Genetic diversity of cultured, naturalized, and native pacific oysters, Crassostrea gigas, determined from multiplexed microsatellite markers.
Article Type: Report
Subject: Crassostrea (Genetic aspects)
Oysters (Genetic aspects)
Population genetics (Research)
Authors: Miller, Penny A.
Elliott, Nicholas G.
Koutoulis, Anthony
Kube, Peter D.
Vaillancourt, Rene E.
Pub Date: 08/01/2012
Publication: Name: Journal of Shellfish Research Publisher: National Shellfisheries Association, Inc. Audience: Academic Format: Magazine/Journal Subject: Biological sciences; Zoology and wildlife conservation Copyright: COPYRIGHT 2012 National Shellfisheries Association, Inc. ISSN: 0730-8000
Issue: Date: August, 2012 Source Volume: 31 Source Issue: 3
Topic: Event Code: 310 Science & research
Product: Product Code: 0913050 Oysters NAICS Code: 114112 Shellfish Fishing SIC Code: 0913 Shellfish
Geographic: Geographic Scope: Japan; South Korea Geographic Code: 9JAPA Japan; 9SOUT South Korea
Accession Number: 303011382
Full Text: ABSTRACT Ten polymorphic microsatellite loci were multiplexed to analyze a total of 343 Pacific oysters (Crassostrea gigas) sampled from native (Japan and Korea), naturalized (France and Australia), and cultured (3 Australian programs) populations. Genetic diversity was high within the native and naturalized populations (average allelic richness, 18.7; expected heterozygosity, 0.89), but lower within samples from hatchery populations (allelic richness, 12.3; expected heterozygosity, 0.84). A significant decrease in diversity was found within Australian cultured populations. However, diversity was shown to be similar in samples from a well-managed, family-based selective breeding population and commercial hatchery mass spawning populations. The Bayesian analysis of population structure found no difference between native and naturalized samples, which, together with other results, indicate that the naturalized populations have not changed genetically since their introduction. This suggests that naturalized populations can provide a good source of genetic diversity for breeding programs.

KEY WORDS: Pacific oyster, Crassostrea gigas, microsatellites, diversity, population genetics

INTRODUCTION

Native to Northeast Asia, the Pacific oyster (Crassostrea gigas) has been introduced deliberately into North America (early 1900s), Australia (1940s and 1950s), and France (1970s), where it is now a very lucrative aquaculture species. Its ability to tolerate a variety of different salinities and temperatures, in conjunction with its high fecundity, has made it an economically important species worldwide. In 2008, world aquaculture produced more than 600,000 t of Pacific oysters valued at more than US$1 billion (FAO 2010).

In Australia, Pacific oysters were released in temperate waters at Oyster Harbour, in southwest Western Australia, and in Pittwater, in Tasmania (English et al. 2000). Only the oysters at the Tasmanian site survived. Spatfall, however, was low and, in 1953, this population was moved to the more optimal site of Port Sorrell, Tasmania, where they flourished and spread to other Tasmanian locations (Stasko 2000). Today, there are several populations of Pacific oyster in Tasmania and, because of illegal introductions, New South Wales (NSW) (English et al. 2000). These now-naturalized oysters formed the basis of a number of commercial farms in Tasmania, South Australia, and NSW. These farms use seed from 2 main commercial hatcheries that maintain their own mass selection breeding programs and also use some seed from the national family-based selective breeding program. All programs have occasionally introduced additional wild-caught, naturalized oysters in an attempt to maintain or increase genetic diversity (Ward et al. 2000).

During the past decade, there has been a move toward family-based selective breeding programs in aquaculture as opposed to mass selection. In many countries, including Australia, the Pacific oyster industry is entirely hatchery based--meaning, it is in a position to benefit strongly from genetic improvement (Ward et al. 2000, Langdon et al. 2003, Degremont et al. 2010). Current selective breeding programs have been highly successful, particularly in improving growth rate (Ward et al. 2005). The future application of marker-assisted selection may provide further enhancement, particularly in relation to summer mortality resistance (Ward et al. 2005, Sauvage et al. 2010). Regardless of breeding technique, the maintenance of genetic diversity is fundamental to future improvement.

Determining the level of genetic variation in cultured, naturalized, and native oysters will provide baseline information important to breeding programs, such as revealing whether there has been a significant change since introduction, and will indicate whether poorly managed cultured stocks are at risk of inbreeding. English et al. (2000) found no significant genetic difference between Australian cultured (mass selected), Australian naturalized, and Japanese native populations of Pacific oyster using allozyme markers, suggesting that there had been little genetic loss since the introduction of the Pacific oyster during the 1940s. The allozyme results of Appleyard and Ward (2006) were in agreement with this conclusion. However, Appleyard and Ward (2006) also used 8 microsatellite markers to analyze the cultured (mass selected), naturalized, and native populations. They discovered a decrease in diversity within the cultured stock, which they attributed to the loss of rare alleles and bottleneck effects. Nevertheless, it was suggested that genetic diversity was still adequate and that inbreeding was likely to be minimal. They recommended continual monitoring of diversity levels to determine whether there is further decline. Kim et al. (2008) found a similar decrease in diversity in Korean cultured Pacific oysters compared with wild ones using 6 microsatellite loci. Li et al, (2006) investigated mass-selected Pacific oysters within and among 5 Chinese farms. The results indicated high genetic diversity within cultured Chinese Pacific oysters and, surprisingly, large differences in allelic frequencies between northern and southern farms. This suggests that population structuring can occur within this species.

Within closed breeding populations, some level of inbreeding is unavoidable, but this can be managed in family-based programs. Oysters are a highly fecund animal, a trait that can increase the risk of inbreeding if breeders only use a small number of individuals as broodstock (Hedgecock et al. 2004). In addition, Appleyard and Ward (2006) found that the effective number of broodstock was significantly less than the actual number of broodstock used, which could also result in greater inbreeding depression. This concurred with work by Boudry et al. (2002), who concluded that unbalanced parental contribution could be explained by both nongenetic and genetic effects. Tolerance to inbreeding is relatively species specific. Evans et al. (2004) found Pacific oysters to be sensitive to inbreeding depression, reporting an 8.8% decrease in average body weight and 4.3% decrease in survival with a 10% increase in inbreeding. Monitoring of inbreeding is highly important within shellfish culture to ensure that a healthy gene pool is maintained.

In the study by Appleyard and Ward (2006), microsatellite markers proved more informative than allozymes at detecting genetic diversity in Pacific oysters. There have been numerous microsatellite markers developed for the Pacific oyster (Hubert & Hedgecock 2004, Appleyard & Ward 2006) many of which have been mapped using linkage analysis (Hubert & Hedgecock 2004, Hubert et al. 2009, Plough & Hedgecock, 2011). Of these, only 15 microsatellite markers in 5 panels have been multiplexed within this species (Taris et al. 2005, Li et al. 2010). Multiplexing allows more rapid and cost-effective analysis. This study aimed to use at least 10 microsatellite markers, analyzed within multiplexing suites, to determine genetic diversity and potential inbreeding of native, naturalized, and cultured Pacific oysters. In addition to the native Japanese oysters used in the study by Appleyard and Ward (2006), oysters from Korea and France, as well as Australian naturalized and cultured populations, were analyzed.

METHOD

Sample Collection and DNA Extraction

In total, 343 Pacific oyster samples were used in this study. Native samples were obtained from Hiroshima (n = 17) and Sendai (24) in Japan and, the west coast of Korea (n = 41). The Japanese samples were those used in the studies by English et al. (2000) and Appleyard and Ward (2006). Naturalized samples were collected from Australia and France. The French samples (n = 50) were collected by L'Institut Francais de Recherche pour l'Exploitation de la Mer (IFREMER) from a number of different locations along the western coast. In Australia, naturalized samples were collected from the Bridport Estuary (n = 25) and Tamar Estuary (n = 25) in Tasmania, and at Port Stephens in NSW (n = 36). Cultured samples were provided from 2 Tasmanian hatcheries: Shellfish Culture (Clifton Beach, Tasmania; n = 50) and Cameron of Tasmania (Dunalley, Tasmania: n = 25). Shellfish Culture oysters were sampled across 3 mass selection breeding lines. Cameron samples were collected from a single mass-selected commercial line. Additional cultured oysters were sampled from the Australian national selective breeding program, operated by Australian Seafood Industries (ASI; Hobart, Tasmania; n = 50). These oysters have been bred selectively for 15 y. The ASI oysters were selected from 50 genetically diverse families (1 individual per family) across 4 year classes (2005 through 2008).

DNA was extracted from gill tissue (~0.5 [cm.sup.2]), mostly using the extraction protocol with NaC1 described by Lopera-Barrero et al. (2008), with the small modification of 2 [micro]L 20 mg/mL proteinase K, and resuspension in 100 [micro]L TE buffer. The DNA was not treated with RNase. A 1 in 10 DNA dilution was performed prior to polymerase chain reaction (PCR). The Korean samples were extracted by Jung-Ha Kang (National Fisheries Research and Development Institute), using MagExtractor MFX-6100 (Toyobo). The Japanese samples were previously extracted by English et al. (2000). All DNA was stored at -20[degrees]C.

Microsatellite Analysis and PCR Conditions

Originally, 29 previously published microsatellite markers, 3 from each linkage group (except L10, for which there were only 2), were selected from Hubert and Hedgecock's (2004) linkage map (for an updated linkage map see Plough and Hedgecock (2011)). Markers were selected based on high polymorphism, low frequency of null alleles, and similar annealing temperatures, as reported by the original designers of each marker (Magoulas et al. 1998, Huvet et al. 2000, Li et al. 2003). The 29 markers were reduced to the 13 that preformed best within a multiplex (e.g., noncomplementary, distinct size ranges). The 13 microsatellite markers used to assess genetic diversity were um2Cg10 and um2Cg48 (Huvet et al. 2000); ucdCg120, ucdCg126, ucdCg129, ucdCg160, ucdCg166, ucdCg171, ucdCg175, ucdCg196, ucdCg198, and ucdCg200 (Li et al. 2003); and imbCg49 (Magoulas et al. 1998). Primers for four of these markers (ucdCg129M F: GCATGCAGTGTATTGCTCTGT TAT, R: TGGCAAGAACTGGTGGTATG; ucdCg160M F: GAGATGGTTAGGCAGAACATTAAGA, R: TGTATCTCT TCCTTGTGCTCTCTC; ucdCg196M F: GCATCAGAAA TTGAACTTGCAC, R: GTCGATCTTGCCATTTGCTTT; um2Cg48M R: TTCCAAATGCAACTGAGAGAGT) were redesigned using Primer3Plus (Untergasser et al. 2007) to generate larger or smaller fragment sizes to allow for multiplexing at 60[degrees]C. Markers were divided into 4 multiplex groups using Multiplex Manager v. 1.1 (Holleley & Geerts 2009) (Table 1). The markers were a mixture of di-, tri-, and tetranucleotide repeats. An assortment of complex and simple microsatellites was used.

PCR was performed in 5-[micro]L reactions using the Qiagen multiplex PCR kit per the manufacturer's instructions (Qiagen, Australia). Primer concentrations were marker dependent (Table 1). Cycling conditions for PCR were as follows: 15 min at 95[degrees]C; 30 cycles of 30 sec at 94[degrees]C, 90 sec at annealing temperature, 90 sec at 72[degrees]C; 30 min at 60[degrees]C. A 1 in 200 dilution of PCR product was used as template for separation on an ABI 3770 (Applied Biosystems, Australia) sequencer, using LIZ 500 (-250) size standard by the Australian Genome Research Facility (Adelaide). Alleles were scored using GeneMapper v.3.7 software (Applied Biosystems, Australia).

Data Analysis

Marker Performance and Measures of Genetic Diversity

To assess the repeatability of each marker, approximately 10% of the samples were repeated by duplicate PCR samples being run across a sequencer (both within and across plates). The repeatability is the percentage of consistent allele scores across blindly scored, independent, repeated samples. The mean error rate per locus, as calculated in the current study, is the most common repeatability measure used (Pompanon et al. 2005). This measure is appropriate to our study given the number of markers used and the high quality of the DNA samples analyzed. Null allele frequency was calculated in INEst v. 1.0, using the individual inbreeding model (10,000 iterations (Chybicki & Burczyk 2009)). This program was also used to calculate a fixation index ([F.sub.IS]) corrected for null alleles. Deviations from Hardy-Weinberg equilibrium for each microsatellite locus were tested using the Markov chain method (10,000 dememorization steps, 100 batches, 5,000 iterations) in Genepop v. 4.0 (Rousset 2008). Linkage disequilibrium was also calculated in this program. FSTAT v. 2.9.3.2 (Goudet 1995) was used to calculate allele numbers and allelic richness across populations. Genetic subdivision ([F.sub.ST]) across predefined sample populations was also analyzed using this program. According to Jost (2008), [G.sub.ST] and its relatives (such as [F.sub.ST]) may not be a good representation of genetic differentiation when there is high within-population diversity, as often found with microsatellites. Therefore, the computer program SMOGD v. 1.2.5 (Crawford 2010) was used to calculate an estimate of D ([D.sub.EST]) (recommended by Jost (2008)) to validate the [F.sub.ST] results (bootstrap replicates, 1,000). GenA1Ex v. 6.4 (Peakall & Smouse 2006) was used to determine the number of private alleles per population, and observed (Ho) and expected (He) heterozygosity, and to generate a principal coordinate analysis using codominant genotypic distance (Smouse & Peakall 1999).

Bayesian Analysis

Bayesian analysis of population structure was undertaken using the methods implemented in BAPS v. 5.1 (Corander et al. 2003) and STRUCTURE v. 2.2.3 (Pritchard et al. 2000). Using BAPS, clustering of groups of individuals was calculated using an admixture model (1,000 iterations, 200 reference individuals, and 10 admixture coefficients). This method takes into account the a priori populations of the samples. For STRUCTURE analysis, the admixture model with correlated allelic frequencies was used (100,000 iterations after a burn in period of 50,000). This program uses an aposteriori method to group the samples into genetic clusters (populations) that reduce Hardy-Weinberg and gametic disequilibrium, thus reducing the biases associated with designations resulting from sampling locality. For each value of K (potential number of clusters) tested, 20 independent runs were performed, structureHarvester v. 0.56.4 (Earl 2009) was used to determine the delta K value using the method of Evanno et al. (2005), which indicates the number of clusters that best fits the data. CLUMPP v. 1.1.2 (Jakobsson & Rosenberg 2007) was used to standardize the results across runs.

RESULTS

Marker Performance

In general, the markers performed well within each multiplex. The microsatellites with tetra- and trinucleotide repeats were easier to score than those with dinucleotide repeats, primarily because more stutter was observed within the dinucleotides. All 13 microsatellite markers were highly polymorphic and had similar levels of repeatability (93.3-98.9%) and missing data (0.03-5%; except ucdCg196 at 16%; Table 2). Locus um2Cg48M had the most alleles (n = 71), but showed a large degree of stutter and was sometimes difficult to score. Consequently, it had the lowest repeatability percentage (93.3%). Null alleles were estimated to exist in all 13 loci at frequencies from 3-32% across all samples. Three loci, ucdCg129, ucdCg200, and ucdCg196, were excluded from the rest of the analysis because they had predicted null allele frequencies greater than 25% (Table 2). After these markers were excluded, the null allele frequencies were similar between native, naturalized, and cultured populations (Table 3). The 10 remaining markers were dispersed into 8 different linkage groups (y 1). Despite the fact that the pairs ucdCg171 um2Cg10 and imbCg49-ucdCg198 were linked with 19.6 cM and 30.1 cM, respectively, between them, linkage disequilibrium was not significant for either pair. All other markers were located more than 50 cM apart, and thus were considered unlinked.

Measures of Genetic Diversity

Private alleles were observed in all populations, with the highest numbers in the native Korean (n = 18) and Japanese (n = 12) samples, and the Tasmanian naturalized (n = 17) samples. However, private allele frequencies were low, with the highest observed being 7%, and most being between 1% and 3%; therefore, the private alleles were not considered further. The observed heterozygosity (Ho) in each population was significantly less than the expected (He) values (Table 3). Null allele frequencies and [F.sub.IS] values were similar frequencies across all populations and subgroups (Table 3). After null alleles were taken into consideration, the corrected [F.sub.IS] was much lower than that originally stated (Table 3). The average number of alleles (A), allelic richness (Ar), and expected heterozygosity (He) were lower in the cultured samples compared with the native and naturalized populations (Table 3). Specifically, the cultured populations showed a 34.5% decrease in the average Ar and a 5.6% decrease in average He. The family based breeding population (ASI) showed slightly, but not significantly, higher values of Ar than the mass-selected populations. There were no discernible differences in allele statistics between the native and naturalized oysters.

The overall [F.sub.ST] value among all populations was low (0.05), as were the [F.sub.ST] values among the naturalized, native, and cultured groups (0.01-0.06). The [F.sub.ST] between native and naturalized populations was negligible (0.00). The [F.sub.ST] between native and cultured or naturalized and cultured populations was slightly higher (0.02-0.03). The [D.sub.EST] values (0.044).30), although higher than the [F.sub.ST] values, showed the same patterns as the [F.sub.ST] results (data not shown). The principal coordinate analysis showed a close clustering of native and naturalized populations compared with the hatchery-derived samples (Fig. 1). The percent of variation explained by PC1 was 46% and by PC2 was 22%.

Bayesian Analysis

The output from BAPS indicated 2 genetic clusters (probability of 2 clusters, 0.99). One cluster grouped the native and naturalized populations; the other cluster grouped the family breeding population and mass-selected populations (data not shown). The STRUCTURE output showed a large degree of log-likelihood variance, particularly within the K = 3 runs, and this was relatively unaltered by an increase in the number of burn-in iterations. To reduce this variance, and thus improve the accuracy of the method of Evanno et al. (2005) at finding the "true" K, the 5 runs with the highest average log-likelihood value were selected and analyzed for each value of K tested. This approach reduced the variance considerably and produced a delta K plot that concurred with the BAPS outcome (i.e., 2 genetic clusters present). The Q matrix plot (bar plot showing individual population assignment) also concurred with the BAPS output by assigning the native and naturalized oysters to one cluster and the cultured oysters to a second (data not shown). Within the native and naturalized populations, the samples from Korea, NSW, and, to a lesser extent, Tasmania showed a small number of individuals with genotypes that were consistent with the cultured oysters. Within the cultured oyster populations, the Cameron samples had the most individuals with genotypes similar to the native/naturalized cluster.

[FIGURE 1 OMITTED]

DISCUSSION

The 10 microsatellite markers used in this study performed well within the multiplexes, suggesting that they could be useful tools for cost-effective genotyping of Pacific oysters in the future. Neutral markers, such as microsatellites, have been shown to be effective for analyzing diversity among populations (Pfrender et al. 2000). All the populations analyzed in this study showed disagreement with Hardy-Weinberg equilibrium, as shown by the large difference between expected and observed heterozygosity. This could be explained by the high number of nonamplifying null alleles that have been shown to occur in this study and others in oysters (McGoldrick et al. 2000, Hedgecock et al. 2004). Fortunately, the presence of null alleles is unlikely to have affected significantly the population assignment analysis of this study because all the populations showed similar frequencies of null alleles.

In general, this study concurred with the microsatellite findings of Appleyard and Ward (2006). Our results indicated a distinct difference in genetic diversity between cultured and native/naturalized populations, supporting the hypothesis of an initial hatchery-induced bottleneck. A loss of genetic variation is a common phenomenon within mass selection breeding programs, particularly in molluscs (Appleyard & Ward 2006), where often only a small number of broodstock are used because of their high fecundity. A number of studies in oysters has shown that the effective size of a population (Ne) is generally smaller than the number of spawned individuals (Hedgecock & Sly 1990, Li & Hedgecock 1998, Boudry et al. 2002, Appleyard & Ward 2006); hence, a large number of broodstock are generally needed to maintain high diversity levels. It is important for oyster breeding to manage genetic diversity, because high diversity is an indicator of future selective breeding potential and a reduced risk of inbreeding depression. A decrease in diversity is expected within breeding programs (both mass and family) as hatcheries need to balance selection intensity with inbreeding (Bentsen & Olesen 2002). As yet, there is no definitive threshold to inform oyster farmers what the minimal diversity index should be. Pacific oyster growth and survival have been shown to be affected significantly and adversely by relatively low levels of inbreeding ([F.sub.IS] = 0.2); therefore, diversity should be maximized (Evans et al. 2004).

The Australian ASI family-based breeding program was established in 1997, primarily to improve growth rate within cultured Pacific oysters. One of the main goals of the breeding program was to balance genetic gain and inbreeding. As a result, the inbreeding coefficient has been calculated over each generation, and the program aims for an average rate of inbreeding less than 1% per generation. Such a rate of inbreeding is considered acceptable for long-term sustainable breeding programs (Ward et al. 2005, Kube et al. 2011). It is important to note that the commercial lines from Shellfish Culture and Cameron showed no significant difference in allelic richness or inbreeding coefficients compared with the ASI population. This suggests that the current mass-selected breeding programs within these 2 Australian hatcheries have inbreeding levels similar to that of the family-based ASI program, which has been monitored to ensure that the inbreeding rate per generation is less than 1%. This suggests that the mass-selected populations are not currently experiencing inbreeding depression.

Our observed 34.5% decrease in Ar within the cultured populations is lower than the 60.4% decrease reported by Xiao et al. (2011) within cultured populations of Crassostrea ariakensis. Our observed reduction in He is also less than that observed (10.7%) by Appleyard and Ward (2006) for previous Australian hatchery year classes. Bottleneck effects are common within breeding programs resulting from small, effective population sizes. It has been shown that, within populations that have undergone a bottleneck, there is a time delay before significant changes to He are observed (Hedgecock & Sly 1990, Leberg 1992), and therefore the difference observed between the 2 studies may be an artifact of the samples and loci in each.

Bayesian analysis could not differentiate between native and naturalized populations. This result, coupled with the similar results observed in allelic richness and heterozygosity, indicate that naturalized populations in France and Australia have lost very little diversity since introduction. This concurs with the work by Appleyard and Ward (2006). Figure 1 showed Tasmanian and French naturalized samples clustered together, suggesting they may have originated from a similar native population. In Australia, many oyster hatcheries, including Shellfish Culture and Cameron, regularly use naturalized individuals as broodstock in an attempt to infuse greater genetic diversity into their lines. The results of this study suggest that naturalized populations of Pacific oysters in France and Australia are good reservoirs of diversity for breeding programs or hatcheries.

ACKNOWLEDGMENTS

We thank the following who helped in sample collection: Mr. Scott Parkinson, Dr. Michel Bermudes and staff (Shellfish Culture, Tasmania), Mr. Mike and Mr. Graeme Cameron and staff (Cameron of Tasmania), Mr. Matthew Cunningham and Mr. Ben Finn (Australian Seafood Industries), Mr. Mike Dove (Industry and Investment, NSW), Dr. Sylvie Lapegue (IFREMER, France), Dr. Jung-Ha Kang (NFRDI, Korea), Dr. Sharon Appleyard (CSIRO, Tasmania), Dr Mathew Cook (CSIRO, Brisbane), Mr. Robert Green (Department of Primary Industries, Tasmania), and Mr. Michael Ezzy (volunteer, Tasmania). We thank Ms. Sascha Wise and Dr. Rebecca Jones, University of Tasmania, for technical advice. We thank the editor and reviewers of the Journal of Shellfish Research. Funding for this study was provided by CSIRO Food Futures Flagship, the Australian Seafood CRC, and Shellfish Culture Ltd.

LITERATURE CITED

Appleyard, S. A. & R. D. Ward. 2006. Genetic diversity and effective population size in mass selection lines of Pacific oyster (Crassostrea gigas). Aquaculture 254:148-159.

Bentsen, H. B. & I. Olesen. 2002. Designing aquaculture mass selection programs to avoid high inbreeding rates. Aquaculture 204:349-359.

Boudry, P., B. Collet, F. Cornette, V. Hervouet & F. Bonhomme. 2002. High variance in reproductive success of the Pacific oyster (Crassostrea gigas, Thunberg) revealed by microsatellite-based parentage analysis of multifactorial crosses. Aquaculture 204:283-296.

Chybicki, I. J. & J. Burczyk. 2009. Simultaneous estimation of null alleles and inbreeding coefficients. J. Hered. 100:106-113.

Corander, J., P. Waldmann & M. J. Sillanpaa. 2003. Bayesian analysis of genetic differentiation between populations. Genetics 163:367-374.

Crawford, N. G. 2010. SMOGD: software for the measurement of genetic diversity. Mol. Ecol. Resour. 10:556-557.

Degremont, L., E. Bedier & P. Boudry. 2010. Summer mortality of hatchery-produced Pacific oyster spat (Crassostrea gigas). II: response to selection for survival and its influence on growth and yield. Aquaculture 299:21-29.

Earl, D. A. 2009. StructureHarvester v. 0.3. http://users.soe.ucsc.edu/ ~dearl/software/struct_harvest/.

English, L. J., G. B. Maguire & R. D. Ward. 2000. Genetic variation of wild and hatchery populations of the Pacific oyster, Crassostrea gigas (Thunberg), in Australia. Aquaculture 187:283-298.

Evanno, G., S. Regnaut & J. Goudet. 2005. Detecting the number of clusters of individuals using the software STRUCTURE: a simulation study. Mol. Ecol. 14:2611-2620.

Evans, F., S. Matson, J. Brake & C. Langdon. 2004. The effects of inbreeding on performance traits of adult Pacific oysters (Crassostrea gigas). Aquaculture 230:89-98.

FAO. 2010. FISHSTAT Plus: universal software for fisheries statistical time series. Version 2.3. 2000. FAO Fisheries Department, Fisheries Information, Data and Statistics Unit.

Goudet, J. 1995. FSTAT (version 1.2): a computer program to calculate F-statistics. J. Hered. 86:485-486.

Hedgecock, D., G. Li, S. Hubert, K. Bucklin & V. Ribes. 2004. Widespread null alleles and poor cross-species amplification of microsatellite DNA loci cloned from the Pacific oyster, Crassostrea gigas. J. Shellfish Res. 23:379-385.

Hedgecock, D. & F. Sly. 1990. Genetic drift and effective population sizes of hatchery-propagated stocks of the Pacific oyster, Crassostrea-gigas. Aquaculture 88:2108.

Holleley, C. E. & P. G. Geerts. 2009. Multiplex Manager 1.0: a cross-platform computer program that plans and optimizes multiplex PCR. Biotechniques 46:511-517.

Hubert, S., E. Cognard & D. Hedgecock. 2009. Centromere mapping in triploid families of the Pacific oyster Crassostrea gigas (Thunberg). Aquaculture 288:172-183.

Hubert, S. & D. Hedgecock. 2004. Linkage maps of microsatellite DNA markers for the Pacific oyster Crassostrea gigas. Genetics 168:351-362.

Huvet, A., P. Boudry, M. Ohresser, C. Delsert & F. Bonhomme. 2000. Variable microsatellites in the Pacific oyster Crassostrea gigas and other cupped oyster species. Anita. Genet. 31:71-72.

Jakobsson, M. & N. A. Rosenberg. 2007. CLUMPP: a cluster matching and permutation program for dealing with label switching and multimodality in analysis of population structure. Bioinformatics 23:1801-1806.

Jost, L. 2008. G(ST) and its relatives do not measure differentiation. Mol. Ecol. 17:4015-4026.

Kim, W. J., K. Y. Park, B. T. Nam, H. J. Kong, Y. O. Kim, E. M. Park & T. I. Kim. 2008. Genetic diversity and population genetic structure of Pacific oyster (Crassostrea gigas) from Korea using microsatellite marker. J. Shellfish Res. 27:1021-1022.

Kube, P., M. Cunningham, S. Dominik, S. Parkinson, B. Finn, J. Henshall, R. Bennett & M. Hamilton. 2011. Enhancement of the Pacific Oyster Selective Breeding Program. CSIRO Marine and Atmospheric Research, Australian Seafood Industries P/L, Seafood CRC, and Fisheries Research and Development Corporation (FRDC), Australia. pp. 1-113.

Langdon, C., F. Evans, D. Jacobson & M. Blouin. 2003. Yields of cultured Pacific oysters Crassostrea gigas Thunberg improved after one generation of selection. Aquaculture 220:227-244.

Leberg, P. L. 1992. Effects of population bottlenecks on genetic diversity as measured by allozyme electrophoresis. Evolution 46: 477-494.

Li, G. & D. Hedgecock. 1998. Genetic heterogeneity, detected by PCRSSCP, among samples of larval Pacific oysters (Crassostrea gigas) supports the hypothesis of large variance in reproductive success. Can. J. Fish. Aquat. Sci. 55:1025-1033.

Li, G., S. Hubert, K. Bucklin, V. Ribes & D. Hedgecock. 2003. Characterization of 79 microsatellite DNA markers in the Pacific oyster Crassostrea gigas. Mol. Ecol. Notes 3:228-232.

Li, R., Q. Li, F. Cornette, L. Degremont & S. Lapegue. 2010. Development of four EST-SSR multiplex PCRs in the Pacific oyster (Crassostrea gigas) and their validation in parentage assignment. Aquaculture 310:234-239.

Li, Q., H. Yu & R. H. Yu. 2006. Genetic variability assessed by microsatellites in cultured populations of the Pacific oyster (Crassostrea gigas) in China. Aquaculture 259:95-102.

Lopera-Barrero, N. M., J. A. Povh, R. P. Ribeiro, P. C. Gomes, C. B. Jacometo & T. S. Lopes. 2008. Comparison of DNA extraction protocols of fish fin and larvae samples: modified salt (NaCI) extraction. Cie. Invest. Agraria 35:65-74.

Magoulas, A., B. Gjetvaj, V. Terzoglou & E. Zouros. 1998. Three polymorphic microsatellites in the Japanese oyster, Crassostrea gigas (Thunberg). Anita. Genet. 29:69-70.

McGoldrick, D. J., D. Hedgecock, L. J. English, P. Baoprasertkul & R. D. Ward. 2000. The transmission of microsatellite alleles in Australian and North American stocks of the Pacific oyster (Crassostrea gigas): selection and nulr alleles. J. Shellfish Res. 19:779-788.

Peakall, R. & P. E. Smouse. 2006. GenAlEx 6: genetic analysis in Excel: population genetic software for teaching and research. Mol. Ecol. Notes 6:288-295.

Pfrender, M. E., K. Spitze, J. Hicks, K. Morgan, L. Latta & M. Lynch. 2000. Lack of concordance between genetic diversity estimates at the molecular and quantitative-trait levels. Conserv. Genet. 1:263-269.

Plough, E. V. & D. Hedgecock. 201 I. Quantitative trait locus analysis of stage-specific inbreeding depression in the Pacific oyster Crassostrea gigas. Genetics 189:1473-1486.

Pompanon, F., A. Bonin, E. Bellemain & P. Taberlet. 2005. Genotyping errors: causes, consequences and solutions. Nat. Rev. Genet. 6:847-859.

Pritchard, J. K., M. Stephens & P. Donnelly. 2000. Inference of population structure using multilocus genotype data. Genetics 155:945-959.

Rousset, F. 2008. GENEPOP " 007: a complete re-implementation of the GENEPOP software for Windows and Linux. Mol. Ecol. Resour. 8:103-106.

Sauvage, C., P. Boudry, D. J. de Koning, C. S. Haley, S. Heurtebise & S. Lapegue. 2010. QTL for resistance to summer mortality and OsHV-1 load in the Pacific oyster (Crassostrea gigas). Anim. Genet. 41:390-399.

Smouse, P. E. & R. Peakall. 1999. Spatial autocorrelation analysis of individual multiallele and multilocus genetic structure. Heredity 82:561-573.

Stasko, N. 2000. Oyster: from Montparnasse to Greenwell Point. In: Stasko, N. (ed.). Australia. Harper Collins. pp. 33-35.

Taris, N., S. Baron, T. F. Sharbel, C. Sauvage & P. Boudry. 2005. A combined microsatellite multiplexing and boiling DNA extraction method for high-throughput parentage analyses in the Pacific oyster (Crassostrea gigas). Aquacult. Res. 36:51-518.

Untergasser, A., H. Nijveen, X. Rao, T. Bisseling, R. Geurts & J. A. M. Leunissen. 2007. Primer3Plus: an enhanced Web interface to Primer3. Nucl. Acids Res. 35:W71-W74.

Ward, R. D., L. J. English, D. J. McGoldrick, G. B. Maguire, J. A. Nell & P. A. Thompson. 2000. Genetic improvement of the Pacific oyster Crassostrea gigas (Thunberg) in Australia. Aquacult. Res. 31:35-44.

Ward, R. D., P. A. Thompson, S. A. Appleyard, A. A. Swan & P. D. Kube. 2005. Sustainable genetic improvement of Pacific oysters in Tasmania and South Australia. Hobart, Australia: Commonwealth Scientific and Industrial Research Organisation (CSIRO) and Fisheries Research and Development Corporation (FRDC).

Xiao, J., J. F. Cordes, J. A. Moss & K. S. Reece. 2011. Genetic diversity in U.S. hatchery stocks of Crassostrea ariakensis (Fujita, 1913) and comparison with natural populations in Asia. J. Shellfish Res. 30: 751-760.

PENNY A. MILLER, (1,2) * NICHOLAS G. ELLIOTT, (3,4) ANTHONY KOUTOULIS, (1) PETER D. KUBE (3,4) AND RENE E. VAILLANCOURT (1)

(1) School of Plant Science, University o)c Tasmania, Private Bag 55, Hobart, Tasmania, Australia, 7001,

(2) Australian Seafood Cooperative Research Centre, Box 26, Mark Oliphant Building, Science Park Adelaide, Laffer Drive, Bedford Park, SA, 5042; (3) CSIRO Marine and Atmospheric Research, Castray Esplanade, Hobart, Tasmania, 7001; (4) CSIRO Food Futures National Research Flagship, Australia

* Corresponding author. E-mail: pamiller@utas.edu.au

DOI: 10.2983/035.031.0303
TABLE 1.
Multiplexes of microsatellite markers for
the Pacific oyster Crassostrea gigas.

                                     Primer
Microsatellite                    Concentration   Linkage   Repeat
                                   ([micro]M)      Group    Array

Panel l; [T.sub.m] 60[degrees]C
  ucdCg129M                             2           10      GA
  ucdCg166                              3            9      TC
  ucdCgJ71                              2           10      CAT
  ucdCg200                              1            1      GAT
  um2Cg48M                              3            3      GA
Panel 2; [T.sub.m] 60[degrees]C
  ucdCg160M                             1            3      (GA)(GACA)
  ucdCg196M                             3            8      (GAC)(GAT)
  ucdCg198                              1            4      CAT
Panel 3; [T.sub.m] 60[degrees]C
  um2Cg10                               1           10      AG
  ucdCg120                              1            5      (CA)(GA)
  ucdCg126                              1            2      (TCTA)
Panel 4; [T.sub.m] 55[degrees]C
  imbCg49                               1            4      GT
  ucdCg175                              3            8      CAT

                                                    Allele     No. of
Microsatellite                    5' Fluorescent     Size     Alleles
                                      Label         Range     Observed

Panel l; [T.sub.m] 60[degrees]C
  ucdCg129M                            FAM         129-207       43
  ucdCg166                             PET         184-264       34
  ucdCgJ71                             FAM         211-273       16
  ucdCg200                             HEX         227-284       18
  um2Cg48M                             NED         107-202       71
Panel 2; [T.sub.m] 60[degrees]C
  ucdCg160M                            FAM         125-336       61
  ucdCg196M                            HEX         311-430       34
  ucdCg198                             HEX         216-278       19
Panel 3; [T.sub.m] 60[degrees]C
  um2Cg10                              HEX          95-240       49
  ucdCg120                             PET         135-169       15
  ucdCg126                             NED          92-233       34
Panel 4; [T.sub.m] 55[degrees]C
  imbCg49                              PET         129-204       37
  ucdCg175                             PET         217-345       37

M indicates that primers have been changed from those
originally published for each microsatellite. [T.sub.m] is
the annealing temperature used in this  study. Linkage
groups are in accordance with Plough and Hedgecock (2011).

TABLE 2.
Marker performance data.

                                        Microsatellite Marker

                             129    166    171    200     48     160

No. of alleles               43     34     16     18     71     61
Repeatability (%)            95.5   97     97     97     93.3   97.8
Missing data (%)              5      3      4.6    1.6    2.4    0.05
Null allele (%)              32     20     21     25     18     11
Null alleles within          31.3   22.1   16.6   26.9   22.7   14.3
  cultured samples (%)
Null alleles within          34.2   20.3   20.1   24.6   18.1    9.3
  naturalized samples (%)
Null alleles within native    2.7   20.3   24.8    1.6   12.9    9.0
  samples (%)

                                    Microsatellite Marker

                             196    198     10      120    126

No. of alleles               34     19     49      15      34
Repeatability (%)            96.3   94.8   97.8    98.6    97.8
Missing data (%)             16      1.1    0.03    0.05    3.8
Null allele (%)              32     12      5       6      21
Null alleles within          36.9   18.2    0.9     2.1    28.6
  cultured samples (%)
Null alleles within          29.8   10.9    5.2     2.2    23.8
  naturalized samples (%)
Null alleles within native    3.2   11.7    4.7     5.2    19.4
  samples (%)

                             Microsatellite Marker

                              49    175     Av

No. of alleles               37     37     36
Repeatability (%)            96.7   97     96.6
Missing data (%)              1.4    1.4    3.1
Null allele (%)              11      3     17
Null alleles within           7.3    2.5   17.7
  cultured samples (%)
Null alleles within          11.6    2.5   16.4
  naturalized samples (%)
Null alleles within native   12.1    1.1    9.9
  samples (%)

The performance statistics for 13 microsatellite markers (details
in Table 1) of the Pacific oyster (Crassostrea gigas) across 343
samples. Null allele  percentage calculated in INEst v.1.0
(10,000 iterations (Chybicki & Burczyk 2009)). Av, average.

TABLE 3.
Sample size (n), average number of alleles (A), allelic richness
(Ar; based on 24 individuals), observed heterozygosity (Ho),
expected heterozygosity (He), inbreeding coefficient
([F.sub.IS]), corrected [F.sub.IS], number of private alleles,
and frequency of null alleles (Null %) calculated using 10
microsatellite markers in the 8 Pacific oyster (Crassostrea
gigas) populations.

Population      n     A      Ar    [H.sub.o]   [H.sub.e]   [F.sub.IS]

Cultured
  ASI           50   15.2   12.8     0.64        0.84         0.25
Cameron         25   12.2   12.1     0.61        0.84         0.30
  S. Culture    50   14.6   11.9     0.59        0.83         0.30
Naturalized
  France        50   25.0   19.1     0.66        0.91         0.28
  Tasmania      50   24.3   19.2     0.67        0.90         0.27
  NSW           36   20.1   17.6     0.64        0.88         0.29
Native
  Japan         41   23.4   19.3     0.71        0.88         0.21
  Korea         41   22.7   18.5     0.62        0.89         0.32

                Corrected    Null
Population      [F.sub.IS]   (%)

Cultured
  ASI              0.01      12.6
Cameron            0.01      14.8
  S. Culture       0.02      13.9
Naturalized
  France           0.02      13.2
  Tasmania         0.02      12.5
  NSW              0.03      13.5
Native
  Japan            0.01      10.3
  Korea            0.04      14.4

ASI, Australian seafood industries; Cameron, Cameron Oysters
Tasmania mass selected line; NSW, New South Wales, Australia;
S. Culture, Shellfish Culture Tasmania.
Gale Copyright: Copyright 2012 Gale, Cengage Learning. All rights reserved.