Marine Biology

Genetic Structure and Diversity Analysis of Three Natural Populations of Tectus pyramis Based on Specific Locus Amplified Fragment Sequencing*

  • HUANG Jing , 1, 2 ,
  • OU Zhekui 1, 2 ,
  • LIU Wenguang 1 ,
  • HE Maoxian , 1
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  • 1. CAS Key Laboratory of Tropical Marine Bio-resources and Ecology, Guangdong Provincial Key Laboratory of Applied Marine Biology, South China Sea Institute of Oceanology, Institution of South China Sea Ecology and Environmental Engineering, Chinese Academy of Sciences, Guangzhou 510301, China
  • 2. University of Chinese Academy of Sciences, Beijing 100049, China
HE Maoxian. E-mail:

HUANG Jing (1993—). E-mail:

Copy editor: YIN Bo

Received date: 2019-12-22

  Request revised date: 2020-02-28

  Online published: 2020-02-28

Supported by

Strategic Priority Research Program of the Chinese Academy of Sciences(XDA13020206)

Innovation Academy of South China Sea Ecology and Environmental, Engineering, Chinese Academy of Sciences(ISEE2018PY03)

Innovation Academy of South China Sea Ecology and Environmental, Engineering, Chinese Academy of Sciences(ISEE2018ZD02)

Science and Technology Service Network Initiative of Chinese Academy of Sciences(KFJ-STS-ZDTP-055)

Science and Technology Planning Project of Guangdong Province, China(2017B030314052)

Copyright

Copyright reserved © 2020. Office of Acta Agronomica Sinica All articles published represent the opinions of the authors, and do not reflect the official policy of the Chinese Medical Association or the Editorial Board, unless this is clearly specified.

Abstract

The marine mollusc Tectus pyramis inhabits the coral reef ecosystems surrounding Dapeng Bay in Shenzhen (SZ), Hainan Island, Xisha (XS) and Nansha Islands of China. In this study, the genetic diversity and genetic structure of three natural populations of T. pyramis from SZ, Sanya (SY) and XS were assessed using specific locus amplified fragment (SLAF) sequencing. A total of 115.74 Mb reads was obtained, with an average depth of 337.87-fold, and an average Q30 value of 94.07 %. Based on 118679 polymorphic SLAF tags, we obtained 846502 highly consistent population single-nucleotide polymorphisms (SNPs), and found 155 SNPs displayed significant differences between populations. Average observed (Ho) and expected (He) heterozygosity values for the three populations ranged from 0.1441 to 0.1611 and from 0.2537 to 0.2695, respectively. Average polymorphism information content (PIC) values were between 0.2048 and 0.2176. The 45 individuals were roughly divided into three groups by phylogenetic tree analysis, principal component analysis (PCA) and clustering; and all the individuals from each population could be clustered into a single group. The genetic structures of SZ and SY populations were similar, yielding the lowest genetic distance (0.2510) and Fst value (0.0818). However, genetic differentiation between XS and the other two populations was significant, especially between SX and SZ (Fst = 0.1868). Genetic differentiation of the three populations may be correlated to ocean currents and geographical locations. The results of this study provide a theoretical basis for germplasm resource management and future aquaculture breeding programs.

Cite this article

HUANG Jing , OU Zhekui , LIU Wenguang , HE Maoxian . Genetic Structure and Diversity Analysis of Three Natural Populations of Tectus pyramis Based on Specific Locus Amplified Fragment Sequencing*[J]. Journal of Tropical Oceanography, 2020 , 39(5) : 1 -18 . DOI: 10.11978/2019136

*We are grateful to the Science and Technology Planning Project of Guangdong Province, China, for providing technical assistance.
Tectus pyramis, an economically important marine shellfish belonging to Mollusca, Gastropoda, Archaeogastropoda, Trochidae, is widely distributed in the South China Sea, including Guangdong coast, Hainan Island, Xisha and Nansha Islands of China, as well as the Indian Ocean and western Pacific Ocean (Shi et al, 2019). This species lives on the bottom of reefs or coral reefs at a depth of 10 m in the subtidal zone, and feeds on algae in shallow water (Chen et al, 2015). The shell is important raw material for the chemical industry, the pearl layer has an extremely high economic value, and the meat is nutritious, making this species important for aquaculture. It also is an important component of coral reef ecosystems to promote the health of marine ecology. In recent years, however, the abundance of T. pyramis has been decreasing. Thus, investigating the genetic structure and diversity of this species can contribute to the management of germplasm resources, artificial breeding to restoring population density. However, the genetic structure and diversity of its germplasm are still unknown. At present, there are few reports about its artificial promoting maturation, induced spawning and seedling raising (Zhang et al, 2008; Chen et al, 2015), and microscopic observation of gonad structure (Li et al, 2008; Wu et al, 2008).
Specific locus amplified fragment sequencing (SLAF-seq) is a simplified deep genome sequencing technology based on high-throughput analysis (Wang et al, 2015). It has the advantages including low cost, high accuracy, high-throughput, high specificity, and good stability and repeatability (Wang et al, 2016b). So, it has been applied to the construction of high-density genetic linkage maps, genetic structure analysis, phylogenetic evolution, and germplasm resource identification (Chen et al, 2013; Laghari et al, 2013; Bai et al, 2016; Huang et al, 2016; Li et al, 2017).
In the present study, genomic single-nucleotide polymorphisms (SNPs) of T. pyramis were developed by using SLAF-seq. The SNPs corresponding to population differences were screened, and genetic relationships among three natural populations were analysed. These results may be beneficial for the conservation of germplasm resources and genetic diversity of T. pyramis, and for future management of breeding programs for aquaculture.

1 Materials and Methods

1.1 Sample Collection and Preparation

Three natural populations of T. pyramis were collected in April — June, 2017, by diving near Shenzhen (SZ, Guangdong Province), Sanya (SY, Hainan Province) and Xisha Islands (XS, Hainan Province) (Table 1). Then, foot tissues from each individual sized 5 ~ 6 cm were extracted and stored in 90 % alcohol.
Tab. 1 Sampling site of Tectus pyramis
Population Names Location Latitude Longitude
SZ Shenzhen 114°28′23.53″N 22°31′24.59″E
SY Sanya 109°26′47.04″N 18°12′01.76″E
XS Xisha 112°21′06.09″N 16°49′10.46″E
Genomic DNA from the 15 individuals of each population was extracted from foot tissue using a HiPure Universal DNA Kit (Magen, China) according to the manufacturer’s instructions. The concentration of extracted DNA was estimated from the absorbance at 260 nm divided by the absorbance at 280 nm (OD 260/280) measured using a spectrophotometer (NanoDrop, Wilmington, DE, USA), and DNA quality was analysed by agarose gel electrophoresis.

1.2 Enzyme Digestion Design

Based on the genome size and guanine-cytosine (GC) content of T. pyramis, the SLAF-seq experiment was designed using the 359 Mb reference genome of Lottian gigantean (Simakov et al, 2013; PRJNA175706). In order to obtain the maximum number of SLAF tags, the reference genome was analysed using internally developed enzyme digestion prediction software (Sun et al, 2013). The selection criteria are: 1) a low percentage of enzyme fragment sequences located in repetitive regions; 2) fragments distributed as evenly as possible across the genome; 3) enzyme fragment sequences were in good agreement with the experimental system (Davey et al, 2013).

1.3 Illumina HiSeq Sequencing and Data Evaluation

The optimal digestion scheme was applied for each sample, and genomic DNA was digested with RsaⅠ and HaeⅢ to generate SLAF tags, followed by end-repair, A-tailing, dual-index paired-end adapter ligation, PCR amplification, sample purification, and gel extraction to yield target fragments for SLAF library construction. The library was qualified and sequenced using an Illumina HiSeq 2500 platform (Illumina Inc., San Diego, CA, USA). In order to evaluate the accuracy of the enzyme digestion experiment, Oryza sativa ssp. japonica (http://rapdb.dna.affrc.go.jp/) was used as a control for sequencing.
Raw data obtained by sequencing were analyzed using Cutadapt software (Kozich et al, 2013) to obtain reads for each sample. After filtering sequencing read linkers, the quality of sequencing and the amount of data were evaluated by calculating the number of reads, the GC content, and the sequencing quality score (Q30), 99.9 % accuracy and one error per 1000 bp are generally used as benchmarks of quality. The efficiency of the experimental procedure was judged using the control data to evaluate the efficiency of enzyme digestion, and sequencing reads for controls were compared with the reference genome using SOAP2 (Li et al, 2009b). Reads were derived for each sample from the same lengths of DNA generated using the same restriction enzymes, and reads for each sample were clustered according to sequence similarity by BLAT software (Kent, 2002). The sequence similarity between the same SLAF tags in different samples was much higher than the similarity between different SLAF tags, and SLAF tags displaying differences in sequence (i.e., polymorphisms) between different samples were defined as polymorphic SLAF tags.

1.4 SNP Information Statistics

Based on a bioinformatics analysis, sequencing reads were aligned using the Burrows-Wheeler alignment tool (BWA) (Li et al, 2009a), genome-wide SNP markers in populations were developed using GATK3.2 and SAMtools (McKenna et al, 2010; Li et al, 2009a), and SNP markers identified by both methods were used as the final reliable SNP marker dataset. The A, T, C, G, and N ratios were calculated for all three populations, and Fisher tests were performed to identify significant differences between populations based on SNPs, yielding p-values (p ≤ 0.001).

1.5 Phylogenetic Tree Construction and Principal Component Analysis (PCA)

First, consistent SNP markers were obtained based on integrity > 0.5 and minor allele frequency (MAF) > 0.05. A phylogenetic tree was then drawn using MEGA 6.0 software (Tamura et al, 2013) based on the neighbour-joining algorithm (1000 bootstraps), and PCA was performed using Cluster (De Hoon et al, 2004).

1.6 Selective Sweep Analysis

Using highly consistent SNPs, the PopGen3.0 module of Bioperl software (http://bioperl.org/howtos/PopGen_HOWTO.html) was used to calculate population genetic indicators with a window of 100 kb and a step size of 10 kb (i.e., Fst values among the three populations).

1.7 Genetic Diversity and Genetic Structure Analyses

Based on the SNP markers identified in this study, the number of subgroups (K) was predicted from 1 to 10, the population structure (clustering) of individuals was analyzed by using Admixture software 1.22 (Alexander et al, 2009) to explore individual sources of origin and composition information, and clustering results were cross-validated. The genetic distance (D) of the three populations was calculated by using MEGA 6.0 software based on the Kimura 2-parameter model method (Kimura, 1980; Tamura et al, 2013).
Population polymorphism analysis was performed using representative high-quality SNPs within populations. The expected heterozygosity (He), observed heterozygosity (Ho), observed number of alleles (Na), effective number of alleles (Ne), Shannon information index (I), Nei’s diversity index, Minor allele frequency (MAF), polymorphism information content (PIC), genetic similarity coefficient (S), genetic differentiation coefficient (Fst), genetic distance (D), and gene flow (Nm) were estimated using MIGRATE-N software (Beerli, 2008).

2 Results

2.1 Assessment of Enzyme Digestion Scheme

According to the selection principles for the digestion scheme, the restriction enzymes RsaⅠ and HaeⅢ were selected based on the reference genome of Lottian gigantean, resulting in 115389 predicted 264 ~ 364 bp SLAF tags.

2.2 Sequencing Data Statistics and Evaluation of Experimental Database Construction

In order to ensure the quality of the analysis, only reads with a length of 100 bp × 2 were utilized. Statistics for sequencing data from the 45 individuals are listed in Table A1, including the number of reads, the Q30 value, and the GC content. Based on high-throughput sequencing, 115.74 Mb reads were obtained, with an average Q30 value of 94.07 % and average GC content of 38.54 %. Control sequencing data (from Oryza sativa ssp. japonica) used to assess the accuracy of the experimental database comprised 1.05 Mb reads. The high Q30 value and low base error rate indicate that the sequencing data were reliable.
Tab. A1 Sequencing data statistics of the 45 Tectus pyramis
Sample ID Total Reads Number GC Percentage / % Q30 Percentage / %
SZ1 2646767 38.73 94.04
SZ2 2868617 38.82 93.27
SZ3 3060839 38.49 93.71
SZ4 2928250 38.94 94.69
SZ5 3236397 37.86 94.48
SZ6 3043836 38.27 94.40
SZ7 2817405 38.63 94.61
SZ8 2655334 38.56 94.77
SZ9 2725248 38.29 94.58
SZ10 3277567 37.43 94.59
SZ11 3492934 38.09 94.81
SZ12 3137716 38.41 94.67
SZ13 2039042 39.02 94.27
SZ14 1444547 39.31 94.19
SZ15 1654279 39.17 93.96
SY1 1618225 39.35 94.28
SY2 1683896 39.19 93.37
SY3 1495848 39.33 93.70
SY4 1645635 39.30 93.83
SY5 1832431 38.72 94.03
SY6 2114769 38.64 94.02
SY7 2547407 37.89 93.08
SY8 2000207 38.68 94.10
SY9 2194326 38.79 94.68
SY10 1967775 39.10 94.86
SY11 1841572 39.09 94.93
SY12 2123797 38.81 94.87
SY13 2151154 39.17 94.85
SY14 1954747 38.74 94.66
SY15 2514960 37.97 93.42
XS1 2060640 38.78 94.46
XS2 3164280 38.32 93.82
XS3 3272003 38.32 94.18
XS4 3171569 37.92 93.92
XS5 3202603 38.38 94.07
XS6 2744002 38.28 93.71
XS7 2303163 38.29 93.63
XS8 3218785 37.93 93.95
XS9 2972343 38.81 94.23
XS10 2844445 37.76 92.61
XS11 3272801 38.08 93.30
XS12 3035468 38.46 93.76
XS13 2943254 38.05 93.17
XS14 2583726 38.19 93.10
XS15 3196614 37.88 93.54
Control 1046368 43.03 92.64

Note: Total Reads: number of reads for each sample; GC percentage: percentage of total bases of G and C in sequencing results; Q30 percentage: percentage of bases whose sequencing quality value is greater than or equal to 30; Control: data of Oryza sativa ssp. japonica for evaluating experimental database construction; SZ (1 to 15): SZ population; SY (1 to 15): SY population; XS (1 to 15): XS population

In total, 90.80 % of paired-end mapped reads were compared against the reference genome, with a distance between both ends of 50 bp to 1 kb, and a digestion efficiency of 90.70 %. Moreover, according to the locations of control sequencing paired-end mapped reads in the genome, the actual length of SLAF tags was calculated, and the length distribution map of control reads inserts was drawn (Fig. 1). In general, the high enzymatic digestion efficiency and comparison efficiency indicate successful SLAF library construction.
Fig. 1 Distribution of insert fragments in control sequence read maps. The abscissa represents the length of the insertion fragment, and the ordinate represents the percentage of reads of corresponding length

2.3 Development of SLAF Tags and SNP Markers

A total of 218422 SLAF tags were developed from the 45 samples, with an average depth per tag of 337.87-fold and a total depth of 73797190 (Table A2). Among these, 118679 SLAF tags displayed polymorphisms.
Tab. A2 SLAF tags statistics of the 45 Tectus pyramis
Sample ID SLAF number Total depth Average depth
SZ1 101537 1843808 18.16
SZ2 101886 2006414 19.69
SZ3 103244 2188358 21.20
SZ4 107505 2029284 18.88
SZ5 108067 2338011 21.63
SZ6 107524 2135565 19.86
SZ7 106819 1963328 18.38
SZ8 109780 1860332 16.95
SZ9 108621 1894662 17.44
SZ10 106877 2366741 22.14
SZ11 113736 2502851 22.01
SZ12 109751 2190045 19.95
SZ13 103761 1101996 10.62
SZ14 98465 781192 7.93
SZ15 100450 937431 9.33
SY1 97381 954691 9.80
SY2 98004 1017468 10.38
SY3 95481 909506 9.53
SY4 96922 996596 10.28
SY5 99774 1213282 12.16
SY6 101941 1350287 13.25
SY7 103004 1781398 17.29
SY8 101915 1228982 12.06
SY9 102675 1319548 12.85
SY10 101263 1145948 11.32
SY11 101126 1068060 10.56
SY12 103524 1246384 12.04
SY13 103145 1282700 12.44
SY14 103038 1148245 11.14
SY15 106571 1495169 14.03
XS1 103543 1212705 11.71
XS2 113977 2033850 17.84
XS3 113800 2082711 18.30
XS4 113523 2059873 18.14
XS5 114052 2075178 18.20
XS6 112858 1763198 15.62
XS7 109594 1472432 13.44
XS8 113536 2157654 19.00
XS9 112609 1938036 17.21
XS10 111522 1808792 16.22
XS11 113827 1923615 16.90
XS12 112561 1792407 15.92
XS13 112568 1747609 15.52
XS14 110288 1502944 13.63
XS15 113093 1927904 17.05
Total 218422 73797190 337.87

Note: Sample ID: Sample number; SLAF number: SLAF tag number of corresponding samples; Total depth: total depth of sequencing in SLAF tag of corresponding samples, that is, total reads number; Average depth: average number of sequencing reads of corresponding samples on each SLAF

The SNP markers were developed based on the sequence type with the highest depth per SLAF tag as the reference sequence. In total, 1197282 SNPs were aligned to the reference genome using BWA software. SNP information statistics obtained using GATK and SAMtools are shown in Table A3. On average, 2010144 population SNPs per accession were identified, equating to 16.9 SNPs per 1 kb of T. pyramis genome (115.74 Mb), and the integrity ratio of SNPs in all samples ranged from 49.33 % to 65.20 %, with a hetloci ratio of 9.44 % to 13.94 %. Among all the SNPs, 846502 highly consistent population SNPs were obtained, based on integrity > 0.5 and minor allele frequencies (MAF) > 0.05. For the SNPs related to species differentiation, A, T, C, G, and N ratios of the three populations were calculated, and Fisher tests were performed to determine whether there was a significant difference between species for SNPs. Finally, a significant difference analysis between the 45 samples was performed using the 846502 highly consistent population SNPs, and 155 SNPs exhibiting significant differences between the three populations were obtained (p ≤ 0.001; Table A4).
Tab. A3 SNP information statistics of the 45 Tectus pyramis
Sample ID Total SNP number SNP number Hetloci ratio / % Integrity ratio / %
SZ1 2010144 1101454 10.65 54.79
SZ2 2010144 1108987 10.99 55.16
SZ3 2010144 1124233 11.03 55.92
SZ4 2010144 1225287 11.18 60.95
SZ5 2010144 1199917 11.03 59.69
SZ6 2010144 1231716 11.21 61.27
SZ7 2010144 1205136 10.93 59.95
SZ8 2010144 1185877 11.65 58.99
SZ9 2010144 1206178 11.23 60.00
SZ10 2010144 1192230 10.94 59.31
SZ11 2010144 1283751 12.85 63.86
SZ12 2010144 1249074 11.64 62.13
SZ13 2010144 1222192 10.91 60.80
SZ14 2010144 1061836 9.44 52.82
SZ15 2010144 1102628 9.75 54.85
SY1 2010144 1038006 9.95 51.63
SY2 2010144 1039742 9.99 51.72
SY3 2010144 991652 9.56 49.33
SY4 2010144 1027875 9.87 51.13
SY5 2010144 1040267 10.05 51.75
SY6 2010144 1122866 10.85 55.85
SY7 2010144 1097668 11.22 54.60
SY8 2010144 1126900 10.79 56.06
SY9 2010144 1164083 11.26 57.91
SY10 2010144 1138116 10.73 56.61
SY11 2010144 1130375 10.70 56.23
SY12 2010144 1182425 11.26 58.82
SY13 2010144 1154786 11.52 57.44
SY14 2010144 1155444 11.03 57.48
SY15 2010144 1223114 12.02 60.84
XS1 2010144 1149640 11.35 57.19
XS2 2010144 1300295 13.19 64.68
XS3 2010144 1307590 13.18 65.04
XS4 2010144 1295832 13.14 64.46
XS5 2010144 1310729 13.41 65.20
XS6 2010144 1269962 12.86 63.17
XS7 2010144 1197594 11.65 59.57
XS8 2010144 1268115 12.84 63.08
XS9 2010144 1267870 12.57 63.07
XS10 2010144 1235461 12.47 61.46
XS11 2010144 1313024 13.94 65.31
XS12 2010144 1287328 13.38 64.04
XS13 2010144 1280442 13.48 63.69
XS14 2010144 1253266 12.95 62.34
XS15 2010144 1293659 13.75 64.35

Note: Sample ID: Sample number; Total SNP: total SNP detected; SNP num: number of SNP detected in corresponding samples; Integrity: SNP integrity detected in samples; Heter ratio: heterozygosity of SNP detected in samples

Tab. A4 Significant differences population SNPs Statistics of three populations
Marker Pos p-value (SY : XS) p-value (SY : SZ) p-value (XS : SZ)
Marker3275 8 5.97×10-4 3.32×10-5 2.09×10-12
Marker5368 96 6.42×10-5 5.24×10-16 5.83×10-6
Marker16106 153 2.51×10-4 8.04×10-5 8.21×10-13
Marker29414 71 8.05×10-5 8.05×10-5 1.52×10-14
Marker31028 126 1.30×10-4 8.24×10-4 7.82×10-12
Marker32764 118 5.54×10-13 1.10×10-5 9.93×10-4
Marker35459 119 1.72×10-10 3.60×10-4 5.97×10-4
Marker39352 151 3.28×10-5 6.11×10-4 2.02×10-13
Marker40948 162 5.83×10-12 9.06×10-4 3.33×10-4
Marker43655 114 5.19×10-5 9.18×10-6 5.24×10-16
Marker43655 195 2.46×10-4 1.89×10-6 5.24×10-16
Marker48466 76 9.12×10-4 4.49×10-4 4.79×10-11
Marker54309 156 7.97×10-4 6.72×10-4 6.70×10-11
Marker56258 144 9.77×10-4 2.29×10-6 1.54×10-14
Marker56258 153 9.77×10-4 2.29×10-6 1.54×10-14
Marker56258 177 9.77×10-4 2.29×10-6 1.54×10-14
Marker56258 187 9.77×10-4 2.29×10-6 1.54×10-14
Marker56258 196 9.77×10-4 2.29×10-6 1.54×10-14
Marker57704 38 1.83×10-12 8.31×10-5 4.12×10-4
Marker57704 56 1.83×10-12 8.31×10-5 4.12×10-4
Marker58004 63 7.97×10-4 1.10×10-5 9.23×10-14
Marker58004 192 7.97×10-4 1.10×10-5 9.23×10-14
Marker58561 4 1.78×10-14 2.59×10-6 4.78×10-4
Marker58831 165 7.08×10-4 9.92×10-5 1.01×10-11
Marker58831 179 7.08×10-4 9.92×10-5 1.01×10-11
Marker59195 168 8.50×10-14 3.92×10-5 5.09×10-4
Marker61327 50 4.25×10-4 5.13×10-4 1.01×10-12
Marker62076 121 9.36×10-5 3.65×10-5 2.88×10-14
Marker62305 23 4.53×10-5 7.45×10-5 8.23×10-15
Marker64435 95 5.78×10-5 3.54×10-13 7.07×10-4
Marker64938 164 1.26×10-6 1.29×10-4 5.24×10-16
Marker64938 192 1.26×10-6 1.29×10-4 5.24×10-16
Marker65298 19 1.26×10-6 4.05×10-4 4.66×10-15
Marker72476 169 7.75×10-5 9.41×10-4 4.08×10-11
Marker74137 39 9.84×10-4 4.89×10-6 1.78×10-14
Marker74137 160 4.09×10-5 1.66×10-5 7.13×10-16
Marker74137 197 4.09×10-5 1.66×10-5 7.13×10-16
Marker77452 27 3.34×10-4 1.62×10-4 9.40×10-12
Marker77571 174 7.37×10-4 4.81×10-7 2.00×10-15
Marker78614 80 9.63×10-4 9.43×10-5 5.49×10-12
Marker79010 46 5.56×10-6 5.96×10-4 1.13×10-14
Marker79010 96 5.56×10-6 5.96×10-4 1.13×10-14
Marker80829 28 4.18×10-5 6.72×10-4 9.86×10-13
Marker85121 127 2.97×10-4 1.04×10-4 2.02×10-13
Marker86368 91 3.80×10-4 1.03×10-9 9.11×10-4
Marker89723 73 2.82×10-5 4.83×10-4 3.55×10-13
Marker89828 32 7.08×10-4 8.14×10-4 2.12×10-9
Marker91169 142 9.10×10-4 1.61×10-4 9.61×10-12
Marker91169 143 9.10×10-4 3.97×10-4 2.55×10-11
Marker91169 193 9.10×10-4 1.61×10-4 9.61×10-12
Marker93033 35 5.09×10-4 3.39×10-4 5.69×10-12
Marker94025 137 8.55×10-4 4.15×10-4 3.54×10-13
Marker96746 95 3.15×10-4 6.99×10-4 5.37×10-11
Marker97131 48 9.89×10-5 9.28×10-6 4.03×10-15
Marker97131 64 9.89×10-5 1.66×10-5 8.23×10-15
Marker97651 49 3.07×10-4 2.13×10-4 1.38×10-11
Marker99962 152 6.07×10-5 4.60×10-4 2.87×10-12
Marker101446 75 2.31×10-4 4.98×10-4 6.74×10-11
Marker102130 123 2.82×10-5 6.46×10-4 2.32×10-13
Marker103561 24 5.74×10-4 7.97×10-4 3.29×10-11
Marker106244 7 3.73×10-4 1.53×10-4 1.18×10-13
Marker114322 156 2.85×10-5 8.53×10-4 2.44×10-12
Marker114487 160 9.77×10-4 2.06×10-4 6.96×10-11
Marker115710 9 3.72×10-4 7.06×10-5 4.07×10-13
Marker115710 37 3.72×10-4 4.05×10-4 9.61×10-12
Marker116654 73 1.91×10-4 8.85×10-5 2.22×10-13
Marker120806 120 3.64×10-4 7.18×10-10 4.58×10-4
Marker128452 23 4.11×10-13 2.60×10-4 8.79×10-6
Marker130399 64 2.87×10-12 9.76×10-4 2.59×10-5
Marker131746 129 4.78×10-4 6.66×10-4 9.09×10-11
Marker134085 78 1.05×10-11 4.50×10-4 2.22×10-4
Marker135504 57 2.29×10-7 7.97×10-4 5.24×10-16
Marker136107 156 8.50×10-14 6.40×10-4 6.95×10-6
Marker142198 82 4.78×10-4 3.68×10-4 1.48×10-11
Marker146079 23 7.48×10-4 8.24×10-5 1.44×10-12
Marker146079 58 7.48×10-4 8.24×10-5 1.44×10-12
Marker149477 131 8.47×10-4 5.31×10-4 1.62×10-10
Marker153118 160 7.93×10-4 8.65×10-4 6.32×10-10
Marker157365 21 9.10×10-4 1.45×10-4 1.62×10-12
Marker164040 135 7.36×10-4 5.21×10-4 2.70×10-10
Marker165818 206 2.35×10-15 7.26×10-5 1.67×10-4
Marker169492 68 3.34×10-4 1.39×10-4 1.25×10-12
Marker170499 42 2.35×10-4 3.12×10-4 9.23×10-14
Marker170499 61 2.35×10-4 3.12×10-4 9.23×10-14
Marker170499 70 2.35×10-4 3.12×10-4 9.23×10-14
Marker170499 85 2.35×10-4 3.12×10-4 9.23×10-14
Marker173073 161 5.88×10-4 3.19×10-8 3.44×10-17
Marker174886 165 8.39×10-15 3.44×10-6 4.12×10-4
Marker174886 174 8.39×10-15 3.44×10-6 4.12×10-4
Marker176168 67 5.49×10-12 2.51×10-4 3.74×10-4
Marker177413 57 6.14×10-4 1.15×10-4 3.74×10-12
Marker181332 62 1.75×10-4 6.40×10-4 2.02×10-11
Marker181637 22 1.98×10-4 2.38×10-5 2.83×10-12
Marker181637 23 1.98×10-4 2.38×10-5 2.83×10-12
Marker181637 33 4.98×10-4 1.24×10-5 2.83×10-12
Marker181637 36 1.98×10-4 2.38×10-5 2.83×10-12
Marker182931 24 4.13×10-5 2.31×10-4 5.69×10-14
Marker182931 36 4.13×10-5 2.31×10-4 5.69×10-14
Marker182931 38 4.99×10-4 2.31×10-4 1.44×10-12
Marker182931 114 4.99×10-4 4.90×10-4 7.37×10-12
Marker182931 171 4.99×10-4 2.31×10-4 1.44×10-12
Marker184767 188 1.95×10-12 3.64×10-4 2.07×10-5
Marker185952 36 3.34×10-4 2.43×10-4 1.89×10-11
Marker186713 35 1.46×10-6 3.41×10-5 3.44×10-17
Marker190981 52 4.12×10-4 8.23×10-5 7.84×10-13
Marker192459 199 7.33×10-4 3.39×10-4 1.88×10-11
Marker195736 85 1.72×10-4 7.11×10-4 1.59×10-11
Marker195973 95 7.07×10-4 5.24×10-16 4.75×10-7
Marker197784 195 8.47×10-4 5.80×10-4 3.01×10-11
Marker202019 15 5.21×10-4 1.19×10-4 8.82×10-12
Marker202019 84 5.21×10-4 1.19×10-4 8.82×10-12
Marker203633 31 2.19×10-11 7.35×10-4 5.69×10-4
Marker204262 11 4.11×10-5 7.37×10-4 1.89×10-11
Marker204262 76 6.80×10-4 7.37×10-4 5.49×10-10
Marker204262 145 4.11×10-5 7.37×10-4 1.89×10-11
Marker204262 159 4.11×10-5 7.37×10-4 1.89×10-11
Marker204262 162 4.11×10-5 7.37×10-4 1.89×10-11
Marker204262 169 4.11×10-5 7.37×10-4 1.89×10-11
Marker204894 204 4.99×10-4 4.90×10-4 7.37×10-12
Marker215169 49 2.59×10-5 9.10×10-4 8.12×10-13
Marker217857 37 7.82×10-4 5.13×10-4 6.01×10-12
Marker217857 81 7.82×10-4 5.13×10-4 6.01×10-12
Marker219928 157 1.15×10-13 6.49×10-4 5.18×10-6
Marker220971 29 8.47×10-4 2.38×10-4 7.37×10-12
Marker227957 59 3.55×10-4 2.89×10-6 1.13×10-14
Marker240685 145 3.25×10-4 1.52×10-4 2.22×10-13
Marker253092 204 9.93×10-4 1.10×10-5 5.54×10-13
Marker253901 155 5.53×10-4 3.77×10-4 4.50×10-11
Marker268253 137 1.07×10-5 3.17×10-12 5.97×10-4
Marker273125 41 8.30×10-4 6.05×10-4 3.69×10-10
Marker273125 51 8.30×10-4 6.05×10-4 3.69×10-10
Marker273125 59 8.30×10-4 6.05×10-4 3.69×10-10
Marker273125 67 8.30×10-4 6.05×10-4 3.69×10-10
Marker273125 147 8.30×10-4 6.05×10-4 3.69×10-10
Marker273125 187 8.30×10-4 6.05×10-4 3.69×10-10
Marker280356 52 8.65×10-4 8.85×10-5 1.19×10-11
Marker281848 160 2.56×10-4 6.58×10-4 1.38×10-11
Marker289553 74 9.36×10-5 2.07×10-5 3.65×10-14
Marker289553 128 9.36×10-5 2.07×10-5 3.65×10-14
Marker296021 144 4.18×10-5 2.99×10-4 2.35×10-15
Marker298184 70 3.93×10-6 2.61×10-5 3.44×10-17
Marker308966 59 8.21×10-13 2.49×10-4 1.24×10-4
Marker330769 199 1.58×10-4 3.40×10-4 2.02×10-11
Marker350607 45 2.63×10-10 1.48×10-4 4.58×10-4
Marker350607 49 2.63×10-10 1.48×10-4 4.58×10-4
Marker350607 71 2.63×10-10 1.48×10-4 4.58×10-4
Marker350607 73 2.63×10-10 1.48×10-4 4.58×10-4
Marker350607 74 2.63×10-10 1.48×10-4 4.58×10-4
Marker350607 82 2.63×10-10 1.48×10-4 4.58×10-4
Marker350607 141 2.63×10-10 1.48×10-4 4.58×10-4
Marker350607 163 2.63×10-10 1.48×10-4 4.58×10-4
Marker350607 181 2.63×10-10 1.48×10-4 4.58×10-4
Marker362138 51 1.48×10-4 1.38×10-4 8.82×10-12
Marker389768 132 3.05×10-4 7.36×10-4 6.74×10-11
Marker475043 205 1.26×10-5 2.37×10-4 8.23×10-15

2.4 Genetic Evolution Analysis

A total of 846502 highly consistent population SNPs were genotyped. These genotype data were used to analyze genetic evolution for the three populations, revealing genetic relationships between different populations and differentiation at the genomic level.

2.5 Phylogenetic Tree Construction and PCA

The 846502 SNPs were used to construct a phylogenetic tree of the three populations. The clustering analysis clustered all 45 individuals into three populations, and the individuals in each population clustered into a single group. In addition, the phylogenetic tree indicate that SY and SZ populations were most closely related, while XS populations clustered into a separate clade (Fig. 2).
Fig. 2 Phylogenetic tree of individuals in three populations of Tectus pyramis. The tree was constructed using the neighbour-joining approach with 1000 bootstrap replicates. Each branch in the graph represents an individual
Moreover, the PCA of the SNPs from individuals of the three populations showed that the XS population generally converged, the distance between individuals within the group was small, and the similarity was high (Fig. 3, Table A5). The distributions of individual PCA maps for SZ and SY populations were diffuse, and there was no obvious clustering evidence. PCA clustering showed that accessions for individuals were distributed into three clusters. However, all individuals were distributed in a single plane, which indicates minimal differences in genetic differentiation among the three populations. There were no obvious crossovers in the three groups of individuals, and there were differences among the groups (Fig. 3, Table A5).
Fig. 3 PCA cluster analysis of 45 Tectus pyramis individuals. Samples are clustered into three dimensions by PCA analysis. PCA1 ~ PCA3 represents the first, second, and third principal components, respectively
Tab. A5 PCA coefficients of individuals
Sample ID PCA1 PCA2 PCA3
SZ1 0.28 -0.09 0.24
SZ2 0.29 -0.08 0.22
SZ3 0.27 -0.10 0.21
SZ4 0.10 -0.29 0.02
SZ5 0.05 -0.28 -0.07
SZ6 0.11 -0.30 0.03
SZ7 0.13 -0.27 0.06
SZ8 0.15 -0.27 0.07
SZ9 0.12 -0.28 0.04
SZ10 0.09 -0.29 -0.02
SZ11 0.06 -0.33 -0.06
SZ12 0.10 -0.30 -0.01
SZ13 0.11 -0.14 -0.06
SZ14 0.20 0.00 0.11
SZ15 0.19 -0.04 0.06
SY1 0.26 0.24 0.04
SY2 0.26 0.23 0.02
SY3 0.28 0.27 0.08
SY4 0.28 0.25 0.05
SY5 0.26 0.22 -0.03
SY6 0.21 0.15 -0.13
SY7 0.19 0.16 -0.16
SY8 0.20 0.14 -0.17
SY9 0.22 0.10 -0.10
SY10 0.22 0.13 -0.08
SY11 0.21 0.14 -0.13
SY12 0.19 0.08 -0.18
SY13 0.21 0.11 -0.11
SY14 0.18 0.12 -0.20
SY15 -0.17 -0.10 -0.50
XS1 -0.03 0.25 0.41
XS2 -0.36 0.03 0.07
XS3 -0.36 0.03 0.07
XS4 -0.36 0.04 0.05
XS5 -0.36 0.03 0.04
XS6 -0.35 0.05 0.07
XS7 -0.33 0.10 0.10
XS8 -0.34 0.05 0.08
XS9 -0.33 0.06 0.10
XS10 -0.36 0.07 0.03
XS11 -0.42 0.01 -0.05
XS12 -0.41 0.02 -0.04
XS13 -0.42 0.02 -0.05
XS14 -0.41 0.04 -0.05
XS15 -0.43 0.02 -0.07

Note: Sample ID: Sample number; PCA1: the first principal component; PCA2: the second principal component; PCA3: the third principal component

2.6 Selective Sweep Analysis

Selective sweep analysis of the evolutions of populations is simply the elimination of polymorphisms in a region of the genome due to selection, and it reflects positive selection within a species genome (Li et al, 2013). A total of 846502 polymorphic SNP markers related to genetic differentiation among populations were selected, including 679983 in the SY population, 684052 in the XS population, and 653204 in the SZ population, and these SNP markers were used to calculate genetic indicators for each population.

2.7 Genetic Diversity and Genetic Structure Analyses

The group structure of the 45 individuals was assessed by using Admixture software, and subgroups were defined based on minimum cross validation (CV) errors as the number of subgroups (K value). Clustering of K values from 1 to 10 based on CV error rate is shown in Figs. 4a and 4b. The results of population structure analysis indicate they shared an ancestral subgroup (K = 1), and genetic differences among the 45 individuals were relatively small; but even when K = 3, the 45 individuals can still be completely separated.
Fig. 4 Admixture individual cluster values corresponding to each K value (a), and admixture validation error rate corresponding to different K values (b). Different color in each line represents different clusters in Fig. 4a. Each little circle shows cross-validation errors of each K values in Fig. 4b
Average genetic diversity values for T. pyramis populations are presented in Table 2. The number of alleles per locus was between 1.7719 and 1.8095, and the number of effective alleles in the three populations ranged from 1.4254 to 1.4511. The Shannon information index (I) of the XS population was the highest (0.4081), indicating higher diversity. The average MAF values were 0.2414, 0.2431 and 0.2437 for SZ, SY and XS populations, respectively. Heterozygosity is an important parameter for measuring the level of population variability, and PIC can reflect the level of genetic variation. The average He values were 0.2537 (SZ), 0.2666 (SY) and 0.2695 (XS); hence, the SZ population had the lowest value. The average Ho values were 0.1545 (SZ), 0.1441 (SY) and 0.1611 (XS); hence, the SY population had the lowest value. In addition, the average PIC values were 0.2048, 0.2152 and 0.2176 for SZ, SY and XS populations, respectively, indicating low polymorphism (PIC < 0.25).
Tab. 2 Genetic diversity of Tectus pyramis among the three populations
Parameter SZ population SY population XS population
Na 1.7719 1.8038 1.8095
Ne 1.4254 1.4463 1.4511
I 0.3847 0.4040 0.4081
Ho 0.1545 0.1441 0.1611
He 0.2537 0.2666 0.2695
PIC 0.2048 0.2152 0.2176
MAF 0.2414 0.2431 0.2437

Note: The average value of genetic diversity for T. pyramis is given. Na, number of observed alleles; Ne, effective number of alleles; I, Shannon information index; Ho, observed heterozygosity; He, expected heterozygosity; PIC, polymorphism information content; MAF, minor allele frequency

Genetic identity between SZ and SY populations was the highest (0.7780), and their genetic distance was the lowest (0.2510). In contrast, the genetic identity between SZ and XS populations was the lowest (0.7189), and the genetic distance was the highest (0.3300; Table 3). Fst ranged from 0.0818 to 0.1868, which implies the modest genetic differentiation. The total inter-population genetic variation was between 8.18 % and 18.68 %, and the intra-population variation was between 81.32 % and 91.82 %. Furthermore, Nm values between the three populations were > 1, indicating that gene flow between the three populations was significant. The Nm value for SZ and SY was 2.8070, indicating frequent gene flow between these two populations. Low Nm values were observed between XS and the other two populations, with the smallest Nm value being 1.0882 between XS and SZ (Table 3).
Tab. 3 Genetic structural parameters for the three populations of Tectus pyramis
Population Nm and Fst D and S
SY XS SZ SY XS SZ
SY 1.3914 2.8070 0.7240 0.7780
XS 0.1523 1.0882 0.3230 0.7189
SZ 0.0818 0.1868 0.2510 0.3300

Note: For Nm and Fst, data above the diagonal are pairwise Nm values, and data below the diagonal are Fst values. Nm (gene flow, estimated from Fst) = 0.25 (1 - Fst) / Fst. Fst, reflecting inbreeding among subpopulations relative to the total population. For D and S, data above the diagonal are the genetic similarity coefficient (S), and data below the diagonal are genetic distance (D). “—” indicates comparison within the same population

3 Discussion

3.1 Data Quality Control and Mass SNP Marker Development

SLAF-seq was recently developed on the basis of high-throughput sequencing to study genetic variation of species (Chen et al, 2013; Zhang et al, 2016). In the present study, we used 45 samples from three populations collected from different regions off South China for analyzing the genome-wide distributions of polymorphism SLAF tags and genotyping. We constructed a SLAF library with a high digestion efficiency of 90.70 % (The Arabidopsis Genome Initiative, 2016) and an average Q30 value of 94.07 %. The single-base error rate is usually evaluated in high-throughput sequencing using the sequencing quality value Q; and the higher the value, the lower the base sequencing error rate (Kwong et al, 2015), demonstrating that the SLAF library was constructed successfully in this work. A total of 118679 polymorphism SLAF markers with a polymorphism rate of 54.33 % were identified alongside 2010144 population SNPs. The presence of some erroneous and missing values in SLAF-seq is relatively normal (Zhang et al, 2013), and 846502 highly consistent population SNPs were considered effective markers for genetic analysis, averaging 16.9 SNPs per kb of T. pyramis genome (115.74 Mb). Moreover, 1268720 SNPs were developed for the XS population, with the highest heterozygosity (12.94 %), significantly higher than the other two populations (p < 0.05). Subsequently, the average depth per tag was found to be 337.87-fold, and the average depth per sample was 7.93- to 22.14-fold, demonstrating high accuracy for genotyping and genetic data analysis (Han et al, 2016). Previous studies confirmed that a sequencing depth of 5-fold is sufficient for accurate genetic analysis (He et al, 2011; Li et al, 2013).
SLAF-seq technology has been widely used for SNP development, genetic diversity assessment, germplasm resource identification, and genome-wide genetic structure analysis (Zhang et al, 2015; Zhu et al, 2016). Sun et al (2013) first tested the efficiency of rice and soybean data using SLAF-seq technology, and demonstrated strong consistency and high accuracy for genotyping, culminating in a high-density genetic map for carp without a reference genome sequence, based on 50530 high-quality SLAFs and 13291 SNPs genotyped. Meanwhile, Li et al (2013) analyzed genetic differences among Landrace, Erhualian and Meishan pigs using 165670 SNPs from 453.75 million reads, and revealed distinct genetic relationships among three different populations, as well as 268 differentially selected regions containing 855 genes for studying the mechanism of formation of genetic differences between different geographical origins. In summary, SLAF-seq technology is a highly efficient and powerful method for genetic analysis, and the results of sequencing in this study are suitable for subsequent analysis.

3.2 Genetic Diversity of the Three Populations

Population genetic diversity, genetic structure and genetic relationships have not yet been reported for T. pyramis. Long-term selection requires genetic variability; hence, both population structure and genetic diversity should be examined (Hamblin et al, 2007). Through a genome-wide SNP screening and genotyping analysis, 846502 polymorphisms and highly consistent population SNPs were obtained by population genetic diversity analysis. Based on population SNPs, the average Ho and He values of the three populations ranged from 0.1441 to 0.1611 and from 0.2537 to 0.2695, respectively. The average PIC values of populations ranged from 0.1923 to 0.2272, with all PIC values < 0.50, indicating that polymorphisms in the three populations contained low or moderate genetic information, consistent with a narrow genetic background. The ability to detect genetic effects depends on the minor allele frequency (MAF), and understanding this parameter is essential in genomic research (Tabangin et al, 2009). There were no significant differences in MAF among populations using the Friedman test (p < 0.05), for which the SZ population yielded the lowest value (0.2414), and the XS population was the highest (0.2437). Regarding SNP loci diversity, polymorphism information and population genetic diversity, there was higher genetic diversity and population polymorphism in the XS population, but differences among the three populations were not significant, indicating the potential for confusion of the three populations.
In another shellfish Pinctada fucata martensii, Wu et al (2013a) found that the average observed heterozygosity was 0.5718 to 0.7366, the average expected heterozygosity was 0.5830 to 0.6954, and all PIC values were greater than 0.50, indicating that the natural population was highly polymorphic, much more so than the observed in our current data. The low to moderate genetic polymorphism of T. pyramis in the present study could be due to the face that SNP markers were mainly composed of two alleles, representing relatively low polymorphism compared with other molecular markers (Kong et al, 2014); it may also be due to a low number of parents (Durand et al, 1993). In addition, autogamy, small population size, restricted distribution range, and geographical isolation may also contribute to low genetic diversity (Gong et al, 2010). T. pyramis is distributed in the South China Sea and lives on reefs and coral reefs. Due to this narrow distribution, its population is greatly affected by both marine environment and climate, resulting in low genetic diversity. Furthermore, with periodic cyclical climate changes, there are strong regime shifts in the world’s oceans, which has a fundamental influence on genetic diversity. The low polymorphisms observed in the present study may be due to population expansion and accumulation of mutations after bottleneck effect, which indicates that these three populations have experienced population expansion (Grant et al, 1998).

3.3 Genetic Structure of the Three Populations

The genetic structure of T. pyramis was accurately estimated using 846502 SNPs, and 155 SNPs exhibiting significant population differences between genomes were identified. In this study, the phylogenetic tree and PCA clustering of the three populations were consistent. The results of the three-dimensional cluster analysis of the PCA data showed that the three populations could be divided into three categories, and the classification boundaries of the three populations were obvious. However, all three populations were distributed on the same plane, which indicates that differentiation in the three populations was not significant. Using both phylogenetic tree and PCA analyses, the 45 individuals were clustered and divided into different groups, and individuals in each group could be clustered into a single group. The SZ and SY populations displayed the closest relationship, while the XS population clustered into a separate group. However, when CV errors were the lowest, the 45 individuals were classified into a single group by admixture software analysis. In general, there was a single germplasm origin and the genetic background was narrow, consistent with the results of genetic diversity analysis. In total, we obtained 155 SNPs that could be used to discriminate populations and avoid germplasm contamination, which lays a foundation for the protection of genetic diversity and the management of breeding parents (Wu et al, 2013b; Xiong et al, 2019). In particular, these findings provide a reference for the classification of T. pyramis.
Fst and genetic distance are important indicators of genetic differentiation among different populations (Weir et al, 1984). The SZ and SY populations shared the most similar genetic structure based on both the lowest genetic distance (0.2510) and the lowest Fst value (0.0818), indicating that genetic variation mostly occurred among populations (Fst > 0.05). According to Weir et al (1984), this result reflects moderate genetic differentiation (Fst < 0.15). Additionally, there was higher inter-population genetic differentiation between the XS and the other two populations (Fst > 0.15), and the lower genetic similarity coefficient also supported this result. Moreover, gene flow values (Nm) for the SZ and SY populations were > 2, indicating that gene exchange prevented genetic differentiation among populations caused by genetic drift (Wright, 1931). The genetic distances between the XS and the other populations were extremely high, especially between the XS and SZ populations (0.3300), and gene flow (Nm) between the XS and SZ populations was the lowest (1.0882).
The extent of gene flow among benthic marine invertebrate populations depends on a series of factors, including the duration of planktonic larval stage, the number of effective net water transport larvae, geographical isolation, and suitable habitat (Bertness et al, 1993). The geographical location of the three populations in this work was significant. Correlation analysis revealed no significant correlation between geographical distance and genetic distance [R2 = 0.2647, p-value (0.6559) > 0.05], which indicates that geographical environment and ocean currents, but not geographical isolation, played important roles in the complexity and diversity of genetic structure in the population, consistent with the transfer of germplasm resources between accelerated regions (Jarret et al, 1994; Zhang et al, 2000). Therefore, there may be genetic differentiation among populations of T. pyramis in different regions, indicating resistance caused by isolation following a post-glacial sea level rise. Separation migration has a distinct barrier effect, and the natural geography can produce this isolation effect (Wang et al, 2016a).
According to a simulated map of the South China Sea (Gan et al, 2016), surface currents in the South China Sea along the continental slope and continental shelf in winter and summer are circulated from the north to Shenzhen and Hainan along the Xisha Islands. The most important ocean currents flowing into the South China Sea include the Taiwan warm current and Kuroshio current (Chen et al, 1996). T. pyramis mainly lives in shallow sea areas such as reefs and coral reefs, and oceanic currents are used for dispersal during the planktonic larval stage. The results of genetic structure analysis showed that genetic differentiation was the lowest between SZ and SY populations, suggesting that the SZ population might have migrated to SY along the Taiwan Current. Surrounding the Xisha Islands, there are deep upwelling that may block gene flow between XS and SZ populations. Therefore, genetic differentiation between these populations was the largest, and gene flow was the lowest.
Chen et al (2015) reported that it took seven days for T. pyramis to enter the metamorphic stage from the development of flour disk larvae. However, the planktonic larval stage of T. pyramis did not exceed 24 h, and there were significant differences between individuals, which is unpublished studies in our laboratory. Therefore, relying on slow ocean current movements, larvae may migrate from planktonic to attachment stages over a relatively short distance, especially in the Xisha area, which is a deep part in the South China Sea and far from the mainland. T. pyramis populations are mainly distributed around the islands, and the SZ population cannot reach the Xisha islands or the Sanya area. Thus, the three populations cannot be regarded as the same population based on genetic distance and differentiation, consistent with differentiation and gene flow data.
Geological changes have occurred in the South China Sea. The sea level has changed dramatically during the glacial period; global sea levels have decreased (Lambeck et al, 2002), and the sea level in the South China Sea has increased significantly (Woodruf, 2003). Because T. pyramis population lives in shallow sea areas, the original population may have migrated along with the rise in sea level. Part of this migration appearred to have involved expansion in the Shenzhen area, as well as the Sanya area, while other populations may have expanded into the Xisha region, explaining the observed group structures. Combining the life history of larval plankton and the movements of currents in the South China Sea, the population appears to have differentiated when adapting to the environment around the Xisha Islands because it differs considerably from that of the other areas (Zhang et al, 2014). This may also explain large differences in genetic structure of the XS population. In summary, genetic differentiation of the three populations may be related to ocean currents, geographical location, and characteristics of the planktonic larval stage of T. pyramis.
In summary, we developed 2010144 SNPs, including 846502 highly consistent population SNPs and 155 with significant differential population structures, using SLAF-seq analysis of 45 individuals from three T. pyramis populations. Although all individuals in the three populations could be grouped into a single group, phylogenetic tree construction and PCA analysis revealed that the three natural populations displayed a certain degree of genetic differentiation with low genetic diversity. Genetic structure differences between the SZ and SY populations were the lowest based on genetic distance and gene flow. However, genetic differentiation between XS and the other two populations was obvious. This study provides useful information for germplasm resource management and future breeding strategies.
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