 6. Linkage disequilibrium decay for Acanthopagrus latus populations.
Figure 6. Linkage disequilibrium decay for Acanthopagrus latus populations.
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Table 1. Information on Acanthopagrus latus sample collection.
Table 1. Information on Acanthopagrus latus sample collection.
Sampling SiteSample No.CoordinatesSample SizeSample Date
Pingtan, FujianFT01-10119.80° E, 25.52° N104/2021
Yangjiang, GuangdongYJ01-10111.84° E, 21.58° N1012/2021
Anpu, GuangdongAP01-10109.92° E, 21.43° N1012/2021
Fangchenggang, GuangxiFCG01-10108.36° E, 21.76° N104/2021
Table 2. Quality control for resequencing data.
Table 2. Quality control for resequencing data.
SamplesTotal ReadsClean ReadsPercentage of Clean ReadsClean BasesGC Content%>Q20%>Q30
PT01101,809,808100,964,80699.17%15,071,611,23241.84%97.72%93.39%
PT0276,468,81075,751,66499.06%11,308,412,16941.78%97.71%93.47%
PT0387,559,76286,760,62099.09%12,960,643,58341.77%97.53%93.01%
PT0468,839,29068,220,42899.10%10,177,228,39141.64%97.52%92.92%
PT0575,535,39874,971,18499.25%11,191,679,42041.83%97.82%93.61%
PT0667,075,94866,449,74499.07%9,913,777,07541.87%97.45%92.81%
PT07108,258,312107,473,01699.27%16,047,612,53041.77%98.04%94.15%
PT0889,303,55288,674,56099.30%13,244,598,36541.60%98.01%94.07%
PT0986,840,37686,052,66699.09%12,836,784,97941.91%97.63%93.24%
PT1089,958,68689,252,39099.21%13,320,160,71541.64%97.79%93.55%
YJ0190,160,31689,401,88099.16%13,330,989,54741.95%98.00%94.09%
YJ0257,567,50257,051,92699.10%8,517,653,50741.91%97.50%92.94%
YJ0347,270,77646,781,76098.97%6,981,618,75042.07%97.53%93.07%
YJ0488,343,85287,589,34899.15%13,066,629,05541.87%97.68%93.39%
YJ0582,130,62281,490,46899.22%12,152,811,87542.07%98.04%94.21%
YJ0689,258,33488,489,37499.14%13,207,771,04941.96%97.54%92.96%
YJ07137,332,282135,238,02498.48%18,602,555,95342.16%97.41%93.33%
YJ08184,719,214183,261,69899.21%27,348,916,62041.88%98.07%94.25%
YJ0969,634,44868,875,34898.91%10,281,000,27342.27%97.47%92.88%
YJ10191,434,786189,944,89699.22%28,352,038,41141.36%98.05%94.20%
AP0161,502,90660,875,95898.98%9,090,671,08541.90%97.62%93.26%
AP0270,704,21870,013,21299.02%10,460,754,70241.88%97.37%92.66%
AP0352,874,55852,325,70498.96%7,819,456,12042.07%97.48%92.92%
AP0481,233,15480,524,94899.13%12,031,774,27741.82%97.75%93.51%
AP0576,994,05476,440,74499.28%11,290,946,42239.96%97.91%93.87%
AP0669,974,79869,337,56099.09%10,362,493,90942.12%97.62%93.15%
AP0741,426,42040,999,86698.97%6,122,846,16742.14%97.36%92.63%
AP0886,534,52485,750,70299.09%12,808,516,56942.26%97.97%94.00%
AP0979,513,19678,778,20899.08%11,769,256,49242.23%97.86%93.75%
AP1081,170,81280,450,25499.11%12,024,905,82042.29%97.93%93.91%
FCG0184,932,01684,282,55299.24%12,537,445,29541.93%97.99%94.03%
FCG0281,907,40081,263,58699.21%12,092,232,81041.76%97.86%93.62%
FCG0361,745,04461,744,694100.00%9,125,633,72138.21%97.62%93.05%
FCG0483,019,15482,443,34499.31%12,167,842,42841.07%98.05%94.21%
FCG05114,709,826112,695,64698.24%16,821,503,66141.98%98.10%94.55%
FCG0682,966,56082,035,81898.88%12,212,423,00141.70%97.73%93.56%
FCG07103,097,888102,132,85499.06%15,113,755,76541.89%98.18%94.82%
FCG08120,985,092119,862,31099.07%17,422,493,98041.79%98.13%94.77%
FCG0986,941,47686,193,75299.14%12,771,778,69141.05%98.16%94.66%
FCG1090,569,54689,881,58499.24%13,344,103,63841.26%97.92%94.10%
Table 3. Results of re-sequencing data mapping.
Table 3. Results of re-sequencing data mapping.
SampleTotal ReadsMappedMapping Rate (%)Depth (X)Coverage (%)
PT01100,964,80699,759,77498.8117.4299.59%
PT0275,751,66474,968,41998.9713.8299.48%
PT0386,760,62085,869,95798.9715.3599.55%
PT0468,220,42867,098,51398.3611.3599.54%
PT0574,971,18473,679,32198.2812.5799.48%
PT0666,449,74465,678,98598.8412.1699.47%
PT07107,473,016105,097,49197.7917.199.61%
PT0888,674,56087,780,57198.9914.8799.53%
PT0986,052,66685,194,99499.0014.8899.54%
PT1089,252,39088,455,35999.1115.5299.54%
YJ0189,401,88088,135,27098.5814.9899.49%
YJ0257,051,92656,647,41099.2910.7799.37%
YJ0346,781,76046,405,60199.208.6999.29%
YJ0487,589,34886,906,92199.2215.3399.50%
YJ0581,490,46880,833,04999.1914.0199.45%
YJ0688,489,37487,826,15899.2515.2499.52%
YJ07135,238,024134,656,29599.5719.599.54%
YJ08183,261,698180,627,06698.5630.7799.60%
YJ0968,875,34868,316,44099.1912.1699.47%
YJ10189,944,896185,373,47697.5931.4799.63%
AP0160,875,95860,412,28899.2411.1199.42%
AP0270,013,21269,428,85299.1712.8399.44%
AP0352,325,70451,934,96899.259.8199.31%
AP0480,524,94879,950,87299.2914.2299.47%
AP0576,440,74474,675,88997.6912.8199.32%
AP0669,337,56068,755,80299.1611.7599.41%
AP0740,999,86640,615,78699.067.5999.13%
AP0885,750,70284,991,68699.1114.7699.52%
AP0978,778,20877,967,50598.9713.5699.49%
AP1080,450,25479,801,24499.1913.6199.48%
FCG0184,282,55283,620,64699.2114.3499.52%
FCG0281,263,58676,406,91394.0212.4398.37%
FCG0361,744,69459,723,42596.738.5699.11%
FCG0482,443,34473,825,04689.5512.2599.44%
FCG05112,695,646103,290,25391.656.7699.13%
FCG0682,035,81858,450,65271.258.7799.18%
FCG07102,132,85498,320,75696.2715.6799.49%
FCG08119,862,310115,463,53196.3318.9299.55%
FCG0986,193,75275,039,44187.0611.9599.42%
FCG1089,881,58489,313,29999.3715.5499.48%
Table 4. Population genetic statistics.
Table 4. Population genetic statistics.
SampleSNP NumberTransitionTransversionTi/TvHeterozygosityHomozygosityHet. Ratio
PT013,230,6082,049,8911,180,7171.742,390,214840,39473.99
PT023,177,1952,016,5061,160,6891.742,326,580850,61573.23
PT033,207,0602,033,1071,173,9531.732,373,126833,93474
PT043,113,5471,974,1291,139,4181.732,378,681734,86676.4
PT053,151,9471,999,4311,152,5161.732,331,673820,27473.98
PT063,133,5971,989,4561,144,1411.742,275,313858,28472.61
PT073,231,0872,048,3651,182,7221.732,417,106813,98174.81
PT083,202,2912,030,8061,171,4851.732,348,822853,46973.35
PT093,196,3532,028,1811,168,1721.742,362,823833,53073.92
PT103,209,2362,035,8161,173,4201.742,367,876841,36073.78
YJ013,204,8032,035,4521,169,3511.742,346,537858,26673.22
YJ023,076,3711,953,6851,122,6861.742,206,752869,61971.73
YJ032,949,8811,872,7351,077,1461.742,071,177878,70470.21
YJ043,210,2812,036,7771,173,5041.742,350,626859,65573.22
YJ053,190,6762,025,5211,165,1551.742,332,962857,71473.12
YJ063,210,8272,037,8111,173,0161.742,352,293858,53473.26
YJ073,183,7522,018,3151,165,4371.732,326,536857,21673.08
YJ083,289,6392,083,7231,205,9161.732,430,629859,01073.89
YJ093,145,0021,997,7421,147,2601.742,281,389863,61372.54
YJ103,292,8332,086,0241,206,8091.732,431,034861,79973.83
AP013,104,3731,970,4831,133,8901.742,242,025862,34872.22
AP023,157,0932,003,5471,153,5461.742,308,917848,17673.13
AP033,031,0911,925,3321,105,7591.742,162,685868,40671.35
AP043,188,5832,022,9461,165,6371.742,336,169852,41473.27
AP053,051,0781,931,1491,119,9291.722,188,455862,62371.73
AP063,119,8271,981,1121,138,7151.742,273,222846,60572.86
AP072,818,1901,790,3121,027,8781.741,925,968892,22268.34
AP083,209,9832,036,3861,173,5971.742,367,467842,51673.75
AP093,184,5902,020,7561,163,8341.742,325,943858,64773.04
AP103,182,8852,019,8521,163,0331.742,338,087844,79873.46
FCG013,200,6082,030,8771,169,7311.742,343,023857,58573.21
FCG0210,363,4586,368,5313,994,9271.596,760,2573,603,20165.23
FCG032,835,0171,799,0181,035,9991.741,946,792888,22568.67
FCG043,103,3221,970,2221,133,1001.742,236,762866,56072.08
FCG052,695,0311,712,297982,7341.741,953,310741,72172.48
FCG062,901,4761,836,7051,064,7711.722,020,702880,77469.64
FCG073,207,6122,036,5911,171,0211.742,351,474856,13873.31
FCG083,240,6862,055,0971,185,5891.732,383,249857,43773.54
FCG093,108,4111,972,7681,135,6431.742,243,918864,49372.19
FCG103,194,7812,026,6721,168,1091.742,334,079860,70273.06
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Sun, C.-H.; Zhang, Q.; Lu, C.-H. Genetic Diversity and Population Structure of Acanthopagrus latus in the South China Sea. Animals 2025, 15, 1295. https://doi.org/10.3390/ani15091295

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Sun C-H, Zhang Q, Lu C-H. Genetic Diversity and Population Structure of Acanthopagrus latus in the South China Sea. Animals. 2025; 15(9):1295. https://doi.org/10.3390/ani15091295

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Sun, Cheng-He, Qun Zhang, and Chang-Hu Lu. 2025. "Genetic Diversity and Population Structure of Acanthopagrus latus in the South China Sea" Animals 15, no. 9: 1295. https://doi.org/10.3390/ani15091295

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Sun, C.-H., Zhang, Q., & Lu, C.-H. (2025). Genetic Diversity and Population Structure of Acanthopagrus latus in the South China Sea. Animals, 15(9), 1295. https://doi.org/10.3390/ani15091295

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