Advertisement
Research Article

Identification of Transcriptome-Derived Microsatellite Markers and Their Association with the Growth Performance of the Mud Crab (Scylla paramamosain)

  • Hongyu Ma,

    Affiliations: East China Sea Fisheries Research Institute, Chinese Academy of Fishery Sciences, Shanghai, China, Key Laboratory of East China Sea and Oceanic Fishery Resources Exploitation, Ministry of Agriculture, Shanghai, China, Yellow Sea Fisheries Research Institute, Chinese Academy of Fishery Sciences, Qingdao, China

    X
  • Wei Jiang,

    Affiliations: East China Sea Fisheries Research Institute, Chinese Academy of Fishery Sciences, Shanghai, China, Key Laboratory of East China Sea and Oceanic Fishery Resources Exploitation, Ministry of Agriculture, Shanghai, China

    X
  • Ping Liu,

    Affiliation: Yellow Sea Fisheries Research Institute, Chinese Academy of Fishery Sciences, Qingdao, China

    X
  • Nana Feng,

    Affiliations: East China Sea Fisheries Research Institute, Chinese Academy of Fishery Sciences, Shanghai, China, Key Laboratory of East China Sea and Oceanic Fishery Resources Exploitation, Ministry of Agriculture, Shanghai, China

    X
  • Qunqun Ma,

    Affiliations: East China Sea Fisheries Research Institute, Chinese Academy of Fishery Sciences, Shanghai, China, Key Laboratory of East China Sea and Oceanic Fishery Resources Exploitation, Ministry of Agriculture, Shanghai, China

    X
  • Chunyan Ma,

    Affiliations: East China Sea Fisheries Research Institute, Chinese Academy of Fishery Sciences, Shanghai, China, Key Laboratory of East China Sea and Oceanic Fishery Resources Exploitation, Ministry of Agriculture, Shanghai, China

    X
  • Shujuan Li,

    Affiliations: East China Sea Fisheries Research Institute, Chinese Academy of Fishery Sciences, Shanghai, China, Key Laboratory of East China Sea and Oceanic Fishery Resources Exploitation, Ministry of Agriculture, Shanghai, China

    X
  • Yuexing Liu,

    Affiliations: East China Sea Fisheries Research Institute, Chinese Academy of Fishery Sciences, Shanghai, China, Key Laboratory of East China Sea and Oceanic Fishery Resources Exploitation, Ministry of Agriculture, Shanghai, China

    X
  • Zhenguo Qiao,

    Affiliations: East China Sea Fisheries Research Institute, Chinese Academy of Fishery Sciences, Shanghai, China, Key Laboratory of East China Sea and Oceanic Fishery Resources Exploitation, Ministry of Agriculture, Shanghai, China

    X
  • Lingbo Ma mail

    malingbo@vip.sina.com

    Affiliations: East China Sea Fisheries Research Institute, Chinese Academy of Fishery Sciences, Shanghai, China, Key Laboratory of East China Sea and Oceanic Fishery Resources Exploitation, Ministry of Agriculture, Shanghai, China

    X
  • Published: February 13, 2014
  • DOI: 10.1371/journal.pone.0089134

Abstract

Microsatellite markers from a transcriptome sequence library were initially isolated, and their genetic variation was characterized in a wild population of the mud crab (Scylla paramamosain). We then tested the association between these microsatellite markers and the growth performance of S. paramamosain. A total of 129 polymorphic microsatellite markers were identified, with an observed heterozygosity ranging from 0.19 to 1.00 per locus, an expected heterozygosity ranging from 0.23 to 0.96 per locus, and a polymorphism information content (PIC) ranging from 0.21 to 0.95 per locus. Of these microsatellite markers, 30 showed polymorphism in 96 full-sib individuals of a first generation family. Statistical analysis indicated that three microsatellite markers were significantly associated with 12 growth traits of S. paramamosain. Of these three markers, locus Scpa36 was significantly associated with eight growth traits, namely, carapace length, abdomen width (AW), body height (BH), fixed finger length of the claw, fixed finger width of the claw, fixed finger height of the claw, meropodite length of pereiopod 2, and meropodite length of pereiopod 3 (MLP3) (P<0.05). Locus Scpa75 was significantly associated with five growth traits, namely, internal carapace width, AW, carapace width at spine 8, distance between lateral spine 2 (DLS2), and MLP3 (P<0.05). Locus Spm30 was significantly associated with BH, DLS2, and body weight (P<0.05). Further analysis suggested a set of genotypes (BC at Scpa36, BC and BD at Scpa75, and AC at Spm30) that have great potential in the selection of S. paramamosain for growth traits. These findings will facilitate the development of population conservation genetics and molecular marker-assisted selective breeding of S. paramamosain and other closely related species.

Introduction

The mud crab (Scylla paramamosain) is a highly commercially valuable species, mainly distributed along the southeastern coasts of China and other Asian countries such as Japan, Vietnam, and the Philippines. S. paramamosain is an important aquaculture and capture marine species in China. Records of S. paramamosain aquaculture date back more than 100 years in China [1] and more than 30 years in other Asian countries [2]. Adult S. paramamosain mate inshore and the gravid females generally migrate offshore to spawn eggs [3]. S. paramamosain has received increasing attention over the years and has been cultured by fishermen because of its wonderful flavor and fast growth rate. The aquaculture production in China reached 110,000 tons in 2011 [4]. However, this production scale does not meet the market demand. An artificial selective breeding program has been launched to develop one or several novel strains with higher economically valuable traits, such as faster growth rates, better flavor, and higher disease resistance.

DNA markers are useful for the assisted breeding of aquacultured organisms. A microsatellite-based parentage assignment technique was successfully developed for S. paramamosain, and its success rate for assigning progeny to real parents reaches 95% [5]. Several types of genetic markers, including microsatellites [6][9], SNP [10], complete mitochondrial DNA [11], and AFLP [12] have been developed to assist in the improvement and enhancement of the economically important traits of S. paramamosain, and the genetic diversity and structure of wild and cultured populations have also been investigated [13], [14]. However, information on the application of molecular technology in the assisted breeding of S. paramamosain is limited.

Microsatellites are nuclear genetic markers that are considered an ideal molecular marker system for investigating genetic diversity [15], constructing genetic maps [16], [17], and marker-assisted selection (MAS) [18]. MAS has become a hot topic for aquacultured organisms in recent years, and it can help genetically improve the species by approximately 25% to 50% compared with traditional artificial selective breeding techniques [19]. MAS mainly focuses on selecting target genotypes rather than phenotypes, and it is carried out during the early development of organisms. Few studies have been performed on aquacultured animals. For example, a microsatellite marker is identified to correlate with the disease resistance of a population of giant black tiger shrimp (Penaeus monodon) [20]. Four SNPs in giant freshwater prawn (Macrobrachium rosenbergii) are significantly associated with three growth traits (body weight, carapace length, and standard length) [21]. A lymphocystis disease-resistant population of Japanese flounder (Paralichthys olivaceus) has been developed using microsatellite-assisted selection [18]. Economically important traits associated molecular markers need to be identified to develop better strains of aquatic organisms.

We recently constructed first generation families of S. paramamosain and investigated the correlation of different growth traits [22]. In the current study, we initially isolated polymorphic microsatellite markers from a transcriptome sequence library and estimated their genetic variation levels in a wild population. Then, we assessed the association between these markers and the growth performance of this important crab species. This study aims to provide references for population conservation genetics and molecular MAS breeding in S. paramamosain and other closely related species.

Materials and Methods

Ethics Statement

All animal experiments in this study were conducted according to relevant national and international guidelines. This project was approved by East China Sea Fisheries Research Institute. In China, catching wild mud crab from seawater does not require specific permits. This study does not involve endangered or protected species.

Sample Collection and Growth Trait Measurement

A wild population of 32 S. paramamosain was collected from the coastal waters along Wenchang City, China in September 2011. This population was used to evaluate the polymorphism of transcriptome-derived microsatellite markers. A first generation (G1) family of S. paramamosain was bred in June 2012 and cultured on Hainan Island, China. The G1 crabs were all cultured in the same pond to maintain them under the same environmental condition. A total of 96 full-sib individuals approximately three months of age were randomly collected from the G1 family in September 2012. The average weight of these individuals is 82.47 g.

Sixteen growth traits of the 96 full-sib individuals were measured according to Keenan et al. [23] and Gao et al. [24]. These traits included carapace length (CL), carapace width (CW), internal carapace width (ICW), carapace frontal width (CFW), abdomen width (AW), body height (BH), carapace width at spine 8 (CWS8), distance between lateral spine 1 (DLS1), distance between lateral spine 2 (DLS2), fixed finger length of the claw (FFLC), fixed finger width of the claw (FFWC), fixed finger height of the claw (FFHC), meropodite length of pereiopod 1 (MLP1), meropodite length of pereiopod 2 (MLP2), meropodite length of pereiopod 3 (MLP3), and body weight (BW). These 15 morphologic traits were measured to the nearest 0.01 mm using Vernier calipers. Body weight was measured to an accuracy of 0.01 g using a digital electronic balance.

Genomic DNA Extraction

Genomic DNA was extracted from the muscle tissues of 32 wild and 96 full-sib individuals following the traditional proteinase K and phenol–chloroform extraction method as described by Ma et al. [25]. The concentration of DNA was adjusted to 100 ng/µl, and DNA was stored at −20°C until used.

Microsatellite Marker Development and Evaluation

In our previous study, we carried out 454 high-throughput pyrosequencing on a mixed cDNA library of S. paramamosain from four tissues (muscle, hepatopancreas, eyestalk, and blood) of 12 individuals (the manuscript is being prepared). A total of 540 Mbp of data was produced, and 78,268 unigenes were obtained through sequence similarity with known proteins (E ≤0.00001) in the UniProt and non-redundant (NR) protein databases. A total of 19,011 microsatellites were identified from unigenes using the MISA software under default settings. In the present study, we designed primers for the microsatellite sequences based on the following criteria: a minimum of eight repeats for dinucleotide, trinucleotide, and tetranucleotide repeats and sufficient flanking regions using the software Primer Premier 5.0.

We used a single population of 32 wild individuals to evaluate the polymorphism of the microsatellite markers derived from the transcriptome. Polymerase chain reaction (PCR) was performed using a 12.5 µl total volume that contained 0.4 µM each primer, 0.2 mM each dNTP, 1× PCR buffer, 1.5 mM MgCl2, 0.4 units of Taq polymerase (TianGen Biotech Co., Ltd, ET101), and approximately 50 ng of DNA. The following conditions were used for the PCR: 1 cycle of denaturation at 94°C for 4 min and 30 cycles of 30 s at 94°C, 50 s at a primer-specific annealing temperature (Table S1), and 50 s at 72°C. In the final step, the products were extended for 7 min at 72°C. The PCR products were separated on 6% denaturing polyacrylamide gel and visualized via silver staining. The allele size was estimated according to the pBR322/Msp I marker (TianGen Biotech Co., Ltd, MD206).

Identification of Growth Traits Associated Microsatellite Markers

All 129 polymorphic microsatellite markers developed from the transcriptome were genotyped in 96 individuals of the G1 family, with 30 loci exhibiting polymorphism. We tested the association between the 30 microsatellite loci and 16 growth traits (i.e., CL, CW, ICW, CFW, AW, BH, CWS8, DLS1, DLS2, FFLC, FFWC, FFHC, MLP1, MLP2, MLP3, and BW). The PCRs and electrophoresis were conducted as described above.

Statistical Analysis

The genetic diversity indices of the microsatellites were calculated using the software POPGENE version 1.31 [26], including the observed number of alleles (Na), effective number of alleles (Ne), observed heterozygosity (HO), expected heterozygosity (HE), polymorphism information content (PIC), Chi-square tests for Hardy–Weinberg equilibrium (HWE), and linkage disequilibrium (LD). Significance values for all multiple tests were corrected through sequential Bonferroni procedure [27].

The associations between microsatellite markers and growth traits (CL, CW, ICW, CFW, AW, BH, CWS8, DLS1, DLS2, FFLC, FFWC, FFHC, MLP1, MLP2, MLP3, and BW) were tested using the General Linear Model (GLM) procedure in the software SPSS version 19. A linear animal model with the fixed effects was used as follows: Yijk = μ+Gi+Sj+eijk, where Yijk is the observed value of the ijkth trait; μ is the mean value of the trait; Gi is the effect of the ith genotype; Sj is the effect of the jth sex; and eijk is the random error effect. All G1 individuals used in the association analysis were derived from the same family, cultured under the same conditions, and collected at the same age, so other effects such as batch, generation, family, age, and site were not considered in the statistical model. Significant differences in growth traits among the different genotypes were calculated through multiple comparison analysis using the S-N-K method. Differences with P values of 0.05 were considered statistically significant.

Results

Characterization of Transcriptome-derived Microsatellite Markers

A total of 563 pairs of primers were successfully designed based on the transcriptome-derived microsatellite sequences. Of these primer pairs, 129 showed polymorphism in a wild population of 32 individuals (Table S1), whereas others exhibited monomorphism, smears, or absence of products. The number of alleles per locus ranged from 2 to 27 (mean = 7), the observed heterozygosity per locus ranged from 0.19 to 1.00 (mean = 0.68), the expected heterozygosity per locus ranged from 0.23 to 0.96 (mean = 0.70), and the PIC per locus ranged from 0.21 to 0.95 (mean = 0.66). Nineteen loci significantly deviated from the HWE after Bonferroni correction (P<0.00039), and all loci exhibited no evidence of stuttering and allelic dropout. Furthermore, no significant LD was found in all pairs of loci.

Genetic Variation in the G1 Population

Of the 129 polymorphic microsatellite loci, 30 showed polymorphism in the 96 individuals of the G1 family (Table 1), whereas others exhibited monomorphism. A total of 85 alleles were detected, with an average of 2.8 per locus. The observed heterozygosity per locus ranged from 0.25 to 1.00 with an average of 0.74, the expected heterozygosity per locus ranged from 0.22 to 0.75 with an average of 0.56, and the PIC per locus ranged from 0.22 to 0.70 with an average of 0.49. Up to 18 loci demonstrated high polymorphism (PIC >0.5), 9 loci demonstrated intermediate polymorphism (0.25<PIC <0.5), and 3 loci demonstrated low polymorphism (PIC <0.25). No significant LD was detected in all loci pairs.

thumbnail

Table 1. Genetic variation of 30 microsatellite markers in G1 family of S. paramamosain.

doi:10.1371/journal.pone.0089134.t001

Association between Microsatellite Loci and Growth Traits

Of the 30 polymorphic microsatellite loci in the G1 family, Scpa36, Scpa75, and Spm30 were significantly associated with 12 of the 16 growth traits of S. paramamosain. Locus Scpa36 was significantly associated with CL, AW, BH, FFLC, FFWC, FFHC, MLP2, and MLP3 (P<0.05). At this locus (Table 2), the individuals with genotype BC exhibited the highest phenotypic values for these eight growth traits. Multiple comparisons analysis showed that the individuals with genotype BC grew significantly faster than those with genotype AB in terms of CL, AW, FFLC, FFHC, and MLP2 (P<0.05). The individuals with genotype BC showed a significantly faster growth rate than those with genotypes AB and BB in terms of BH (P<0.05). The individuals with genotypes BC and AC grew significantly faster than AB individuals in terms of FFWC (P<0.05). However, the individuals with genotype BC grew significantly faster than those with AB and BB in terms of MLP3 (P<0.05). Meanwhile, the individuals with genotype AC grew significantly faster than those with AB in terms of MLP3 (P<0.05).

thumbnail

Table 2. Association analysis between microsatellite locus Scpa36 and eight growth traits of S. paramamosain.

doi:10.1371/journal.pone.0089134.t002

Locus Scpa75 was significantly associated with traits ICW, AW, CWS8, DLS2, and MLP3 (P<0.05). At this locus (Table 3), the individuals with genotype BC exhibited the highest phenotypic values for ICW, CWS8, DLS2, and MLP3. Multiple comparisons analysis showed that the individuals with genotype BC grew significantly faster than those with AD in terms of DLS2 (P<0.05). By contrast, no significant differences in phenotypic values were detected between genotype pairs in terms of ICW, AW, CWS8, and MLP3 (P>0.05) at this locus.

thumbnail

Table 3. Association analysis between microsatellite locus Scpa75 and five growth traits of S. paramamosain.

doi:10.1371/journal.pone.0089134.t003

Locus Spm30 was significantly associated with BH, DLS2, and BW (P<0.05). At this locus (Table 4), the individuals with genotype AC exhibited the highest phenotypic values for BH, DLS2, and BW. Multiple comparisons analysis showed that the individuals with genotypes AC and BD grew significantly faster than those with CD in terms of BH and DLS2 (P<0.05). The individuals with genotypes AC, BD, and AB grew significantly faster than those with genotype CD in terms of BW (P<0.05).

thumbnail

Table 4. Association analysis between microsatellite locus Spm30 and three growth traits of S. paramamosain.

doi:10.1371/journal.pone.0089134.t004

No microsatellite marker was significantly associated with CW, CFW, DLS1, and MLP1 (P>0.05).

Microsatellite Markers with the Maximum Potential for Growth Performance Breeding

Of the 16 growth traits, AW was significantly associated with Scpa36 and Scpa75. At locus Scpa36, the average AW (27.08 mm) of the individuals with genotype BC (N = 21) was much higher than that (24.92 mm) of the individuals with the other three genotypes (N = 63). At locus Scpa75, the average AW (26.85 mm) of the individuals with genotype BD (N = 24) was much higher than that (24.86 mm) of the individuals with the other three genotypes (N = 63). Therefore, genotype BC at locus Scpa36 has greater potential in selecting for AW than BD at locus Scpa75.

Trait BH was significantly associated with loci Scpa36 and Spm30. At locus Scpa36, the average BH (32.21 mm) of the individuals with genotype BC (N = 21) was much higher than that (29.43 mm) of the individuals with the other three genotypes (N = 63). At locus Spm30, the average BH (30.96 mm) of the individuals with genotypes AC and BD (N = 54) was much higher than that (28.59 mm) of the individuals with the other two genotypes (N = 42). Therefore, genotype BC at locus Scpa36 would have a more important role in selecting for BH than AC and BD at locus Spm30.

Trait DLS2 was significantly associated with loci Scpa75 and Spm30. At locus Scpa75, the average DLS2 (48.69 mm) of the individuals with genotype BC (N = 14) was much higher than that (45.43 mm) of the individuals with the other three genotypes (N = 73). At locus Spm30, the average DLS2 (47.38 mm) of the individuals with genotypes AC and BD (N = 54) was much higher than that (43.64 mm) of the individuals with the other two genotypes (N = 42). This result shows that genotype BC at locus Scpa75 is better in selecting for DLS2 than AC and BD at locus Spm30.

MLP3 was significantly associated with loci Scpa36 and Scpa75. At locus Scpa36, the average MLP3 (25.75 mm) of the individuals with genotype BC (N = 21) was much higher than that (23.29 mm) of the individuals with the other three genotypes (N = 63). At locus Scpa75, the average MLP3 (25.51 mm) of the individuals with genotype BC (N = 14) was much higher than that (23.64 mm) of the individuals with the other three genotypes (N = 73). Therefore, genotype BC at locus Scpa36 is more useful in selecting for MLP3 than BC at locus Scpa75.

CL, FFLC, FFWC, FFHC, MLP2, ICW, CWS8, and BW were significantly associated with one microsatellite locus. CL, FFLC, FFWC, FFHC, and MLP2 were significantly associated with locus Scpa36. At this locus, the phenotypic values of these five growth traits (54.46 mm, 52.37 mm, 14.39 mm, 21.01 mm, and 30.45 mm, respectively) of the individuals with genotype BC were much higher than those of the individuals with the other three genotypes (50.20 mm, 47.07 mm, 13.03 mm, 18.67 mm, and 27.59 mm, respectively). Therefore, genotype BC at microsatellite locus Scpa36 is useful in selecting for these five growth traits. ICW and CWS8 were significantly associated with locus Scpa75. At this locus, the phenotypic values of the two growth traits of the individuals with genotypes BC and BD (77.10 mm and 79.46 mm, respectively) were much higher than those of the individuals with the other two genotypes (71.49 mm and 73.68 mm, respectively). Therefore, these two genotypes (BC and BD) at locus Scpa75 have great potential in selecting for ICW and CWS8. BW was significantly associated with locus Spm30, and its phenotypic value (92.99 mm) of the individuals with genotype AC was much higher than that (78.74 mm) of the individuals with the other three genotypes. Therefore, genotype AC at locus Spm30 could be useful in selecting for BW in breeding programs.

Discussion

Next-generation sequencing has recently been used to discover microsatellite markers and SNPs in aquaculture species [28][30], and is considered a time-saving, highly efficient approach. Microsatellite markers for the mud crab (S. paramamosain) have been reported, but most of them are randomly derived from genomic DNA [6][9] and information on known genes is unavailable. This study first reported a large number of transcriptome-level polymorphic microsatellite markers in S. paramamosain, which have several advantages compared with DNA-derived microsatellites, such as their correlation with potential known genes, better transferability among different species, and better suitability for comparative mapping and genomic studies [31].

Type I microsatellite markers are predicted to be relatively less polymorphic than those derived from genomic DNA [32], [33]. The genetic diversity indices of the microsatellites isolated in this study (average Na of 7.00 and HO of 0.68) are nearly equal to those isolated from genes (average Na of 5.90 and HO of 0.67) [34]. These indices are slightly lower than those discovered from genomic DNA in our previous study (average Na of 6.80 and HO of 0.76) [9]. A similar phenomenon has been observed in the Pacific oyster (Crassostrea gigas) [35]. By contrast, the genetic variation in type I microsatellites is reportedly higher than that of type II markers in the silver crucian carp (Cyprinus carpio L.) [36].

Of the 129 microsatellite loci, 30 showed polymorphism in the G1 family, whereas the others were monomorphic. A total of 78 effective alleles were detected at 30 loci, which is nearly equal to the observed number of alleles (85). This finding indicates that the alleles in the G1 family are uniform distributed, and the variation of microsatellite loci is not significantly affected by selection pressure [37]. In addition, a considerably high genetic diversity was detected in this family (average HO of 0.74 and average PIC of 0.49), which is similar to that found in other G1 families in a previous study (HO ranging from 0.46 to 0.85 and PIC ranging from 0.40 to 0.77) [13], albeit slightly lower than that detected in wild populations (average HO of 0.76 and PIC of 0.67) [8].

Scientists have identified a set of microsatellite markers that were significantly associated with growth traits and other economically important traits in aquatic animals [37][39]. In the present study, we first analyzed the association between microsatellite markers and the growth traits of S. paramamosain, and tried to identify potential markers for molecular MAS. Among the 30 microsatellite markers used in association analysis, Scpa36, Scpa75, and Spm30 were confirmed to be significantly associated with 12 of the 16 growth traits of S. paramamosain. All three loci showed high polymorphism in the G1 family, with the observed number of alleles ranging from 3 to 4 per locus and the PIC ranging from 0.56 to 0.70 per locus. The loci with low polymorphism (alleles lower than three and PIC lower than 0.5) were not associated with any growth traits (P>0.05). This phenomenon indicates that the lowly polymorphic microsatellite loci have less advantage in association analysis than the highly polymorphic loci. A similar trend was found in other aquatic animals such as GIFT (genetically improved farmed tilapia species in China), the common carp (Cyprinus carpio L.), and the Japanese scallop (Patinopecten yessoensis) [38][40].

If a microsatellite marker is closely linked to a phenotypic trait, it would be detected in terms of a significant association according to the theory of linkage disequilibrium [41]. Four QTLs and two microsatellite markers were identified to be significantly associated with growth traits of the half-smooth tongue sole (Cynoglossus semilaevis) through the mapping of high-density genetic maps [17]. Eleven significant QTLs related with growth traits and the microsatellite markers associated with these QTLs have been identified in turbot (Scophthalmus maximus) [42]. Our study indicates that the microsatellite markers Scpa36, Scpa75, and Spm30 are probably in LD with the QTLs of growth traits of S. paramamosain, even though we did not conduct a linkage analysis. The linkage between these markers and QTLs should be confirmed and their genetic distances should be determined through mapping on genetic linkage maps in subsequent studies.

The growth traits AW, BH, DLS2, and MLP3 were significantly associated with two microsatellite loci, and eight traits (CL, FFLC, FFWC, FFHC, MLP2, ICW, CWS8, and BW) were significantly associated with one microsatellite locus. Meanwhile, one microsatellite marker was significantly associated with several different growth traits, and different markers were simultaneously and significantly associated with one trait. This phenomenon indicates that one locus contributes to multiple growth traits and multiple loci influenced the same growth trait of S. paramamosain. Similar cases have also been found in other animals, such as the largemouth bass (Micropterus salmoides) [43] and the swimming crab (P. trituberculatus) [37]. As far as we know, growth traits are quantitative traits and they are possibly controlled by several to numerous genes. These genes may have segregated and/or recombined among different generations. Hence, we should investigate the replicability of these three markers in different families and populations, and evaluate their correlation across different generations. In artificial breeding programs, individuals with the target genotypes of these three microsatellite loci should be chosen as candidate parents for breeding, and their offspring that carry the target genotypes should be chosen again. These target microsatellite loci will thus be applied for the practical selection of S. paramamosain for growth performance.

Conclusions

We initially isolated 129 transcriptome-derived polymorphic microsatellite markers. We then identified three markers that were significantly associated with 12 phenotypic growth traits of the mud crab (Scylla paramamosain). These findings are helpful in investigations on the population conservation genetics, construction of genetic maps, and molecular MAS of S. paramamosain and other closely related species.

Supporting Information

Table S1.

Characterization of 129 polymorphic microsatellite markers derived from a transcriptome sequence library in S. paramamosain.

doi:10.1371/journal.pone.0089134.s001

(DOCX)

Author Contributions

Conceived and designed the experiments: HM LM PL. Performed the experiments: HM WJ NF QM SL YL. Analyzed the data: HM WJ. Contributed reagents/materials/analysis tools: HM CM ZQ LM. Wrote the paper: HM. Revised the manuscipt: HM PL.

References

  1. 1. Shen Y, Lai Q (1994) Present status of mangrove crab (Scylla serrata (Forskål)) culture in China. ICLARM Quart 28–29.
  2. 2. Keenan CP, Blackshaw PA (1999) Mud crab aquaculture and biology. ACAIR Proceedings No. 78. Watson Ferguson & Co, Australia.
  3. 3. Perrine D (1979) The mangrove crab on Ponape. Marine Resources Division, Ponape Eastern Caroline Islands. 88 pp.
  4. 4. Fishery Bureau of Ministry of Agriculture of China. China Fisheries Yearbook (2012) Chinese Agricultural Press, China, Beijing, October.
  5. 5. Ma QQ, Ma HY, Chen JH, Ma CY, Feng NN, et al. (2013) Parentage assignment of the mud crab (Scylla paramamosain) based on microsatellite markers. Biochem Syst Ecol 49: 62–68. doi: 10.1016/j.bse.2013.03.013
  6. 6. Takano M, Barinova A, Sugaya T, Obata Y, Watanabe T, et al. (2005) Isolation and characterization of microsatellite DNA markers from mangrove crab, Scylla paramamosain. Mol Ecol Notes 5: 794–795. doi: 10.1111/j.1471-8286.2005.01065.x
  7. 7. Xu XJ, Wang GZ, Wang KJ, Li SJ (2009) Isolation and characterization of ten new polymorphic microsatellite loci in the mud crab, Scylla paramamosain. Conserv Genet 10: 1877–1878. doi: 10.1007/s10592-009-9843-y
  8. 8. Ma HY, Ma CY, Ma LB, Cui HY (2010) Novel polymorphic microsatellite markers in Scylla paramamosain and cross-species amplification in related crab species. J Crustacean Biol 30: 441–444. doi: 10.1651/09-3263.1
  9. 9. Ma HY, Ma CY, Ma LB, Zhang FY, Qiao ZG (2011) Isolation and characterization of 54 polymorphic microsatellite markers in Scylla paramamosain by FIASCO approach. J World Aquacult Soc 42: 591–597. doi: 10.1111/j.1749-7345.2011.00503.x
  10. 10. Ma HY, Ma QQ, Ma CY, Ma LB (2011) Isolation and characterization of gene-derived single nucleotide polymorphism (SNP) markers in Scylla paramamosain. Biochem Syst Ecol 39: 419–424. doi: 10.1016/j.bse.2011.05.024
  11. 11. Ma HY, Ma CY, Li XC, Xu Z, Feng NN, et al. (2013) The complete mitochondrial genome and gene organization of the mud crab (Scylla paramamosain) with phylogenetic consideration. Gene 519: 120–127. doi: 10.1016/j.gene.2013.01.028
  12. 12. Li SJ, Ma HY, Ma CY, Jiang W, Feng NN, et al. (2013) Segregating principle of AFLP marker in the mud crab (Scylla paramamosain). Biotechnol Bull 11: 123–129 (In Chinese with English Abstract)..
  13. 13. Cui HY, Ma HY, Ma CY, Qiao ZG, Ma QQ, et al. (2011) Genetic diversity among different families of mud crab Scylla paramamosain by microsatellite markers. Mar Fish 33 (3): 274–281 (In Chinese with English Abstract)..
  14. 14. Ma HY, Cui HY, Ma CY, Ma LB (2012) High genetic diversity and low differentiation in mud crab (Scylla paramamosain) along the southeastern coast of China revealed by microsatellite markers. J Exp Biol 215: 3120–3125. doi: 10.1242/jeb.071654
  15. 15. Dudaniec RY, Storfer A, Spear SF, Richardson JS (2010) New microsatellite markers for examining genetic variation in peripheral and core populations of the coastal giant salamander (Dicamptodon tenebrosus). Plos One 5 (12): e14333. doi: 10.1371/journal.pone.0014333
  16. 16. Ma HY, Chen SL, Yang JF, Chen SQ, Liu HW (2011) Genetic linkage maps of barfin flounder (Verasper moseri) and spotted halibut (Verasper variegatus) based on AFLP and microsatellite markers. Mol Biol Rep 38: 4749–4764. doi: 10.1007/s11033-010-0612-2
  17. 17. Song WT, Li YZ, Zhao YW, Liu Y, Niu YZ, et al. (2012) Construction of a high-density microsatellite genetic linkage map and mapping of sexual and growth-related traits in half-smooth tongue sole (Cynoglossus semilaevis). Plos One 7 (12): e52097. doi: 10.1371/journal.pone.0052097
  18. 18. Fuji K, Hasegawa O, Honda K, Kumasaka K, Sakamoto T, et al. (2007) Marker-assisted breeding of a lymphocystis disease-resistant Japanese flounder (Paralichthys olivaceus). Aquaculture 272: 291–295. doi: 10.1016/j.aquaculture.2007.07.210
  19. 19. Weller JI (1994) Economic aspects of animal breeding. Chapman and Hall, UK, 244pp.
  20. 20. Mukherjee K, Mandal N (2009) A microsatellite DNA marker developed for identifying disease-resistant population of giant black tiger shrimp, Penaeus monodon. J World Aquacult Soc 40 (2): 274–280. doi: 10.1111/j.1749-7345.2009.00250.x
  21. 21. Thanh NM, Barnes AC, Mather PB, Li Y, Lyons RE (2010) Single nucleotide polymorphisms in the actin and crustacean hyperglycemic hormone genes and their correlation with individual growth performance in giant freshwater prawn Macrobrachium rosenbergii. Aquaculture 301: 7–15. doi: 10.1016/j.aquaculture.2010.02.001
  22. 22. Jiang W, Ma HY, Ma CY, Li SJ, Liu YX, et al.. (2013) Characteristics of growth traits and their effects on body weight of G1 individuals in the mud crab (Scylla paramamosain). Genet Mol Res, accepted.
  23. 23. Keenan CP, Davie PJF, Mann DL (1998) A revision of the genus Scylla de Haan, 1833 (Crustacea: Decapoda: Brachyura: Portunidae). Raffles B Zool 46: 217–245.
  24. 24. Gao BQ, Liu P, Li J, Chi H, Dai FY (2008) The relationship between morphometric characters and body weight of Portunus trituberculatus. Mar Fish Res 29: 44–50 (In Chinese with English Abstract)..
  25. 25. Ma HY, Yang JF, Su PZ, Chen SL (2009) Genetic analysis of gynogenetic and common populations of Verasper moseri using SSR markers. Wuhan Univ J Nat Sci 14 (3): 267–273. doi: 10.1007/s11859-009-0315-5
  26. 26. Yeh FC, Yang RC, Boyle T (1999) POPGENE version 1.31. Microsoft window-based freeware for population genetic analysis. Available: www.ualberta.ca/~fyeh/. University of Alberta and the Centre for International Forestry Research.
  27. 27. Rice WR (1989) Analyzing tables of statistical tests. Evolution 43: 223–225. doi: 10.2307/2409177
  28. 28. Hou R, Bao Z, Wang S, Su H, Li Y, et al. (2011) Transcriptome sequencing and de novo analysis for yesso scallop (Patinopecten yessoensis) using 454 GS FLX. Plos One 6 (6): e21560. doi: 10.1371/journal.pone.0021560
  29. 29. Jung H, Lyons RE, Dinh H, Hurwood DA, McWilliam S, et al. (2011) Transcriptomics of a giant freshwater prawn (Macrobrachium rosenbergii): de novo assembly, annotation and marker discovery. Plos One 6 (12): e27938. doi: 10.1371/journal.pone.0027938
  30. 30. Du H, Bao Z, Hou R, Wang S, Su H, et al. (2012) Transcriptome sequencing and characterization for the sea cucumber Apostichopus japonicus (Selenka, 1867). Plos One 7 (3): e33311. doi: 10.1371/journal.pone.0033311
  31. 31. Wang Y, Guo X (2007) Development and characterization of EST-SSR markers in the eastern oyster Crassostrea virginica. Mar Biotechnol 9: 500–511. doi: 10.1007/s10126-007-9011-7
  32. 32. Thiel T, Michalek W, Varshney RK, Graner A (2003) Exploiting EST databases for the development and characterization of gene-derived SSR-markers in barley (Hordeum vulgare L.). Theor Appl Genet 106: 411–422.
  33. 33. Ellis JR, Burke JM (2007) EST-SSRs as a resource for population genetic analyses. Heredity 99: 125–132. doi: 10.1038/sj.hdy.6801001
  34. 34. Ma HY, Ma CY, Ma LB (2011) Identification of type I microsatellite markers associated with genes and ESTs in Scylla paramamosain. Biochem Syst Ecol 39: 371–376. doi: 10.1016/j.bse.2011.05.007
  35. 35. Yu H, Li Q (2008) Exploiting EST databases for the development and characterization of EST-SSRs in the Pacific oyster (Crassostrea gigas). J Hered 99 (2): 208–214. doi: 10.1093/jhered/esm124
  36. 36. Yue GH, Ho MY, Orban L, Komen J (2004) Microsatellites within genes and ESTs of common carp and their applicability in silver crucian carp. Aquaculture 234: 85–98. doi: 10.1016/j.aquaculture.2003.12.021
  37. 37. Liu L, Li J, Liu P, Zhao FZ, Gao BQ, et al. (2012) Correlation analysis of microsatellite DNA markers with growth related traits of swimming crab (Portunus trituberculatus). J Fish China 36 (7): 1034–1041 (In Chinese with English Abstract)..
  38. 38. Li JL, Tang YK, Chen WH, Yu JH, Dong ZJ, et al. (2009) Association analysis of microsatellite DNA markers with body weight and body shape in GIFT. J Fish Sci China 16 (6): 824–832 (In Chinese with English Abstract)..
  39. 39. Yang J, Zhang XF, Chu ZY, Sun XW (2010) Correlation analysis of microsatellite markers with body weight, length, height and upper jaw length wensize of common carp (Cyprinus carpio L.). J Fish Sci China 17 (4): 721–730 (In Chinese with English Abstract)..
  40. 40. Chen M, Chang YQ, Sun Q, Zhao XY (2009) Genetic structure of Japanese scallop Patinopecten yessoensis population and correlation analysis of microsatellite DNA markers with economic traits. J Dalian Fish Univ 24 (4): 311–316 (In Chinese with English Abstract)..
  41. 41. Chatterjee R, Sharma RP, Bhattacharya TK, Niranjan M, Reddy BL (2010) Microsatellite variability and its relationship with growth, egg production, and immunocompetence traits in chickens. Biochem Genet 48: 71–82. doi: 10.1007/s10528-009-9296-5
  42. 42. Sanchez-Molano E, Cerna A, Toro MA, Bouza C, Hermida M, et al. (2011) Detection of growth-related QTL in turbot (Scophthalmus maximus). BMC Genomics 12: 473. doi: 10.1186/1471-2164-12-473
  43. 43. Li XH, Bai JJ, Ye X, Hu YC, Li SJ, et al. (2009) Polymorphisms in the 5′ flanking region of the insulin-like growth factor I gene are associated with growth traits in largemouth bass Micropterus salmoides. Fisheries Sci 75: 351–358. doi: 10.1007/s12562-008-0051-3