Analysis of Genetic Diversity and Differentiation of Seven Stocks of Litopenaeus vannamei Using Microsatellite Markers

ZHANG Kai, WANG Weiji, LI Weiya,, ZHANG Quanqi, and KONG Jie,

1) College of Marine Life Sciences, Key Laboratory of Marine Genetics and Breeding (Ministry of Education), Ocean University of China, Qingdao 266003, P. R. China

2) Key Laboratory for Sustainable Utilization of Marine Fisheries Resources (Ministry of Agriculture), Yellow Sea Fisheries Research Institute, Chinese Academy of Fishery Sciences, Qingdao 266071, P. R. China

3) Shandong Marine Fishing and Production Management Station, Yantai 264000, P. R. China

Analysis of Genetic Diversity and Differentiation of Seven Stocks of Litopenaeus vannamei Using Microsatellite Markers

ZHANG Kai1), WANG Weiji2), LI Weiya2),3), ZHANG Quanqi1), and KONG Jie2),*

1) College of Marine Life Sciences, Key Laboratory of Marine Genetics and Breeding (Ministry of Education), Ocean University of China, Qingdao 266003, P. R. China

2) Key Laboratory for Sustainable Utilization of Marine Fisheries Resources (Ministry of Agriculture), Yellow Sea Fisheries Research Institute, Chinese Academy of Fishery Sciences, Qingdao 266071, P. R. China

3) Shandong Marine Fishing and Production Management Station, Yantai 264000, P. R. China

Seven microsatellite markers were used to evaluate the genetic diversity and differentiation of seven stocks ofLitopenaeus vannamei, which were introduced from Central and South America to China. All seven microsatellite loci were polymorphic, with polymorphism information content (PIC) values ranging from 0.593 to 0.952. Totally 92 alleles were identified, and the number of alleles (Na) and effective alleles (Ne) varied between 4 and 21 and 2.7 and 14.6, respectively. Observed heterozygosity (Ho) values were lower than the expected heterozygosity (He) values (0.526–0.754), which indicated that the seven stocks possessed a rich genetic diversity. Thirty-seven tests were detected for reasonable significant deviation from Hardy-Weinberg equilibrium.Fisvalues were positive at five loci, suggesting that there was a relatively high degree of inbreeding within stocks. PairwiseFstvalues ranged from 0.0225 to 0.151, and most of the stock pairs were moderately differentiated. Genetic distance and cluster analysis using UPGMA revealed a close genetic relationship ofL. vannameibetween Pop2 and Pop3. AMOVA indicated that the genetic variation among stocks (11.3%) was much lower than that within stocks (88.7%). Although the seven stocks had a certain degree of genetic differentiation and a rich genetic diversity, there is an increasing risk of decreased performance due to inbreeding in subsequent generations.

Litopenaeus vannamei; microsatellite; introduced stock; genetic diversity; genetic differentiation

1 Introduction

Litopenaeus vannamei, also known as Pacific white shrimp, is an important aquatic species that is naturally distributed along the Pacific coast of Central and South America between the 20℃ isotherms (Holthuis, 1980). As an economically valuable marine crustacean in aquaculture, numerous selective breeding programs have been initiated in the past decade. Shrimp farming is a pillar industry of marine aquaculture in China, andL. vannameiis one of the major commercial shrimp species. A large number of broodstocks are required to set up basic families in selective breeding programs. However, the small number of foreign imports does not meet the current demand. Compared with the shrimp imported, inbreeding would be more obvious if the cultured shrimp were used as broodstocks, causing inbreeding depression of some important traits. Therefore, more stocks from different companies must be introduced to expand the number of broodstocks. In such a scenario, it is necessary to conduct detailed genetic diversity and differentiation analyses to avoid inbreeding and other undesirable effects.

With characteristics of co-dominance, high reproducibility, polymorphism richness and an ubiquitous genome distribution (Songet al., 2011), microsatellite markers are suitable for genetic population structure and diversity analysis (Sunet al., 2001). Wardet al.(2006) selected six population samples from four locations of the Australian endemic brown tiger prawn,Penaeus esculentus,to assess the population genetic structure using eight microsatellite loci. Microsatellites have also been used to estimate the relatedness of terricolous and aquatic animals. In the Kuruma prawn (Penaeus japonicus), more than 47% of the offspring were accurately traced to their birth mothers using five microsatellite markers (Sugayaet al., 2002). The unambiguous parentage of the Japanese flounder (Paralichthys olivaceus) was determined using hypervariable microsatellite markers with many rare alleles (Hara and Sekino, 2003). In the Atlantic salmon (Salmo salar), eight highly variable microsatellite markers resulted in the correct assignment of 95.6% of the offspring to the correct parental pair, even when there were >12000 potential pairs (Norriset al., 2000). Some related applica-tions have been reported in the Chinese shrimp (Fenneropenaeus chinensis) (Donget al., 2006) and the black tiger shrimp (Penaeus monodon) (Dixonet al., 2008), among others.

Many in-depth investigations ofL. vannameiwith respect to farming techniques, nutrition and disease control (Perez-Velazquezet al., 2011; Tsenget al., 2009) have been conducted. However, few genetic diversity and genetic structure have been studied. Perez-Enriquezet al.(2009) evaluated the genetic composition and diversity of the broodstocks from six hatcheries in Northwestern Mexico and estimated their levels of inbreeding based on six microsatellite markers. Wanget al.(2006) evaluated the ability of six microsatellite markers to assign accurate parentage in Pacific white shrimp progeny. Houet al.(2011) assessed genetic structure within and among four geographical stocks of turbot (Scophthalmus maximus) using 12 microsatellite loci. Those four stocks have a relatively high genetic differentiation and diversity and can be used to initiate pedigree breeding populations. However, Perez-Enriquezet al.(2009) found that even if the introduced stocks had a better genetic diversity compared with a wild population, inbreeding depression was more likely to occur in subsequent generations. Therefore, it has become very important to utilize microsatellite markers to analyze the genetic diversity and inbreeding depression of introducedL. vannamei.

In the present study, seven stocks ofL. vannameithat have been introduced from Central and South America to China were analyzed using seven microsatellites markers. The genetic diversity and inbreeding levels were evaluated. The present results could provide a theoretical basis and technical support for large-scale pedigree breeding ofL. vannamei.

2 Materials and Methods

2.1 Sampled Stocks

The stocks ofL. vannameiwere sampled from seven different commercial companies. Specific information regarding the samples is presented in Table 1. The sample ID was constituted by the stock ID and the sampling sequence. For example, individual 5–51 represents individual 51 from stock 5. Samples were stored at -20℃ for further analysis.

Table 1 L. vannamei stocks

2.2 DNA Extraction and PCR Amplification

Total DNA was extracted from the muscle ofL. vannameiaccording to the method described by Wang (2008). Genomic DNA was dissolved in double-distilled water and the concentration was adjusted to 50 ng μL-1. The quality was detected by 8 g L-1agarose gel electrophoresis.

Seven microsatellite markers (Meehanet al., 2003) with clear and reproducible polymorphic fragments were selected (Table 2). Polymerase chain reaction (PCR) amplification was performed in a 25-μL reaction volume containing 100 ng of genomic DNA, 0.2 mmol L-1of each primer (forward primers were fluorescently labeled with HEX and 6-FAM), 2.0 mmol L-1Mg2+, 0.2 mmol L-1dNTPs, and 1 UTaqpolymerase (Fermentas, Canada). PCR was performed using an initial denaturation at 94 ℃for 5 min followed by 28 cycles of denaturation at 94 ℃for 40 s, annealing at locus-specific temperatures for 1 min, and extending at 72 for 1℃ min, and a final extension at 72℃ for 5 min. PCR products were evaluated using 20 g L-1agarose gel electrophoresis and then loaded into an Applied Biosystems 3130 sequencer and scored using GeneMapper version 3.7 software (Applied Biosystems) with GeneScan-500 LIZ Size Standard as an internal size standard.

Table 2 L. vannamei microsatellite primers?

2.3 Genetic Diversity and Differentiation Analyses

A set of intra- and inter-stock genetic statistics were generated using the program Cervus 3.0 (Marshallet al., 1998), including the number of loci, percentage of gene frequencies, observed number of alleles (Na), expected heterozygosity (He), observed heterozygosity (Ho), polymorphism information content (PIC) and Hardy-Weinberg Equilibrium (HWE). The effective number of alleles (Ne, Crow and Kimura, 1965) was evaluated using the following formula:

wherexiis the frequency of theith allele for each locus. POPGENE (Yehet al., 1999) was used to calculate Nei’s genetic distance (GD) (Nei, 1978) between stocks. To detect genetic differentiation among individuals in all seven stocks,F-statistic indices (Wright, 1978) were estimated using GENEPOP (Raymond and Rousset, 1995). To evaluate the overall change in genetic diversity of the seven introduced stocks, the number of alleles per locus and allele frequencies was compared. The distribution of genetic variation among and within stocks was analyzed by analysis of molecular variance (AMOVA) with Arlequin 3.1.1 software (Excoffieret al., 2005). Cluster analysis was performed to generate a dendrogram based on the unweighed pair group method with arithmetic averages (UPGMA) using MEGA 5.0 software (Tamuraet al., 2011).

3 Results

3.1 Genetic Diversity

The genetic diversity indices of the seven stocks were summarized in Table 3. For all stocks, 92 alleles were detected at all seven microsatellite loci. The number of alleles per locus ranged from 4 to 21, with an average of 11.7, and varied in size from 154 to 525 bp. TUMXLv-7.56 had the largest number of alleles (21) in Pop1 and Pop6; the maximum numbers of alleles at TUMXLv5.38, TUMXLv7.121, TUMXLv7.148, TUMXLv8.220, TUMXLv8.256 and TUMXLv9.43 were 9, 4, 9, 19, 8 and 15, respectively. The number of effective alleles per locus displayed a high degree of polymorphism.Newas lower thanNain all stocks. With respect to a single locus,Newas largest for the TUMXLv7.56 locus. The value ofPICper locus ranged from 0.593 to 0.952, with an average of 0.766, indicating a high level of polymorphism (PIC>0.5) that would be effective for further analyses. The average observed heterozygosity (Ho) values of the seven stocks were 0.599, 0.754, 0.741, 0.552, 0.651, 0.526 and 0.591, respectively. The expected heterozygosity (He) varied between 0.600 and 0.802 and was slightly greater thanHo. In the locus TUMXLv5.38, TUMXLv7.121 and TUMXLv8.256, the most common alleles were same in all stocks. Loci TUMXLv7.56 and TUMXLv8.220 showed the highest allele size variation, and several private alleles were present in each stock (Appendix A). According to the 49 tests for Hardy-Weinberg equilibrium, deviations (P<0.05) were observed for 37 tests (75.5%) due to the heterozygote deficiency. All of the populations departed from HWE at the TUMXLv7.148 locus. Both Pop1 and Pop5 departed from HWE at all of the seven microsatellite loci. None of the stocks displayed a departure from HWE (P>0.05) at each locus.

3.2 Genetic Differentiation

Genetic differentiation was detected for each locus by the fixation indicesFisandFst. The results of theF-statistic analysis were summarized in Table 4. Estimates ofFst(0.0498–0.1156), with a mean of 0.0834, indicated that there was moderate differentiation among the stocks. Calculation ofFisrevealed that 20 tests had heterozygote excess. However, for each of the stock, it displayed a small degree of heterozygosity deficiency. All of the multilocusFstvalues were significant atP<0.05, although some of the differences were small. According to Wright (1978),Fstvalues ranging from 0 to 0.05, from 0.05 to 0.15, and from 0.15 to 0.25 indicated a low, moderate and high level of genetic differentiation, respectively. The maximum value was detected between Pop2 and Pop7 (0.1510, the onlyFstindicating a large amount of genetic differentiation between stocks) and the minimum value between Pop1 and Pop6 (0.0225, indicating a low level of genetic differentiation between stocks) (Table 5). Four other values indicated a low level of genetic differentiation; the remaining value revealed moderate genetic differentiation. Similar toFst, most comparisons of allelic frequencies showed a genetic differentiation among stocks at most loci. The comparison between the seven stocks at TUMXLv7.148 showed moderate differences. All of the stocks 2 alleles (C and G) showed higher frequencies, while uneven distribution was observed in other alleles (Fig.1). Private alleles were observed in some stocks; however the frequency was always lower than 10%. The results of AMOVA (Table 6) showed that the differentiation was weak among the seven stocks.

3.3 Genetic Distance and Phylogenetic Reconstruction

To further analyze the degree of genetic differentiation among the various stocks, we calculated the genetic identity and the genetic distance (Table 7). The genetic distance ranged from 0.1044 to 0.5127, whereas the genetic identity ranged from 0.5989 to 0.9009. The highest genetic identity was found between Pop1 and Pop6, which indicated that Pop1 and Pop6 also had the lowest genetic distance. Cluster analysis performed using UPGMA divided the seven stocks into two groups (Fig.2). The first group included Pop2 and Pop3, while the second group included the remaining five stocks, indicating that there was a closer relationship between Pop2 and Pop3.

Table 3 Allelic variabilityaat seven microsatellite loci in L. vannamei

Table 4 F-statistics for 7 stocks of L. vannamei at seven microsatellite loci

Table 5values for pairwise comparison among different stocks of L. vannamei according to seven microsatellite loci

Table 5values for pairwise comparison among different stocks of L. vannamei according to seven microsatellite loci

Note:?Fstestimates of F-statistics (Hartl and Clark, 1989).

?

Table 6 Results of AMOVA for seven stocks of L. vannamei

Fig.1 Genetic composition of seven stocks at TUMXLv7.148 locus.

Table 7 Nei’s original measure of genetic identity (I) and genetic distance (GD) in the seven introduced stocks

Fig.2 UPGMA dendrogram of the seven introduced stocks. Values below the dendrogram indicate genetic distance.

4 Discussion

4.1 Genetic Diversity

In the present study, the number of alleles over the seven loci ranged from 4 to 21, which was higher than the number (2–13) reported by Valles-Jimenez (2005) and similar to that reported by Supungulet al.(2002). The results showed that the seven microsatellite loci can be used as genetic markers in genetic diversity and differentiation analysis. In all of the stocks assessed, TUMXLv7.56 had the most alleles (21 alleles), consistent withthe findings of Wanget al. (2006) (22 alleles).

The polymorphic information content (PIC) serves as an indicator of the degree of genetic variation. In the present study, the seven microsatellite loci showedPICvalues ranging from 0.593 to 0.952, indicating a high level of polymorphism (PIC>0.5). Zhanget al.(2010) obtainedPICvalues ranging from 0.5763 to 0.9994 forPenaeus (Fenneropenaeus) chinensisin studies examining genetic variability based on microsatellite markers. Ma (2011) calculated thePICvalue ofL. vannmeibased on eight microsatellite loci and reported values that varied between 0.4052 and 0.8693. The values reported by Zhanget al.(2010) and Maet al.(2011) were lower than ours, indicating that the seven pairs of microsatellite primers used in the present study were more effective for genetic diversity analyses.

The allele frequency analysis indicated a moderate degree of similarity among the seven stocks examined in this study. Although some allele frequencies were similar in all stocks, the private alleles which could be the indicators of specific stocks occurred at certain levels. These results suggested that the stocks might have a common origin before several generations of selective. Nevertheless, new alleles were introduced from wild populations during the breeding programs. Allele variation among the stocks of the seven loci in the present study showed the source of genetic differentiation. From the comparison of the number of alleles per locus and its frequencies, there has been rich genetic diversity among the seven stocks.

The average level of population heterozygosity reflects the degree of genetic consistency in a given population. Menget al.(2008) reported that the average observed heterozygosity for the prawnFenneropenaeus chinensisfrom seven geographic locations ranged from 0.638 to 0.713, which was lower than the average expected heterozygosity (0.810–0.864). Wanget al.(2006) used six microsatellite loci to evaluate the parentage of pacific white shrimp progeny. The obtained heterozygosity varied between 0.480 and 0.940. Those data were consistent with our results, which providedHeandHovalues that varied between 0.600 and 0.802 and 0.526 and 0.754. In the present study the heterozygosity of each stock was higher than 0.5, indicating a high level of genetic diversity. Pop3 and Pop5 had the higher heterozygosity among the stocks. The allele analysis showed similar result that the two stocks had more private alleles. The lower level of heterozygosity in Pop7 might be caused by the small sample size. However, the values ofHowere lower thanHein all seven stocks indicating heterozygote deficit. One possible reason is that genetic variation changed after several rounds of artificial selection. Consequently, it could be concluded that the seven stocks had a high heterozygosity within and among stocks.

4.2 Deviation from Hardy-Weinberg Equilibrium

In all of the 49 tests (seven stocks × seven loci), we detected 37 deviations from HWE. A similar trend was reported by Jimenezet al.(2005) (19 deviations from HWE in all 20 tests), Supungulet al.(2002) (19 deviations from HWE in all 25 tests) and Xuet al.(2001) (8 deviations from HEW in all 24 tests). Heterozygote excess typically appears in relatively small or closed populations. In contrast, heterozygous deletion may also be caused by degenerate and close relationships that result in the loss of rare alleles, in addition to null alleles and sample size (Antoroet al., 2005). In the present study, seven stocks with unknown pedigree information were obtained from different companies, potentially comprising multi-generation populations. Therefore, in addition to the stock capacity, other factors leading to the deviation from Hardy-Weinberg Equilibrium at most sites were genetic drift and a bottleneck effect, resulting in changes in allele frequency in multi-generational breeding. These deviations also indicated a certain degree of genetic information loss at the evaluated loci. Together with the polymorphic information content and other information, we suggest that although the seven stocks had a relatively high level of genetic diversity, there was also a certain degree of genetic loss.

4.3 Genetic Differentiation and Inbreeding

Genetic differentiation (Fst) is an important indicator of the degree of genetic differentiation among populations.Fstvalues range from 0 to 1, with larger numbers correlating to a greater degree of genetic differentiation among groups. Luanet al.(2006) determined anFstof 0.023 in a comparison of wild and cultured populations ofMarsupenaeus japonicus,and Limaet al.(2010) reported anFstof 0.0217 in twoL. vannameistocks obtained from hatcheries located in the state of Pernambuco (Northeast Brazil). However, Soto-Hernandez & Grihalava-Chon (2004) observed higher values (Fst=0.086) using allozymes in wild and cultivatedL. vannameipopulations, which is consistent with our data. According to the pairwiseFstanalysis, Pop1 and Pop6 had the lowest degree of genetic differentiation among the stocks, whereas Pop2 and Pop7 had the highest degree of genetic differentiation. However, the majority of theFstvalues indicated a moderate degree of genetic differentiation. AMOVA analysis revealed that 11.3% of the variation occurred among stocks and 88.7% among individuals within populations, consistent with the results of Houet al.(2011) and Limaet al.(2010).

AverageFisvalues for each stock were significantly different from zero, which indicated that the stocks might have undergone some inbreeding events. SignificantFisvalues have been reported for natural populations ofL. vannameiin Mexico (Fis= 0.53, Valles-Jimenezet al., 2005), and both wild and farmed populations (Fis= 0.63, Soto-Hernandez & Grihalava-Chon, 2004). In the present study, the inbreeding coefficiency ranged from 2.99% to 27.40%, which was far less than the above results. The differences inFisvalues observed among the seven introduced stocks (Table 5) could be attributed to a close relationship among the stocks.

Inbreeding depression has been documented in salmonids (Wanget al., 2002) and in some penaeids (Sbor-doniet al., 1987; Moss and Arce, 2004) with respect to traits such as hatching rate, larval survival, and growth rate. However, it has been difficult to determine which inbreeding level can be considered high. Nevertheless, Mosset al. (2007) recommended that the level of inbreeding should not exceed 10%. TheFisvalues observed in Pop2, Pop3 and Pop7 indicated a relatively low level of inbreeding. However, the small number of individuals in Pop2 and Pop7 could have affected the accuracy of these results. Stocks 1, 4, 5, and 6 demonstrated the levels of inbreeding between 16.32% and 27.40%, which might be considered relatively high. Consequently, there is an increased risk of decreased performance due to inbreeding depression in subsequent generations.

In summary, although the seven introduced stocks displayed a relatively rich level of genetic diversity, the presence of inbreeding was obvious.Fstvalues indicated that the seven stocks had a moderate level of genetic differentiation. The total genetic variation was mainly due to the individuals within rather than among stocks. A potential explanation for this phenomenon is thatL. vannameiand the broodstocks did not originate in our country but were imported from different companies or countries. The introduced stocks were broad of geographical distribution on the surface. However, a genetic background analysis of these stocks was lacking. The majority of the stocks with a relatively high inbreeding coefficient may have been selected for years before sales. Therefore, to ensure that ourL. vannameishrimp farming industry is healthy and sustainable, there is a need for control of seed germplasm to evaluate genetic backgrounds and avoid inbreeding depression. Together with scientific methods used for selection, new varieties of shrimp could be cultured that are healthy and showing good performance over different environments. Such strategies could ensure the sustainable utilization of domestic germplasm ofL. vannamei.

Acknowledgements

This work was supported by the National High Technology Development Project of China under contract No. 2012AA10A404 and the Recommended International Advanced Agricultural Science and Technology Project of China under contract No. 2012-S5.

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(Edited by Qiu Yantao)

Appendix A Allele frequencies at each microsatellite locus of Litopenaeus vannamei

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(Received November 8, 2012; revised December 29, 2012; accepted August 23, 2013)

? Ocean University of China, Science Press and Spring-Verlag Berlin Heidelberg 2014

* Corresponding author. Tel: 0086-532-85821650

E-mail: kongjie@ysfri.ac.cn