GENE-39541; No. of pages: 8; 4C: Gene xxx (2014) xxx–xxx

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A preliminary study for identification of candidate AFLP markers under artificial selection for shell color in pearl oyster Pinctada fucata Keshu Zou a,b, Dianchang Zhang a,c,⁎, Huayang Guo a, Caiyan Zhu a, Min Li a,c, Shigui Jiang a,c,⁎ a b c

Division of Aquaculture and Biotechnology, South China Sea Fisheries Research Institute, Chinese Academy of Fishery Science, Guangzhou 510300, China School of Fisheries and Life Science, Shanghai Ocean University, Shanghai 201306, China Key Laboratory of South China Sea Fishery Resources Exploitation and Utilization, Ministry of Agriculture, Guangzhou 510300, China

a r t i c l e

i n f o

Article history: Received 16 May 2013 Received in revised form 20 December 2013 Accepted 11 March 2014 Available online xxxx Keywords: Pinctada fucata AFLP Shell color Artificial selection Outlier loci

a b s t r a c t Pearl oyster Pinctada fucata is widely cultured to produce seawater pearl in South China, and the quality of pearl is significantly affected by its shell color. Thus the Pearl Oyster Selective Breeding Program (POSBP) was carried out for the shell color and growth traits. The black (B), gold (G), red (R) and white (W) shell strains with fast growth trait were achieved after five successive generation selection. In this study, AFLP technique was used to scan genome of four strains with different shell colors to identify the candidate markers under artificial selection. Eight AFLP primer combinations were screened and yielded 688 loci, 676 (98.26%) of which were polymorphic. In black, gold, red and white strains, the percentage of polymorphic loci was 90.41%, 87.79%, 93.60% and 93.31%, respectively, Nei's gene diversity was 0.3225, 0.2829, 0.3221 and 0.3292, Shannon's information index was 0.4801, 0.4271, 0.4825 and 0.4923, and the value of FST was 0.1805. These results suggested that the four different shell color strains had high genetic diversity and great genetic differentiation among strains, which had been subjected to the continuous selective pressures during the artificial selective breeding. Furthermore, six outlier loci were considered as the candidate markers under artificial selection for shell color. This study provides a molecular evidence for the inheritance of shell color of P. fucata. © 2014 Elsevier B.V. All rights reserved.

1. Introduction Molluscan shell color is highly variable and the rich color polymorphisms have attracted many scientists to studying in related fields throughout human history. Previous studies demonstrated that environmental factors, such as lights, salinity and climate, could affect the molluscan shell color (Cowie, 1990; Heath, 1975; Sokolova and Berger, 2000). For example, climatic selection was involved in producing the differences among populations of land snail (Theba pisana), and populations in hotter regions tended to have higher frequencies of pale shells (Cowie, 1990). Besides that, it was one of the significant factors in maintaining shell color polymorphism of the intertidal snail Batillaria (Miura et al., 2007). In some cases, it was suggested that correlation between environment variation and shell color polymorphisms was a result of pleiotropic effects of genes responsible for the shell color, or a linkage between them and genes determining certain physiological features (Raffaelli, 1982). Therefore, molluscan shell color is regulated not only

Abbreviations: AFLP, amplified fragment length polymorphism; AMOVA, analysis of molecular variance; dNTP, deoxyribonucleoside triphosphate; PCR, polymerase chain reaction; QTL, quantitative trait loci; POSBP, Pearl Oyster Selective Breeding Program. ⁎ Corresponding authors at: 231 W Xingang Road, Guangzhou City, Guangdong Province, China. E-mail addresses: [email protected] (D. Zhang), [email protected] (S. Jiang).

by environmental factors but also by genetic factors. Moreover, the genetic factors may play a major role in the determination of shell color (Liu et al., 2009). Cole (1975) found that a single-locus genetic model controlled three color types in the conch Urosalpinx cinerea, which was the first evidence of genetic determination in shell colors. Recently, a new bluish shell color was discovered in a full-sib family of the pacific abalone (Haliotis discus hannai), and segregation data indicated that the bluish and greenish variants were genetically controlled by a recessive and a dominant allele, respectively, at a single locus (Kobayashi et al., 2004). Similar study also demonstrated that the relatively simple genetic basis for shell color polymorphisms was involved in one or two loci with dominance (Ekendahl and Johannesson, 1997; Elek and Adamkewicz, 1990), although shell color polymorphisms might be determined by more complex genetic systems (Winkler et al., 2001). Studies on the gene expression in the mantle of vetigastropod (Haliotis asinina) revealed a complex secretome and characterized a number of genes involved in shell construction and coloration in this tropical abalone (Jackson et al., 2006, 2007). Considerable evidences suggested that molluscan shell color was under genetic control. Thus, shell color polymorphisms are amenable to artificial selection. Color manipulation through selection in aquaculture has occurred for many species (Lutz, 2001). Wada and Komaru (1996) found that white coloration might be inherited under the control of recessive gene(s) by artificial selection for shell color in pearl oyster

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Please cite this article as: Zou, K., et al., A preliminary study for identification of candidate AFLP markers under artificial selection for shell color in pearl oyster Pincta..., Gene (2014), http://dx.doi.org/10.1016/j.gene.2014.03.029

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Pinctada fucata. The shell color polymorphism is suitable as a genetic marker, since individuals with different shell colors are easily distinguished from each other. To better understand the trait's genetic basis of shell color polymorphisms, researchers have used them for genetic markers in classic population studies and in selective breeding programs of pearl oyster (Guan et al., 2011), scallops (Qin et al., 2007; Yuan et al., 2012), pacific oyster (Sanford et al., 2009), clams (Sokolowski et al., 2002), snails (Sokolova and Berger, 2000) and abalones (Kobayashi et al., 2004; Liu et al., 2009). During selective breeding, human exercised extremely strong selective pressure on ancestral gene pools to achieve the desired phenotypic characteristics (Innan and Kim, 2004). The intense selection for specific phenotypic traits might leave a genetic signal in the underlying genome, which allowed the genetic basis of the trait to be determined without an extensive breeding design. To date, the patterns of genetic differentiation between populations are used to detect outlier loci under selection pressure in genome scans (Excoffier et al., 2009). Likewise, genome scans of genetic-differentiation approach are capable of evaluating the contribution and extent of artificial selection in cultured animals (Pollinger et al., 2005; Qanbari et al., 2011; Wiener and Wilkinson, 2011). Genome scans are designed to identify regions where selection has acted and which therefore, might contain genes of large phenotypic effect. The rationale is that, even without phenotypic information, one can use patterns of genetic differentiation to highlight genomic regions under selection. In empirical studies, encouraging results have been obtained regarding the robustness of the genome scans based on differentiation (Foll and Gaggiotti, 2008; Murray and Hare, 2006) and independent evidence for selection or at least increased differentiation in the genomic regions was indicated by outlier loci (Mäkinen et al., 2008; Wiener et al., 2011; Wood et al., 2008). The genome scans of differentiation is an approach that markers with strong evidence of genetic differentiation (e.g. high levels of Wright's FST, a measure of genetic differentiation between populations) are taken as signals of differential selection across populations. Whereas a candidate gene approach requires a priori knowledge of gene function and linkage mapping requires information about familial relationships as well as access to samples from large numbers of relatives, the genome scans of differentiation evaluate the effects of natural or artificial selection across whole genomes in populations of unrelated individuals that have been subjected to differential selection pressures for the trait or traits of interest (Akey et al., 2002; Kayser et al., 2003; Pollinger et al., 2005). Recently, Holt et al. (2011) demonstrated that high differentiation AFLP outliers suggested divergent selection on color forms of hamlet fish and indicated three genomic regions potentially involved in color pattern polymorphism. Balancing selection was inferred to maintain color polymorphisms in theridiid spiders based on the observation that differentiation at the ‘color’ locus was smaller than the neutral estimates based on AFLP markers (Croucher et al., 2011). Although the genome scans is largely used in natural selection, this technique is possibly even better suited to study the domesticated animals because breeds are in general genetically similar entities and the differences that do exist may reflect the relatively recent selection for breed-specific traits. Akey et al. (2010) conducted an FST scan of genome for 10 dog breeds and identified outlier loci, which they argued were candidates for targets of selection. For cattle, the genetic-differentiation approach has highlighted genomic regions that contained gene encoding coat features or body size/conformation, indicating that these genes have been important in the establishment of cattle breeds (Flori et al., 2009; Wiener et al., 2011). Analogously, Xu et al. (2012) demonstrated that the domesticated common carp (Cyprinus carpio L.) strains had higher genetic differentiation than the wild populations, and 5 outlier loci appeared to be under positive directional selection in the domesticated strains. Pearl oyster P. fucata is one of the most important species that is cultured for production of high-quality pearls, which cover more than 90% seawater pearl production in China (Gu et al., 2011). Thus the Pearl Oyster Selective Breeding Program (POSBP) was implemented for the

shell color and growth traits. The black (B), gold (G), red (R) and white (W) shell strains with fast growth trait were obtained after five successive generation selection (Fig. 1). However, no study has focused on the artificial selection in the genome-wide population differentiation of these strains. AFLP genome scans is a relevant approach to detect outlier loci potentially undergoing selection at the genome scale, without the need for a priori knowledge of the genes involved. Therefore, the goal of this study is to investigate the extent of genetic diversity and genetic differentiation in the four strains driven by artificial selection pressures, and detect the potentially genetic markers under the artificial selective pressures using AFLP genome scan, which could be consequently applied in marker-assisted selection. 2. Materials and methods 2.1. Experimental animals All experimental animals were cultured in the pearl oyster culture base of South China Sea Fisheries Research Institute, Xincun Village, Hainan Province, China. In the beginning, P. fucata with black (B), gold (G), red (R) and white (W) shells were collected respectively from the same population to establish F0 selective population, and then 200 individuals of each shell color population were selected on the basis of shell color and growth trait to serve as parents of next generation, the selection intensity was 1.76. Four different shell color strains of P. fucata were achieved with fast growth trait after five successive generation selection (Fig. 1). In this study, 32 individuals of each shell color strain were randomly selected from F5 populations, and the adductor muscle of each individual was dissected and stored in 95% ethanol at 4 °C until used, respectively. 2.2. Genomic DNA extraction Genomic DNA was extracted from the adductor muscle tissue using proteinase-K digestion and DNA binding columns (QIAGEN QIAamp DNA Mini Kit, Hilden, GmbH) according to the protocol. The quality and quantity of extracted DNA were detected by 1% agarose gel electrophoresis and Thermo NanoDrop-2000 separately. DNA concentration was adjusted to 50 ng/μl. 2.3. AFLP procedure AFLP procedure (Fig. 2) was carried out based on the method described by Vos et al. (1995), with few modifications. 250 ng DNA of each sample (10 μl solution) was digested by 10 U EcoRI restriction enzyme for 1 h at 37 °C in 10 μl reaction volume containing 1 μl 10× EcoRI buffer. Then 10 μl digested DNA solution was digested by 2 U MseI restriction enzyme for 3 h at 37 °C, and 0.1 μl 100× MseI BSA buffer was added. In the same reaction, 1.0 μl of each adapter pair (EcoRI adaptors 2.5 μM; MseI 25 μΜ; (Table 1)) was ligated to cutting sites by adding 1.2 U T4 ligase and 2 μl 10× T4 ligase buffer. The solution was incubated for 1 h at 37 °C firstly and then for 20 h at 16 °C. Pre-selective and selective amplification PCR was shown in Tables 2 and 3. As the template for the selective PCR, pre-selective PCR products were diluted 20 times by sterile distilled water. Amplification products were separated in an ABI3730 DNA analyzer (Applied Biosystems Inc.) following the manufacturer's protocol and GeneScan-500 (LIZ) size standard (Applied Biosystems Inc.) was used. Eight primer combinations (Table 4) were chosen for genotyping after screening 64 different primer combinations for profile quality (i.e. whether the presence (1) or absence (0) of individual peaks could be clearly determined) and polymorphism (i.e. there appeared to be high variation in peak presence/absence between individual profiles). Primer combination pairs of the highest polymorphism were selected for further analysis.

Please cite this article as: Zou, K., et al., A preliminary study for identification of candidate AFLP markers under artificial selection for shell color in pearl oyster Pincta..., Gene (2014), http://dx.doi.org/10.1016/j.gene.2014.03.029

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Fig. 1. Three representative individuals from each of the four strains. Four pearl oysters Pinctada fucata of F5 strains with white (W), gold (G), black (B), and red (R) shells in the study. The first line is white shell; the second line is gold shell; the third line is black shell; and the fourth line is red shell of P. fucata. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

AFLP protocol

Table 1 Adaptors, pre-selective primers and selective primer combination sequences used in the study.

Sample collection

Primers Adapters MseI adapter

DNA extraction

EcoRI adapter

EcoRI digestion

Selective primers screened for further population genetic analyses

Pre-selective primers MseI pre-selective primer EcoRI pre-selective primer

Mse I digestion

Pre-selective PCR

Selective PCR ABI3730 DNA analyzer AFLP data generated ABI Genemapper v4.0 Binary data matrix

Popgene 1.32

Popgene 1.32

Genetic diversity

Arlequin 3.5

Genetic differentiation

Arlequin 3.5

Bayescan 2.01

Outlier loci analysis

Fig. 2. The flow diagram for AFLP procedure.

Selective primers MseI primers M-AAC M-CAC M-CAG M-CAT M-CTA M-CTC M-CTG M-CTT EcoRI primers E-AAC E-AAG E-ACA E-ACT E-ACC E-ACG E-AGC E-AGG

Sequence 5′-GACGATGAGTCCTGAG-3′ 5′-TACTCAGGACTCAT-3′ 5′-CTCGTAGACTGCGTACC-3′ 5′-AATTGGTACGCAGTCTAC-3′

5′-GATGAGTCCTGAGTAAC-3′ 5′-GACTGCGTACCAATTCA-3′

5′-GATGAGTCCTGAGTAACAA-3′ 5′-GATGAGTCCTGAGTAACAC-3′ 5′-GATGAGTCCTGAGTAACAG-3′ 5′-GATGAGTCCTGAGTAACAT-3′ 5′-GATGAGTCCTGAGTAACTA-3′ 5′-GATGAGTCCTGAGTAACTC-3′ 5′-GATGAGTCCTGAGTAACTG-3′ 5′-GATGAGTCC TGAGTAACTT-3′ 5′-GACTGCGTACCAATTCAAC-3′ 5′-GACTGCGTACCAATTCAAG-3′ 5′-GACTGCGTACCAATTCACA-3′ 5′-GACTGCGTACCAATTCACT-3′ 5′-GACTGCGTACCAATTCACC-3′ 5′-GACTGCGTACCAATTCACG-3′ 5′-GACTGCGTACCAATTCAGC-3′ 5′-GACTGCGTACCAATTCAGG-3′

EcoRI selective primers were labeled with FAM fluorescent labels.

Please cite this article as: Zou, K., et al., A preliminary study for identification of candidate AFLP markers under artificial selection for shell color in pearl oyster Pincta..., Gene (2014), http://dx.doi.org/10.1016/j.gene.2014.03.029

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Table 2 Pre-selective amplification. Reaction mixture

25 μl

PCR amplification

ddH2O 10× PCR buffer (+Mg2+) dNTP mixture (2 mM each) EcoRI pre-selective primer (50 ng/μl) MseI pre-selective primer (50 ng/μl) ExTaq polymerase (5 U/μl) Adapter-ligated fragments

13.2 μl 2.5 μl 3 μl 1 μl 1 μl 0.3 4 μl

94 °C 94 °C 56 °C 72 °C 72 °C

Table 4 Selective primer combinations and their respective extensions chosen (marked with √) for use in the AFLP assay. EcoRI

3 min 30 s 30 s 30 s 5 min

M-AAC

g

24 cycles

2.4. AFLP data analysis AFLP profiles were initially viewed using the ABI Genemapper v4.0 software (Applied Biosystems Inc.) and the presence (1) and absence (0) of AFLP peaks for each individual were scored for each primer combination. Only markers that occurred in at least two individuals and only clear and unambiguous peaks with fluorescence greater than or equal to 100 arbitrary units were entered into a binary data matrix for further analysis. Loci in the range of 50–300 bp length were analyzed in comparison with the internal GeneScan-500(LIZ) size standard and a 1 bp call accuracy. The 0/1 AFLP matrix was analyzed with Popgene 1.32 (Yeh and Yang, 1999). Genetic diversity was calculated by the percentage of polymorphic loci, average Nei's gene diversity (h), and Shannon's information index (I). The percentage of polymorphic loci was calculated on the basis of the number of alleles (Na). The average Nei's gene diversity (h) and Shannon's information index (I) were calculated according to effective number of alleles (Ne). Genetic differentiation (Kumla et al., 2012) was assessed by total gene diversity (HT), gene diversity within strains (HS), coefficient of differentiation (GST), Nm and the fixation index (FST). The analysis of molecular variance (AMOVA) was implemented in Arlequin 3.5 (Excoffier and Lischer, 2010). AFLP data were analyzed by genome scans of genetic-differentiation approach to identify the candidate loci under artificial selective pressure. Two different methods were being used preferentially to detect outlier loci owing to selection on AFLP (Cooke et al., 2012; Holt et al., 2011; Tice and Carlon, 2011). Firstly, data were analyzed using hierarchical analysis based on the Beaumont test (Beaumont and Balding, 2004; Beaumont and Nichols, 1996; Excoffier and Lischer, 2010; Xu et al., 2012), with modifications for co-dominant markers. We calculated FST values for all observed AFLP loci that were polymorphic in the four shell color strains by using Arlequin 3.5 with 20,000 simulations. Any locus linked to specific shell color of P. fucata would be expected to exhibit relatively high frequency disequilibrium in all samples, therefore locus with higher FST values might be considered under artificial selective pressure. Additionally, FST values of loci were expected to vary according to the degree of heterozygosity of the loci concerned. P value of FST represented the probability that the observed FST value for an observed locus could be matched or exceeded under neutral conditions. And it was appropriate for our study design that P value was less than 0.05. Observed loci with FST values above simulated quantile distributions were designated as 95% and 99% outlier loci accordingly.

Table 3 Selective amplification. Reaction mixture

10 μl

Touchdown PCR amplification

ddH2O 10× PCR buffer (+Mg2+) dNTP mixture (2 mM each) EcoRI selective primer (100 ng/μl) MseI selective primer (100 ng/μl) ExTaq polymerase (5 U/μl) Diluted pre-selective PCR product

4.92 μl 0.84 μl 0.6 μl 0.8 μl 0.8 μl 0.04 μl 2 μl

94 °C 94 °C 65 °C 72 °C 94 °C 56 °C 72 °C 72 °C

3 min 30 s 30 s 1 min 30 s 30 s 1 min 10 min

MseI

g g

−0.9 °C each cycle, 10 cycles 29 cycles

M-CAC

M-CAG

M-CAT

M-CTA

M-CTC

M-CTG

M-CTT

√

E-AAC E-AAG E-ACA E-ACT E-ACC E-ACG E-AGC E-AGG

√

√

√ √ √ √

√

Rows contain selective trinucleotide extensions attached to the 3′ end of the fluorescently labeled EcoRI primer 5′-GACTGCGTACCAATTCNNN-3′. Columns contain selective trinucleotide extensions attached to the 3′ end of the MseI primer 5′-GATGAGTCCTGACCGANNN-3′.

As a second approach to detect the potential loci under selective pressure (outlier loci), the Bayesian program was applied with Bayescan 2.01 (Foll and Gaggiotti, 2008). The Bayesian aimed to identify candidate loci under selection from genetic data. The Bayesian was considered more efficient when dealing with complex demographic scenarios because it estimated population-specific and locus-specific FST coefficients (Foll and Gaggiotti, 2008). In addition, The Bayesian was much better to avoid false positives than other methodologies (PerezFigueroa et al., 2010). This methodology allowed the use of dominant markers (such as AFLPs) and estimated the probability that specific locus was experiencing selection by directly comparing the posterior probabilities. Loci with probability values exceeded 0.7 were likely to be under directional selection, and they were noted as outlier loci (Foll and Gaggiotti, 2008). We employed a low threshold of log10 (BF) N 0.5 for the rejection of the null hypothesis in each of the conducted tests. Outlier loci that were shown in two methods were more likely to be under selection, because the two separate approaches had completely different assumptions and employed separate algorithms. The Bayesian provided an independent test of the outlier loci detected by hierarchical analysis. Those outlier loci found to be under selection in only one method were considered to be false positives. For example, the outlier loci analyzed by hierarchical analysis that the Bayesian did not find to be under selection in either comparison were false positives. 3. Results 3.1. Genetic diversity 8 AFLP primer combinations (Table 4) were selected to study the four F5 different shell color strains of P. fucata. Loci data were identified across the 128 pearl oysters. A total of 688 loci were positively scored and transferred into 0/1 matrix. 675 loci were unequivocally scored as polymorphic loci (Table 5). The number of loci generated by per primer combination ranged from 71 to 103. At the strain level, the percentages Table 5 Statistical analysis of genetic diversity in four strains of P. fucata. Strains

N

Num poly

Poly.loci (%)

Na

Ne

h

I

B G R W Overall

32 32 32 32 128

622 604 644 642 676

90.41% 87.79% 93.60% 93.31% 98.26%

1.9041 1.8779 1.9360 1.9331 1.9811

1.5549 1.4792 1.5496 1.5613 1.6041

0.3225 0.2829 0.3221 0.3292 0.3557

0.4801 0.4271 0.4825 0.4923 0.5315

Estimates for the total number of polymorphic loci (num poly) and the percentage of polymorphic loci were based on the observed number of alleles present within each strain; Nei's gene diversity and Shannon's information index were calculated according to effective number of alleles. Poly.loci = percentage of polymorphic loci; N = the sample sizes; Na = observed number of alleles; Ne = effective number of alleles; h = average Nei's gene diversity; I = Shannon's information index; overall = specieslevel value.

Please cite this article as: Zou, K., et al., A preliminary study for identification of candidate AFLP markers under artificial selection for shell color in pearl oyster Pincta..., Gene (2014), http://dx.doi.org/10.1016/j.gene.2014.03.029

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of polymorphic loci were estimated to be 87.79% (G), 90.41% (B), 93.31% (W) and 93.60% (R), with a mean of 91.28% across all individuals (Table 5). The h values of gold, red, black and white strains were 0.2829, 0.3221, 0.3225 and 0.3292, respectively, and I values of the four strains were 0.4271, 0.4825, 0.4801 and 0.4923, respectively (Table 5). Across all individuals, the h and I values were obviously higher than each strain, at 0.3557 and 0.5315 respectively (Table 5). The levels of genetic diversity in each strain were shown in Table 3. The white shell strain had higher genetic diversity (h = 0.3292 and I = 0.4923) than other color strains, and the gold shell strain was observed to have the lowest genetic diversity (h = 0.2829 and I = 0.4271). Apparently, there are differences of genetic diversity among the four strains. 3.2. Genetic differentiation The values of HT and HS of each primer combination were shown in Table 6. The values of HT and HS of all primers for total samples were 0.3557 and 0.3142, respectively. The maximum value of GST, 0.2085, was generated by the primer pair E-AGG/M-CAG and the minimum value, 0.0713, by the primer pair of E-ACG/M-CTA. The average value of GST across all individuals for each primer was 0.1139. The value of Nm for each primer combination ranged from 1.8981 (E-AGG/M-CAG) to 6.5133 (E-ACG/M-CTA) with mean value of 4.3068. The value of Nm across all individuals was 3.7813 (Table 6). In addition, the analysis of molecular variance (AMOVA) was performed to study the four strains of P. fucata and to estimate the genetic differentiation within strain and among strains (Table 7). All fixation indices within strain and among strains were significant (P b 0.001). The differentiation among strains (total FST) was 18.05% (Table 7). 3.3. Outlier loci analysis Hierarchical analysis revealed that 15 loci fell within the 95–99% FST level and 10 loci fell outside the 99% FST level (Fig. 3). Heterozygosity and P values of the outlier loci were shown in Table 6. In consideration of P values, a total of 21 loci fulfilled the criteria as outlier loci, 13 of them fell within the 95–99% FST level and 8 of them fell outside the 99% FST level (Table 8). Then, nine loci were detected as outlier loci by the Bayesian method with log10 (BF) N 0.5 and probability N 0.7 among populations (Table 8, Fig. 4). Finally, only six of the outlier loci (Table 8) were identified as the candidate markers being under selection by a combination of both methods (the hierarchical analysis with Arlequin 3.5 and the Bayesian approach using Bayescan 2.01). The other eighteen outliers were only identified in one analysis method and considered as false positives (Table 8). Taking into account the probability for six outlier loci in different shell color strains, locus-511, locus-537 and locus-566 were at possibility of 0.91, 0.97, and 0.94 in red shell strain, but locus-538 and locus-567 were at possibility of 0.13 and 0.03 in red shell strain. Locus-518 was at possibility of 0.91 in black shell strain (Table 9). Table 6 Genetic differentiation based on statistical analysis of AFLP data. Primer combinations

HT

HS

GST

Nm

E-AAC/M-CTA E-AAG/M-CAT E-AAG/M-CTC E-ACA/M-CAG E-ACT/M-CAC E-ACG/M-CTA E-AGG/M-CAG E-AGG/M-CTG Mean Overall

0.3600 0.3374 0.3488 0.3431 0.3254 0.3800 0.3723 0.3725 0.3549 0.3557

0.3227 0.2924 0.3084 0.3100 0.2956 0.3530 0.2947 0.3387 0.3144 0.3142

0.1036 0.1332 0.1160 0.0965 0.0916 0.0713 0.2085 0.0908 0.1139 0.1168

4.3277 3.2526 3.8116 4.6828 4.9600 6.5133 1.8981 5.0080 4.3068 3.7813

HT = total Nei's gene diversity; HS = Nei's gene diversity within strain; GST = coefficient of differentiation and Nm = 0.5(1 − GST) / GST; mean = average value for primer combinations; overall = species level value.

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4. Discussion 4.1. Genetic diversity In contrast to the wild populations, the reduction of genetic diversity in cultured strains was demonstrated in a few species such as Haliotis discus (Li et al., 2007), Crassostrea ariakensis (Xiao et al., 2011), and Crassostrea gigas (Miller et al., 2012). However, some species had a high or higher level of genetic diversity under artificial selection (Gawenda et al., 2012; Peter et al., 2007; Yu and Chu, 2006a,b). The estimates of genetic diversity confirmed that there was a higher degree of genetic diversity present within breeds from the center of domestication (black pearl oyster (Durand et al., 1993); cattle (Loftus et al., 1999); and sheep (Peter et al., 2007)). Previous studies also demonstrated that the selected populations of P. fucata did not significantly change in genetic diversity compared with the corresponding wild populations (Yu and Chu, 2006a,b). In this study, the different shell color strains of P. fucata still maintained a high level of genetic diversity (Table 5) after five successive generation selection. The average Nei's gene diversity and Shannon's Information index across all individuals still reached 0.3557 and 0.5315 (Table 5). The high level of genetic diversity is beneficial for selective breeding and helps avoid inbreeding depression, as lower genetic diversity will limit response to selection and may lead to lowering of fitness (Amos and Balmford, 2001). Thus the conservation of genetic diversity is important for the long-term interest of any species. P. fucata was found to possess high genetic diversity at species level but lower genetic diversity at strain level (Table 5). If a population was isolated by contributing factors, such as small population size, the severity of artificial selection and time since isolation, genetic diversity within population could be adversely influenced (Wilkinson, 2011). In the present study, an ancestral population was separated into four shell color strains by the strict breeding. Each strain carried a subset of the genetic diversity from the same ancestral population. Consequently, the lower levels of genetic diversity in each cultured strain mainly resulted from artificial selection, especially artificial directional selection during the process of cultivation. 4.2. Genetic differentiation Average genetic difference among strains (HT − HS) was 0.0415, and GST value across all individuals in P. fucata was 0.1168 (Table 6). The average genetic difference among strains and GST value was higher than the previous studies on cultured P. fucata (Yu and Chu, 2006a,b). It might indicate that there was genetic differentiation under the successive generation selection for shell colors. Reproductive isolation was artificially promoted in the POSBP, contributing to genetic differentiation. Similar to it, strict breeding practices were likely in place to isolate and preserved phenotypically distinct sets of chicken and pig breeds, resulting in high levels of breed genetic differentiation (Bodzsar et al., 2009; Sancristobal et al., 2006). The results of genetic differentiation were confirmed by AMOVA test. Wright (1978) suggested qualitative measures for explaining the degree of genetic differentiation (FST). The range of FST values from 0 to 0.05 might be considered as little genetic differentiation, while the ranges from 0.05 to 0.15 considered as moderate. Besides, the ranges from 0.15 to 0.25 and values of above 0.25 considered as great and very great genetic differentiation, respectively. In this study, FST among cultured strains of P. fucata was 0.1805 (Table 7), which suggested that five successive generation selection resulted in great genetic differentiation among four different shell color strains. It is well accepted that high genetic differentiation among cultured strains is due to positive selection, in particular, strong artificial selection (Barker et al., 2009). The dog with extremely high levels of differentiation among breeds has been observed, possibly due to even stricter enforced breeding practices for favorable morphological traits, thus creating breed barriers between breeds (Parker et al., 2004).

Please cite this article as: Zou, K., et al., A preliminary study for identification of candidate AFLP markers under artificial selection for shell color in pearl oyster Pincta..., Gene (2014), http://dx.doi.org/10.1016/j.gene.2014.03.029

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Table 7 Analysis of molecular variance (AMOVA) for P. fucata based on AFLP data. Source of variation Among strains Within strain Overall Fixation index (total FST):

d.f. 3 124 127 0.1805

SS 2664.055 13,685.594 16,349.648

Table 8 Outlier loci detected by hierarchical analysis and Bayesian approach.

Variance components

Percentage of variation

24.302 110.368 134.669

18.05% 81.95%

Locus

d.f.: degree of freedom; SS: sum of squares (Significant at P b 0.001.)

These results contributed to the continuing ability of culturists to select for shell colors. Gene flow, the movement of gene within and between populations, is negatively correlated with genetic differentiation (Song et al., 2010). In the POSBP, strict reproductive isolation could completely block the gene flow, which explained the high levels of differentiation among shell color strains. Simultaneously, Nm was 3.7813 based on GST. The reason for the Nm might be that individuals in each shell color strain were from a same ancestral population of P. fucata. These could explain the high level of genetic diversity with strains and great genetic differentiation among strains during the artificial breeding. 4.3. Outlier loci under successive generation selection for shell colors

Bayesian P

Prob

Levels

He

Ho

Obs.FST

99% 95%

0.44 0.45

0.53 0.51

0.68 0.49

0.002 0.026

95% 95% 95%

0.37 0.41 0.20

0.43 0.48 0.22

0.60 0.59 0.42

0.024 0.011 0.040

95% 95% 95% 99% 99% 95%

0.47 0.47 0.46 0.46 0.47 0.34

0.53 0.54 0.52 0.54 0.55 0.38

0.48 0.51 0.51 0.60 0.63 0.51

0.038 0.026 0.021 0.006 0.006 0.042

95% 95% 99% 95% 99% 99% 95% 99% 99% 95%

0.49 0.49 0.41 0.43 0.38 0.39 0.32 0.35 0.44 0.34

0.56 0.57 0.49 0.49 0.49 0.51 0.37 0.45 0.51 0.40

0.55 0.57 0.71 0.52 0.94 0.97 0.57 0.90 0.56 0.55

0.042 0.032 0.002 0.015 b0.001 b0.001 0.030 b0.001 0.008 0.034

log10(BF)

FST

0.94

1.19

0.38

0.78

0.54

0.29

0.87 0.94

0.81 1.19

0.33 0.37

1.00

1000

0.45

0.96 0.95

1.38 1.28

0.38 0.36

1.00 1.00

3.40 1000

0.46 0.54

He = expected heterozygosity; Ho = observed heterozygosity; prob = probability; P = P value of FST.

Putative outlier loci might be false positives (Campbell and Bernatchez, 2004), so two genetic-differentiation approaches have been employed to minimize the false positives (Vasemagi and Primmer, 2005). Firstly, the hierarchical analysis identified 13 outlier loci fell within the 95–99% FST level and 8 outlier loci fell outside the 99% FST level (Fig. 3, Table 8). These loci represented the best candidate markers for further analysis and provided some suggestion that there could be genetic differentiation among different shell color strains. Subsequently, the Bayesian method was used to identify nine outlier loci in multiple population comparisons (Fig. 4, Table 8). Finally, these results were comprehensively considered and six outlier loci were identified to represent truly selective loci in four different shell color strains of P. fucata. On the basis of the Jeffreys (1961) scale of evidence (Cooke et al., 2012), loci of 566, 567 were supported with log10 (BF) N 2, which is considered as the ‘decisive’ evidence for selection, loci of 518,

FST

F5 different shell color strains of P. fucata in this study was under artificial selection, we expected that a strong marker in the data should be picked up by detecting outlier loci in a number proportional to the true percentage of selective loci existing in the genome. AFLP has been successfully used to obtain outlier loci selective pressure, especially suitable for species in which no other genetic information was available (Campbell and Bernatchez, 2004; Cooke et al., 2012; Coyer et al., 2011; Mattersdorfer et al., 2012). Among 688 AFLP loci, genome scans of genetic-differentiation approach detected that six outlier loci could be considered as candidates for artificial selection (Table 8). The results suggested that only a small proportion of loci might have been under the effect of artificial selection for shell colors. The high level of observed phenotypic variation among cultured P. fucata was a result of both neutral demographic processes, and strong short-term artificial selection for divergent breeding goals. The ability to detect outlier loci with a genome scan under selection was related to the strength of selection and population history (Tice and Carlon, 2011). Thus the small proportion of outlier loci was probably due to short artificial selection history in the four different shell color strains.

15 64 77 157 161 227 258 285 333 402 511 518 526 527 534 537 538 544 566 567 571 579 679 688

Hierarchical analysis

Heterozygosity Fig. 3. FST values for AFLP loci detected by hierarchical analysis. Arlequin 3.5 plot of 675 polymorphic AFLP loci in the genome scan analysis of 128 individuals from four shell color strains (black, gold, red and white shell). FST is plotted against heterozygosity. Dashed line represents the simulated 95% FST level, dotted line represents the simulated 99% FST level. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

Please cite this article as: Zou, K., et al., A preliminary study for identification of candidate AFLP markers under artificial selection for shell color in pearl oyster Pincta..., Gene (2014), http://dx.doi.org/10.1016/j.gene.2014.03.029

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7

Acknowledgments

FST

We are grateful to all the laboratory members for their technical advice and helpful discussions. We wish to acknowledge the contributions of members from the South China Sea Fisheries Research Institute in Xincun Village, Hainan Province, China for collecting pearl oyster samples. This research was supported by National Infrastructure of Fishery Germplasm Resources Project, Marine Fisheries Science and Technology Promotion Project of Guangdong Province (A201301A09, A201201A08).

References

Probability Fig. 4. FST values for AFLP loci analyzed by Bayesian approach. Bayescan 2.01 plot of 675 polymorphic AFLP loci in the genome scan analysis of 128 individuals from four shell color strains (black, gold, red and white shell). FST is plotted against the probability of Bayes factors. Loci with probability N 0.7 and log10(BF) N 0.5 are under selection. The vertical line denotes the threshold used for identifying outlier loci. There are two loci whose log10(BF) = 1000 was not shown in this figure. The seven loci on the right side of the vertical line and two not shown loci are candidates for being under selection.

537, and 538 were supported with log10 (BF) N 1, which is considered as the ‘strong’ evidence for selection and locus of 511 was supported with log10 (BF) N 0.5, which is considered as the ‘substantial’ (Table 8). Our FST values derived from the two different methods (Table 8), suggested that selective pressure acting on a limited number of loci was indirectly resulting in low levels of significant isolation in other parts of the P. fucata genome. Previous studies have suggested that shell color and shell pigmentation in shellfish species might be associated with genetic factor in some cases (Guan et al., 2011; Liu et al., 2009; Yuan et al., 2012). After the five successive generation selection for shell color, we identified locus-566 at possibility of 0.94 in red shell strain, whereas at possibility of 0.06 in white shell strain and no possibility in black or gold shell strain (Table 9). It indicated that locus-566 was much more likely to be found in red shell strain of P. fucata genome. The same analysis for other outlier loci revealed that locus-511, locus-537, locus-538 and locus-567 were extremely unlikely to be found in red shell strain, and locus-518 was much more likely to be discovered in black shell strain of P. fucata genome. However, AFLP technique in combination with genome-scans analysis was a robust approach for the identification of a color linked marker in P. fucata. The present study is a fundamental step to understand the genetic factor of shell colors, and detect the potential genetic markers under artificial selection in P. fucata. Further studies could incorporate more methods (i.e. QTL mapping) to test and validate the present results.

Conflict of interest The authors report no conflicts of interest.

Table 9 Probability for six outlier loci in different shell color strains based on AFLP data. Strains

Locus-511

Locus-518

Locus-537

Locus-538

Locus-566

Locus-567

B G R W

0.31 0 0.91 0.22

0.91 0.09 0.50 0

0.34 0.13 0.97 0.16

0.84 0.97 0.13 0.94

0 0 0.94 0.06

0.97 1 0.03 0.97

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