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Article

Development of Microsatellite Markers from Transcriptome of Eriocheir sinensis and Their Application in Multiplex PCR Panels

by
Qizhen Xiao
1,2,†,
Meijun Tang
1,3,†,
Qingqing Li
1,4,5,
Pengsheng Dong
1,4,5,
Yongxu Cheng
1,4,5,
Deng Deng
6,* and
Xugan Wu
1,3,4,*
1
Key Laboratory of Freshwater Aquatic Genetic Resources, Ministry of Agriculture, Shanghai Ocean University, Shanghai 201306, China
2
Fujian Provincial Key Lab of Coastal Basin Environment, Fujian Polytechnic Normal University, Fuqing 350300, China
3
Dongying Huize Agricultural Technology Co., Ltd., Dongying 257503, China
4
Shanghai Collaborative Innovation Center for Aquatic Animal Genetics and Breeding, Shanghai Ocean University, Shanghai 201306, China
5
National Demonstration Center for Experimental Fisheries Science Education, Shanghai Ocean University, Shanghai 201306, China
6
Shenzhen Alpha Feed Co., Ltd., Shenzhen 518054, China
*
Authors to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Animals 2024, 14(22), 3200; https://doi.org/10.3390/ani14223200
Submission received: 20 September 2024 / Revised: 23 October 2024 / Accepted: 5 November 2024 / Published: 8 November 2024
(This article belongs to the Section Aquatic Animals)
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Simple Summary

As a key economic species in China, the breeding of new strains of Chinese mitten crab (Eriocheir sinensis) has been undertaken across various regions to address genetic resource degradation caused by the industry’s rapid expansion. Limited pond space often resulted in different families of Eriocheir sinensis being raised together, complicating the preservation of distinct family lines. Microsatellite markers are widely used to assess the genetic diversity and assign parentage, but they can be expensive to develop and apply. Advances in high-throughput sequencing technology have made these markers more accessible, while multiplex PCR can significantly reduce costs. In this study, 100 highly polymorphic SSR markers were developed from the transcriptome, four multiplex PCR panels were constructed, and a parentage assignment method was created for Eriocheir sinensis. The results demonstrated that 100% of offspring were accurately assigned to their parents across six crab families when three or four panels were used. This method provides an efficient and cost-effective solution for breeding and family management, promoting the sustainable development of the industry.

Abstract

Background: The development of genetic markers is crucial for aquaculture and population genetics. Polyallelic microsatellite markers are widely used for breeding and family management of Eriocheir sinensis (E. sinensis), contributing to the sustainable development of the industry. Methods: Through transcriptome analysis, 100 highly polymorphic SSR markers were identified and developed. From these, four multiplex PCR panels, each containing 16 markers, were constructed and applied to parentage assignment in six E. sinensis families. Results: The number of alleles (Na) ranged from 7 to 21, the observed heterozygosity (Ho) varied from 0.356 to 0.951, and the expected heterozygosity (He) varied from 0.656 to 0.909. The polymorphism information content (PIC) ranged from 0.633 to 0.900. The CERVUS analysis showed that using only the two most informative multiplex PCR panels, assignment success rates exceeded 90.34%, with 100% accuracy in assigning offspring to parents when three or four panels were used. Conclusions: This method significantly enhances the efficiency of microsatellite marker analysis, which provides a convenient and cost-effective method for breeding programs and family management of E. sinensis.

1. Introduction

The Chinese mitten crab, Eriocheir sinensis (E. sinensis), a member of the Grapsidae family of decapod crustaceans, is widely distributed in the East Asian–Pacific region and holds significant aquaculture value in China. Due to its high nutritional content and market demand, E. sinensis is extensively cultured throughout China, with a production volume reaching 888,629 tons in 2023 [1]. The cultivation of E. sinensis supports a vital industry in eastern China, attracting increasing numbers of farmers. However, this expansion has introduced challenges such as sexual precocity [2,3], hepatopancreatic necrosis disease [4], inbreeding, and the degeneration of genetic resources [5]. These issues arise largely because many farmers rely on small-scale stock for breeding or indiscriminately hybridize geographically distinct wild populations with poor growth traits or lower taste quality in an attempt to reduce costs. Consequently, the genetic quality and overall performance of aquaculture stocks have deteriorated. To address these challenges, it is crucial to protect the genetic resources of E. sinensis and guide the industry towards more sustainable breeding practices. Family-based selection, which has proven effective in the breeding of plants and livestock, holds great potential for aquaculture as well [6]. By constructing large-scale families, breeders can accurately trace genealogical information, select high-quality parents, and avoid inbreeding. This approach also helps to continuously improve desirable traits and shortens the breeding cycle [6,7]. While family-based selection offers many benefits, it has not been widely adopted in E. sinensis breeding due to the high costs associated with maintaining large family groups. Assigning each family to a specific pond can help minimize environmental variations and reduce the costs of maintaining separate family lines [8]. Traditionally, physical tags have been used to distinguish individual lineages [9,10]. However, applying physical tags to E. sinensis is problematic, as the crabs molt frequently, making it difficult to attach and maintain tags on their bodies. Implanting electronic tags may offer a solution, but the crabs need to reach a sufficient size, and the technology for reliable tagging is still under development. Tags are prone to falling off, and the mortality rate may increase if the tags are improperly applied. A more effective solution is to house different families in the same pond and use DNA molecular markers to distinguish between them.
Molecular markers such as random amplification polymorphism DNA (RAPD), amplified fragment length polymorphism (AFLP), microsatellites (also known as simple sequence repeat, SSR), and single-nucleotide polymorphisms (SNPs) are widely used in breeding programs [11,12]. While SNPs have been increasingly applied in genetic studies of crustaceans such as Pacific white shrimp (Litopenaeus vannamei) [13,14], mud crab (Scylla paramamosain) [15], and swimming crab (Portunus trituberculatus) [16], their development and analysis require more advanced and costly technologies. In contrast, SSR markers remain a preferred tool for genetic diversity analysis [17,18] and parentage identification [19,20] due to their accessibility. Microsatellite markers have been widely utilized for over a decade due to their high abundance, variability, co-dominant inheritance, and neutral selection within the genome. They typically consist of tandem repeating units of mono-, di-, tri-, and tetra-nucleotide sequences [21,22]. Their high allelic diversity provides superior power for determining pedigree relationships among individuals [23]. In E. sinensis, microsatellite markers are commonly used for constructing linkage maps [24,25], assessing population genetic diversity [26,27,28], and conserving and exploiting wild crab genetic resources [29,30]. For family management, the use of microsatellite markers in E. sinensis offers several advantages, including reduced maintenance costs, savings in manpower and space, and a more reliable method for evaluating genetic parameters. However, despite these benefits, the cost of genotyping large numbers of individuals with microsatellite markers can still be considerable.
Establishing microsatellite multiplex PCR panels is useful for evaluating parentage assignment in applied fisheries, particularly when managing large numbers of families. Multiplex PCR panels reduce the time and cost associated with genetic diversity evaluations using SSR markers, minimizing repetitive manipulations and manual handling errors [31]. These panels have been successfully implemented in several aquatic species, including grass carp (Ctenopharyngodon idellus) [32], mussel Anodonta (Sinanodonta) woodiana (Lea, 1834) [33], and swimming crab (Portunus trituberculatus) [34]. However, research on the development of multiplex PCR systems for E. sinensis remains limited, despite their potential to enhance large-scale family breeding programs.
With the advent of next-generation sequencing technology for transcriptomics, the acquisition of a substantial number of SSR markers has become more cost-effective. In this study, we sequenced the transcriptomics of E. sinensis, identifying 1,521,519 putative microsatellite markers. From 272 designed SSR primer pairs, 205 were validated, with 100 demonstrating high polymorphism. By screening these polymorphic microsatellite markers, we developed four microsatellite multiplex PCR panels for the first time in E. sinensis and applied them to determine parentage in six families. This approach significantly enhances analytical efficiency and offers a practical and effective method for breeding and family management of E. sinensis, promoting the sustainable development of the industry.

2. Materials and Methods

2.1. Experimental Animals and Tissue Collection

Samples for transcriptome sequencing were collected at the Chongming Research Station of Shanghai Ocean University (N 31°37′, E 121°23′), where continuous breeding and lineage establishment of E. sinensis has been conducted for an extended period. Based on previous research on ovarian development [35], we sampled three successive developmental stages (Stages I, II, and III) of E. sinensis, collecting three ovarian tissue samples from each stage. These samples were promptly stored in liquid nitrogen for subsequent analysis. For polymorphism validation of putative SSR markers and the construction of multiplex PCR panels, samples were obtained from 15 wild crabs collected from Zhenjiang, Jiangsu province (N 32°11′, E 119°25′), and 15 pond-cultured crabs from Chongming district, Shanghai City (N 31°37′, E 121°23′). Additionally, samples for multiplex PCR construction and parentage assignment were obtained from six families, consisting of 12 adult parents and 207 offspring (Table 1). The third appendage muscle from each sample was collected and preserved in 95% ethanol for DNA analysis. Total genomic DNA was extracted using a modified phenol–chloroform method [36]. The quantity and purity of the extracted DNA were assessed with a Q5000 UV spectrophotometer (Qwawell, San Jose, CA, USA), and DNA quality was confirmed through 1% agarose gel electrophoresis. Total RNA was extracted from the ovarian tissues using the TRIzol@ Reagent (TaKaRa, Kyoto, Japan) following the manufacturer’s protocol. The quantity and quality of total RNA were measured using a Q5000 UV spectrophotometer (Qwawell, USA) and a 2100 Bioanalyzer (Agilent Technologies, Santa Clara, CA, USA), respectively.

2.2. Transcriptome Sequencing, Assembly, and Microsatellite Identification

Transcriptome sequencing was conducted using the Illumina HiSeq™ 2500 platform (Illumina, Inc., San Diego, CA, USA) [37]. After filtering out reads containing adapters, poly-N sequences, and low-quality reads, clean reads were aligned to the reference genome assembly using the HISAT v2.0.4 program [38]. Microsatellite markers with a 10 bp minimum repeat length, encompassing di- to hexa-nucleotide motifs, were identified using MISA software 1.0, resulting in the identification of 1,521,519 putative SSR markers. Subsequently, primers were designed for these markers using Primer 5.0 [39], leading to the selection of 272 SSR markers for polymorphism evaluation. Each marker was PCR-amplified individually in 30 samples. The PCR amplification was carried out in a final volume of 12.5 µL, containing 6.25 µL of Premix Taq (which include 0.5U ExTaq, 0.4 mM dNTP mixture, and 4 Mm EXTaq buffer, Novoprotein Biotech Co., Ltd., Suzhou, China), 0.5 µL of each primer (at a concentration of 10 pmol/µL), 1 µL of template DNA (approximately 50 ng), and 4.25 µL of double-distilled water. One primer from each pair was labeled with the fluorescent dye HEX or FAM (see Table 2 for details). The primers were synthesized by Sangon Biotech (Shanghai Co., Ltd., Shanghai, China). The PCR conditions included an initial denaturation step at 94 °C for 5 min, followed by 35 cycles of 30 s at 94 °C, 30 s at the optimal annealing temperature (as indicated in Supplementary Table S1), and 1 min at 72 °C, concluding with a final extension step at 72 °C for 10 min. The quality of the PCR products was assessed using 1% agarose gel electrophoresis, and all PCR products were analyzed on an automatic capillary sequencer Applied Biosystems 3730XL (Thermo Fisher Scientific, Waltham, MA, USA) using the GeneScan 500 LIZ size standard (Thermo Fisher Scientific, Waltham, MA, USA). The polymorphic microsatellite markers were retained to construct multiplex PCR panels.

2.3. Multiplex PCR Panel Optimization

The polymorphic microsatellite markers were combined into multiplex PCR reactions using Multiplex Manager1.0 software [40] to evaluate potential issues such as mismatches, primer dimers, and hairpin structures. We tested the SSR multiplex PCR panels using 30 samples of E. sinensis. The multiplex PCR amplifications were conducted in a final volume of 12.5 µL, containing 50 ng of template DNA, 6.25 µL of Premix Taq (as detailed in Section 2.2), and 0.5 µL of each primer (as detailed in Section 2.2), with double-distilled water added to achieve the total volume. The thermal cycling protocol consisted of an initial denaturation at 94 °C for 3 min, followed by 35 cycles of 94 °C for 1 min, 55 °C for 1 min, 72 °C for 1 min, and a final extension step at 72 °C for 10 min. To optimize amplification and ensure comparable fluorescent signals, the volume of primers was adjusted from the initial volume of 0.05 µL. The PCR products were analyzed on an automatic capillary sequencer Applied Biosystems 3730XL (Thermo Fisher Scientific, Waltham, MA, USA) with the GeneScan 500 LIZ size standard (Thermo Fisher Scientific, Waltham, MA, USA).

2.4. Parentage Assignment

The SSR multiplex PCR panels were utilized for parentage assignment in six families of E. sinensis, with the number of progeny assigned to each family detailed in Table 1. Cervus v3.0 [41] was used for parentage analysis based on the genotyping data with the maximum likelihood method. This analysis included both simulated and actual identification rates, with a default genotyping error rate set at 1% and confidence intervals at 95%. Using genotype data from the offspring and candidate parents, the parentage assignment module determined the parental pair for each offspring.

2.5. Data Analysis

Alleles were assigned by GeneMapper Software v4.0 (Applied Biosystems) and potential genotyping errors were checked with Micro-Checker Software 2.2.3 [42]. The following genetic parameters were calculated using POPGENE 3.2 [43]: number of alleles (Na), number of effective alleles (Ne), observed heterozygosity (Ho), expected heterozygosity (He), Shannon index (I), and departure from the Hardy–Weinberg equilibrium (HW). Polymorphism information content (PIC) was calculated using PICcalc 0.6 [44]. Parentage assignments were performed using CERVUS 3.0 software [41].

3. Results

3.1. Primer Design and Validation

A total of 1,521,519 putative SSR markers were identified from the transcriptome data of E. sinensis (Figure 1). Among these, 695,674 SSR markers were di-nucleotide repeats (45.72%), 645,399 SSR markers were tri-nucleotide repeats (42.42%), 141,615 SSR markers were tetra-nucleotide repeats (9.31%), 34,445 SSR markers were penta-nucleotide repeats (2.26%), and 4386 SSR markers were hexa-nucleotide repeats (0.29%) (Figure 1). To ensure unbiased and specific selection of SSR markers, 272 SSR markers were randomly chosen for polymorphism assessment in 30 crabs (Table 3). The results demonstrated that 205 of the selected markers produced clear and stable bands within the expected fragment range, with 100 markers exhibiting polymorphisms (Table 3 and Supplementary Table S1).

3.2. Established SSR Multiplex PCR Panels

The same panel of 30 crabs was used for the optimization of multiplex PCR systems. Four sets of optimized multiplex PCR panels were developed based on primer annealing temperatures, allelic size ranges, and compatibility of fluorescent labeling dyes. Each set consisted of four markers as follows: panel 1 included CX140, CX287, CX256, and CX416; panel 2 contained CX003, CX254, CX400, and CX427; panel 3 comprised CX074, CX013, CX298, and CX312; and panel 4 featured CX071, CX118, CX325, and CX435 (Table 2). The partial gene scans for the four multiplex PCR panels in E. sinensis are depicted in Figure 2, demonstrating that the primer amplification for each set of multiplex PCR reactions produced similar fluorescence signal peaks. The use of two fluorescent dyes, 6-FAM and HEX, facilitated the overlap of similarly sized markers, enabling the successful amplification of all markers across the different panels. To verify the feasibility of multiplex PCR in assessing genetic diversity, these 16 markers were subsequently evaluated in 207 individuals. All markers exhibited polymorphism, with the number of alleles (Na) ranging from 7 to 21. The observed heterozygosity (Ho) varied from 0.356 to 0.951, while the expected heterozygosity (He) ranged from 0.656 to 0.909 (Table 4). Furthermore, the polymorphism information content (PIC) ranged from 0.633 to 0.900, and it was found that 13 markers deviated from the Hardy–Weinberg equilibrium (p < 0.01) (Table 4).

3.3. Result of Parentage Assignment

Parentage assignments for six families were analyzed using microsatellite multiplex PCR in conjunction with the maximum likelihood method via CERVUS 3.0 [41]. The actual identification rates for the known parents were 70.05%, 41.55%, 46.38%, and 36.23% for multiplex PCR sets 1, 2, 3, and 4, respectively. Simulation results indicated assignment success rates of 79.71%, 50.07%, 41.55%, and 54.59% for sets 1, 2, 3, and 4, respectively (Figure 3). Notably, the simulated identification rates were generally higher than the actual rates, with the exception of multiplex PCR set 3.
At a 95% confidence level, cumulative parentage assignment was prioritized based on the actual identification rates of each multiplex PCR system, arranged from highest to lowest. The results demonstrated that employing the two multiplex PCRs (set 1 and set 3) achieved assignment accuracy rates exceeding 90.34% (Figure 4). Furthermore, the use of three or four multiplex PCR sets resulted in 100% correct allocation of offspring to their respective parents (Figure 4).

4. Discussion

With the advent of next-generation sequencing technology, the discovery of SSR markers is no longer confined to constructing SSR libraries and sequencing candidate clones. Given the challenges of obtaining high-quality genome-wide information for non-model organisms, an increasing number of studies have shown that high-throughput microsatellite marker development from transcriptome data is an effective approach [45]. In this study, we conducted transcriptome sequencing of the ovaries of Eriocheir sinensis at different developmental stages using the Illumina HiSeq™ 2500 platform, resulting in the identification of 1,521,519 candidate microsatellite markers. Among these SSR markers, di-nucleotide repeat motifs were the most prevalent (45.72%), followed closely by tri-nucleotide repeat motifs (42.42%). These findings align with previous reports on E. sinensis, which indicated that di-nucleotide repeat motifs were the most abundant type (58.54%), followed by tri-nucleotide repeats (30.11%) [28]. It is generally recognized that most SSR repeats in animals are di-nucleotide repeats [46,47], and our results support this observation. To validate the identified SSR markers, we randomly selected a subset of 272 primer pairs for PCR, achieving a high polymorphism rate of 48.8% (100 out of 205) for the SSR markers. The lower polymorphism rates observed in our study compared to previous reports on E. sinensis may stem from false positives inherent in transcriptome sequencing, as well as variations in SSR search tools and criteria employed [48]. The number of SSR markers found in the transcriptome was lower, which is expected since transcriptome sequences primarily represent functional genes with fewer repeat sequences in coding regions; most repeated sequences are typically found in the untranslated regions (UTRs). Additionally, the challenge of amplifying larger intron regions can hinder the efficiency of microsatellite development in the transcriptome, necessitating the selection of primers that amplify smaller fragments. However, microsatellite markers developed from the transcriptome are often derived from regulatory or coding regions, enabling the direct and accurate tagging of functional genes, which is beneficial for studying functional variation, adaptive evolution, and genetic diversity in species during genetic processes [49,50]. In summary, this transcriptome provides a valuable resource for future gene analyses, and these microsatellite markers will be instrumental in advancing our understanding of the molecular ecology and genetic breeding of E. sinensis.
The establishment of microsatellite multiplex PCR represents the most efficient method for evaluating parentage assignment in applied fisheries, especially when a large number of family separations is required. This study is the first to establish a microsatellite multiplex PCR panel for E. sinensis. We developed four multiplex panels, each containing four microsatellite markers, designed based on allelic size range and compatibility of fluorescent labeling dyes. A critical factor in establishing and applying a multiplex microsatellite assay is the appropriate selection of sites and their combinations [51]. The concentration of each primer in the multiplex PCR reaction system is particularly crucial, albeit a tedious and challenging step. Various factors influence primer concentrations, including annealing temperature, nucleotide sequence patterns, and PCR product size [34,52]. Research suggests that the size difference between fragments at two markers should exceed 20 bp to minimize potential biases in amplification [53]. In our assay, the product fragment sizes between markers were greater than 20 bp or labeled with different fluorescent markers, allowing for accurate allele peak distinction. Ultimately, we successfully developed four SSR multiplex PCR sets for E. sinensis, which can serve as effective tools for genetic studies, including population genetic structure assessments and parentage assignments.
Beyond E. sinensis, multiplex PCR has been widely applied in other aquatic species. For instance, in the giant freshwater prawn (Macrobrachium rosenbergii), multiplex PCR has been utilized for genetic analysis and parentage verification. Similarly to our findings, studies on Macrobrachium rosenbergii have emphasized the efficiency of multiplex PCR in reducing time and reagent costs while maintaining high accuracy in genetic assessments. However, it is noteworthy that genetic diversity in Macrobrachium rosenbergii populations tends to be lower than that in E. sinensis [54,55]. In grass carp (Ctenopharyngodon idellus), multiplex PCR has been successfully applied to evaluate genetic diversity in breeding programs, revealing higher genetic diversity in cultured populations compared to E. sinensis, with a mean Na of 22.15, mean Ho of 0.823, mean He of 0.859, and mean PIC of 0.846 across nine populations [32]. The successful application of multiplex PCR across various aquatic animals underscores its value as a versatile and powerful tool for genetic analysis and breeding management.
Microsatellite markers have been extensively used for parentage assignment in various aquatic animals, such as orange-spotted grouper (Epinephelus coioides) [56], tiger trout (Salmo trutta × Salvelinus fontinalis) [57], and red swamp crayfish (Procambarus clarkii) [58]. These markers have significantly reduced the workload associated with distinguishing different families compared to traditional physical tagging methods. In our study, parentage assignment was performed on six families using microsatellite multiplex PCR, achieving 100% accuracy in assigning offspring to their parents when three or four multiplex PCR sets were utilized. Varying success rates for parentage assignments have been reported previously; for example, Zhang et al. (2016) used nine SSR markers to identify 18 full-sib families in yellow catfish Pelteobagrus Fulvidraco, achieving 100% allocation for all progeny [59]. Similarly, Satyanarayana et al. (2015) achieved a 91% assignment success rate using four SSR markers in freshwater prawn Macrobrachium rosenbergii [60]. Yang et al. (2014) reported a 97% success rate with ten SSR markers in mandarin fish Siniperca chuatsi [61]. The success rate observed in our study meets the practical needs for production applications.
Our findings suggest that the accuracy of parentage assignment depends on the number of markers used and their polymorphism; a lower-locus polymorphism necessitates a higher number of markers for reliable assignment. Previous studies have emphasized that selecting microsatellite markers with high polymorphism enhances the power of parentage assignment [59,62]. Moreover, the ability to identify paternity diminishes as the number of candidate parents increases, underscoring the need for an increased number of molecular markers alongside the growing number of parents and offspring. For instance, it was shown that with the same number of microsatellite markers, identification rates were 92.2% with three pairs of candidate parents but only 78.9% with eight pairs [63]. While higher identification rates can be achieved with a greater number of microsatellite markers, this approach also increases workload and optimal number of markers. In our study, 100% of the offspring were accurately allocated to their parents when using 12 microsatellite markers.
Additionally, our results indicate that the simulated assignment success rates were higher than the actual assignment rates. This discrepancy may be attributed to genotyping errors and the presence of null alleles, which are known to impact the accuracy of parentage assignments [64]. Such issues could arise from mutations or indels at one or both of the primer-binding sites [65]. Similar phenomena have been reported in studies involving the pacific oyster Crassostrea gigas [66] and yellow catfish [59]. Notably, some markers (CX140, CX416, CX003) exhibited high null allele frequencies; however, the error rates in parentage assignments can be mitigated by utilizing a combination of all markers, enabling the correct identification of offspring and assignment to their most likely parent.

5. Conclusions

In conclusion, this study successfully developed 100 polymorphic SSR markers from the transcriptome and established four SSR multiplex PCR sets for parentage assignment in E. sinensis. This method significantly enhances analytical efficiency and provides a convenient and effective approach for breeding and family management, thereby promoting the sustainable development of the industry. However, potential limitations should be considered, particularly in large-scale breeding programs. One notable challenge is the cost associated with maintaining and optimizing these multiplex panels when genotyping a substantial number of individuals. Additionally, as the number of markers or individuals increases, issues such as unequal amplification efficiency and cross-amplification between markers may arise. To mitigate these challenges, integrating multiplex PCR with advanced technologies, such as next-generation sequencing, or automating PCR setups could help enhance scalability and reduce labor costs. Despite these limitations, multiplex PCR remains a valuable tool for genetic analysis, particularly for targeted breeding and population management in E. sinensis.

Supplementary Materials

The following supporting information can be downloaded at https://www.mdpi.com/article/10.3390/ani14223200/s1, Table S1: A total of 100 polymorphism microsatellite loci in the Chinese mitten crab (Eriocheir sinensis).

Author Contributions

Conceptualization, Q.X., P.D. and X.W.; methodology, Q.X. and P.D.; software, Q.X., M.T. and P.D.; validation, M.T., Q.X. and X.W.; formal analysis, Q.X. and M.T.; investigation, Q.X.; resources, X.W. and Y.C.; data curation, Q.L. and P.D.; writing—original draft preparation, Q.X.; writing—review and editing, M.T.; visualization, M.T.; supervision, X.W. and D.D.; project administration, X.W.; funding acquisition, X.W. and D.D. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by an open competition project (No. JBGS[2021]127) from the Department of Agriculture and Rural Affairs of Jiangsu province and a Key R&D Program (No. 2022YFD2400702) from the Ministry of Science and Technology of China. Infrastructure costs were partially supported by the project (No. 2021-KJ-02-12) for Shanghai Collaborative Innovation Center for Cultivating Elite Breeds and Green-culture of Aquaculture animals from the Shanghai Education Committee. X.W. was supported by the Leading Talent Project (No. DYRC202100215) in the Yellow River Delta from the Dongying Municipal Government of Shandong Province.

Institutional Review Board Statement

The Welfare and Ethical Committee of Shanghai Ocean University approved all experimental protocols and guidelines.

Informed Consent Statement

Not applicable.

Data Availability Statement

The transcriptomic raw data were uploaded to the National Center for Biotechnology Information (NCBI) (accession number is PRJNA660118), accessed on 10 September 2020.

Conflicts of Interest

Author Meijun Tang was employed by the company Dongying Huize Agricultural Technology Co., Ltd. and Author Deng Deng was employed by the company Shenzhen Alpha Feed Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

References

  1. Bureau of Fisheries and Fishery Administration; Ministry of Agriculture and Rural Affairs. 2024 China Fishery Statistical Yearbook; China Agriculture Press: Beijing, China, 2024; p. 24. [Google Scholar]
  2. Chang, G.; Wu, X.; Cheng, Y.; Zeng, C.; Yu, Z. Reproductive Performance, Offspring Quality, Proximate and Fatty Acid Composition of Normal and Precocious Chinese Mitten Crab Eriocheir sinensis. Aquaculture 2017, 469, 137–143. [Google Scholar] [CrossRef]
  3. Li, X.; Li, Z.; Liu, J.; De Silva, S.S. Advances in Precocity Research of the Chinese Mitten Crab Eriocheir sinensis. Aquacult. Int. 2011, 19, 251–267. [Google Scholar] [CrossRef]
  4. Ding, Z.; Meng, Q.; Liu, H.; Yuan, S.; Zhang, F.; Sun, M.; Zhao, Y.; Shen, M.; Zhou, G.; Pan, J.; et al. First Case of Hepatopancreatic Necrosis Disease in Pond-reared Chinese Mitten Crab, Eriocheir sinensis, Associated with Microsporidian. J. Fish Dis. 2016, 39, 1043–1051. [Google Scholar] [CrossRef] [PubMed]
  5. Wang, J.; Xu, P.; Zhou, G.; Li, X.; Lu, Q.; Liu, X.; Zhou, J.; Wang, C. Genetic Improvement and Breeding Practices for Chinese Mitten Crab, Eriocheir sinensis. J. World Aquacult. Soc. 2018, 49, 292–301. [Google Scholar] [CrossRef]
  6. Gjedrem, T. The First Family-based Breeding Program in Aquaculture. Rev. Aquacult. 2010, 2, 2–15. [Google Scholar] [CrossRef]
  7. Allen, S.K.; Small, J.M.; Kube, P.D. Genetic Parameters for Crassostrea Virginica and Their Application to Family-Based Breeding in the Mid-Atlantic, USA. Aquaculture 2021, 538, 736578. [Google Scholar] [CrossRef]
  8. Ruohonen, K. Individual Measurements and Nested Designs in Aquaculture Experiments: A Simulation Study. Aquaculture 1998, 165, 149–157. [Google Scholar] [CrossRef]
  9. Song, C.; Zhong, L.; Chen, X.; Bian, W.; Qiu, L.; Ming, J.; Chen, J. Variation Analysis and Sample Size Estimation for Growth Indicators during PIT-Tag-Assisted Family Construction of Channel Catfish (Ictalurus punctatus). Aquacult. Int. 2014, 22, 821–831. [Google Scholar] [CrossRef]
  10. Soula, M.; Navarro, A.; Hildebrandt, S.; Zamorano, M.J.; Roo, J.; Hernández-Cruz, C.M.; Afonso, J.M. Evaluation of VIE (Visible Implant Elastomer) and PIT (Passive Integrated Transponder) Physical Tagging Systems for the Identification of Red Porgy Fingerlings (Pagrus pagrus). Aquacult. Int. 2012, 20, 571–583. [Google Scholar] [CrossRef]
  11. Chauhan, T.; Rajiv, K. Molecular Markers and Their Applications in Fisheries and Aquaculture. Adv. Biosci. Biotechnol. 2010, 1, 281–291. [Google Scholar] [CrossRef]
  12. Napora-Rutkowski, Ł.; Rakus, K.; Nowak, Z.; Szczygieł, J.; Pilarczyk, A.; Ostaszewska, T.; Irnazarow, I. Genetic Diversity of Common Carp (Cyprinus carpio L.) Strains Breed in Poland Based on Microsatellite, AFLP, and mtDNA Genotype Data. Aquaculture 2017, 473, 433–442. [Google Scholar] [CrossRef]
  13. Yu, Y.; Luo, Z.; Wang, Q.; Zhang, Q.; Zhang, X.; Xiang, J.; Li, F. Development of High Throughput SNP Genotyping Approach Using Target Sequencing in Pacific White Shrimp and Its Application for Genetic Study. Aquaculture 2020, 528, 735549. [Google Scholar] [CrossRef]
  14. Silva, N.M.L.; Ianella, P.; Yamagishi, M.E.B.; Rocha, J.L.; Teixeira, A.K.; Farias, F.G.; Guerrelhas, A.C.; Caetano, A.R. Development and Validation of a Low-Density SNP Panel for Paternity and Kinship Analysis and Evaluation of Genetic Variability and Structure of Commercial Pacific White Shrimp (Litopenaeus vannamei) Populations from Brazil. Aquaculture 2022, 560, 738540. [Google Scholar] [CrossRef]
  15. Ye, S.; Zhou, X.; Ouyang, M.; Cui, W.; Xiang, Z.; Zhang, Y.; Yuan, Y.; Ikhwanuddin, M.; Li, S.; Zheng, H.; et al. Development and Validation of a 40 K Liquid SNP Array for the Mud Crab (Scylla paramamosain). Aquaculture 2025, 594, 741394. [Google Scholar] [CrossRef]
  16. Duan, B.; Mu, S.; Guan, Y.; Li, S.; Yu, Y.; Liu, W.; Li, Z.; Ji, X.; Kang, X. Genetic Diversity and Population Structure of the Swimming Crab (Portunus trituberculatus) in China Seas Determined by Genotyping-by-Sequencing (GBS). Aquaculture 2022, 555, 738233. [Google Scholar] [CrossRef]
  17. Zhang, Z.; Lu, C.; Lin, K.; You, W.; Yang, Z. Genetic Diversity and Genetic Structure among Four Selected Strains of Whiteleg Shrimp (Litopenaeus vannamei) Using SSR Markers. Fishes 2023, 8, 544. [Google Scholar] [CrossRef]
  18. Liu, X.; Tang, X.; Chen, M.; Ni, G.; Yang, Y. Development and Application of Molecular Markers in Fisheries, Aquaculture, and Industry of Representative Temperate and Tropical Sea Cucumbers: A Review. Front. Mar. Sci. 2024, 11, 1423096. [Google Scholar] [CrossRef]
  19. Han, Z.; Han, Q.; Xia, Y.; Geng, X.; Du, K.; Yang, J.; Kang, X. Construction of a Breeding Parent Population of Populus Tomentosa Based on SSR Genetic Distance Analysis. Sci. Rep. 2020, 10, 18573. [Google Scholar] [CrossRef]
  20. Chen, B.; Long, J.; Liu, J.; Wang, P.; Ma, Z.; Lan, Z.; Liang, Z.; Fu, Q.; Zhang, Z.; Zhang, Y.; et al. The Development of Novel Genome-SSRs, Multiplex PCR Panels, and Allelic Ladders for Parentage Identification in Tachypleus tridentatus. Aquaculture 2024, 592, 741262. [Google Scholar] [CrossRef]
  21. Sigang, F.; Hao, H.; Yong, L.; Pengfei, W.; Chao, Z.; Lulu, Y.; Xiuting, Q.; Qiu, L. Genome-Wide Identification of Microsatellite and Development of Polymorphic SSR Markers for Spotted Sea Bass (Lateolabrax maculatus). Aquacult. Rep. 2021, 20, 100677. [Google Scholar] [CrossRef]
  22. Sundaray, J.K.; Rasal, K.D.; Chakrapani, V.; Swain, P.; Kumar, D.; Ninawe, A.S.; Nandi, S.; Jayasankar, P. Simple Sequence Repeats (SSRs) Markers in Fish Genomic Research and Their Acceleration via next-Generation Sequencing and Computational Approaches. Aquacult. Int. 2016, 24, 1089–1102. [Google Scholar] [CrossRef]
  23. Jerry, D.R.; Evans, B.S.; Kenway, M.; Wilson, K. Development of a Microsatellite DNA Parentage Marker Suite for Black Tiger Shrimp Penaeus monodon. Aquaculture 2006, 255, 542–547. [Google Scholar] [CrossRef]
  24. Qiu, G.F.; Xiong, L.W.; Liu, Z.Q.; Yan, Y.L.; Shen, H. A First Generation Microsatellite-Based Linkage Map of the Chinese Mitten Crab Eriocheir sinensis and Its Application in Quantitative Trait Loci (QTL) Detection. Aquaculture 2016, 451, 223–231. [Google Scholar] [CrossRef]
  25. Qiu, G.F.; Xiong, L.W.; Han, Z.K.; Liu, Z.Q.; Feng, J.B.; Wu, X.G.; Yan, Y.L.; Shen, H.; Huang, L.; Chen, L. A Second Generation SNP and SSR Integrated Linkage Map and QTL Mapping for the Chinese Mitten Crab Eriocheir sinensis. Sci. Rep. 2017, 7, 39826. [Google Scholar] [CrossRef] [PubMed]
  26. Gao, X.G.; Li, H.J.; Li, Y.F.; Sui, L.J.; Zhu, B.; Liang, Y.; Liu, W.D.; He, C.B. Sixteen Polymorphic Simple Sequence Repeat Markers from Expressed Sequence Tags of the Chinese Mitten Crab Eriocheir sinensis. Int. J. Mol. Sci. 2010, 11, 3035–3038. [Google Scholar] [CrossRef]
  27. Chang, Y.; Liang, L.; Ma, H.; He, J.; Sun, X. Microsatellite Analysis of Genetic Diversity and Population Structure of Chinese Mitten Crab (Eriocheir sinensis). J. Genet. Genom. 2008, 35, 171–176. [Google Scholar] [CrossRef]
  28. Xiong, L.W.; Wang, Q.; Qiu, G.F. LargeScale Isolation of Microsatellites from Chinese Mitten Crab Eriocheir sinensis via a Solexa Genomic Survey. Int. J. Mol. Sci. 2012, 13, 16333–16345. [Google Scholar] [CrossRef]
  29. Wang, S.H.; Zhang, C.; Shang, M.; Wu, X.G.; Cheng, Y.X. Genetic Diversity and Population Structure of Native Mitten Crab (Eriocheir sensu stricto) by Microsatellite Markers and Mitochondrial COI Gene Sequence. Gene 2019, 693, 101–113. [Google Scholar] [CrossRef]
  30. Wang, S.; Luo, L.; Zhang, R.; Guo, K.; Xu, W.; Zhao, Z. Population Genetic Diversity and Differentiation of Mitten Crab, Genus Eriocheir, Based on Microsatellite Markers. Fishes 2022, 7, 182. [Google Scholar] [CrossRef]
  31. Porta, J.; María Porta, J.; Martínez-Rodríguez, G.; Del Carmen Alvarez, M. Development of a Microsatellite Multiplex PCR for Senegalese Sole (Solea senegalensis) and Its Application to Broodstock Management. Aquaculture 2006, 256, 159–166. [Google Scholar] [CrossRef]
  32. Li, D.; Wang, S.; Shen, Y.; Meng, X.; Xu, X.; Wang, R.; Li, J. A Multiplex Microsatellite PCR Method for Evaluating Genetic Diversity in Grass Carp (Ctenopharyngodon idellus). Aquac. Fish. 2018, 3, 238–245. [Google Scholar] [CrossRef]
  33. Popa, O.P.; Bartáková, V.; Bryja, J.; Reichard, M.; Popa, L.O. Characterization of Nine Microsatellite Markers and Development of Multiplex PCRs for the Chinese Huge Mussel Anodonta (Sinanodonta) Woodiana Lea, 1834 (Mollusca, Bivalvia). Biochem. Syst. Ecol. 2015, 60, 234–237. [Google Scholar] [CrossRef]
  34. Lee, H.J.; Lee, D.H.; Yoon, S.J.; Kim, D.H.; Kim, S.G.; Hyun, Y.S.; Min, G.S.; Chung, K.W. Characterization of 20 Microsatellite Loci by Multiplex PCR in Swimming Crab, Portunus trituberculatus. Genes Genom. 2013, 35, 77–85. [Google Scholar] [CrossRef]
  35. Wu, X.; Liu, M.; Pan, J.; Chen, H.; Zeng, C.; Cheng, Y. The Ovarian Development Pattern of Pond-Reared Chinese Mitten Crab, Eriocheir sinensis H. Milne-Edwards, 1853. Crustaceana 2017, 90, 449–470. [Google Scholar] [CrossRef]
  36. Green, M.R.; Sambrook, J.; Sambrook, J. Molecular Cloning: A Laboratory Manual, 4th ed.; Cold Spring Harbor Laboratory Press: Cold Spring Harbor, NY, USA, 2012; ISBN 978-1-936113-41-5. [Google Scholar]
  37. Feng, Q.; Liu, M.; Cheng, Y.; Wu, X. Comparative Transcriptome Analysis Reveals the Process of Ovarian Development and Nutrition Metabolism in Chinese Mitten Crab, Eriocheir sinensis. Front. Genet. 2022, 13, 910682. [Google Scholar] [CrossRef]
  38. Pertea, M.; Kim, D.; Pertea, G.M.; Leek, J.T.; Salzberg, S.L. Transcript-Level Expression Analysis of RNA-Seq Experiments with HISAT, StringTie and Ballgown. Nat. Protoc. 2016, 11, 1650–1667. [Google Scholar] [CrossRef]
  39. Clarke, K.R.; Gorley, R.N. PRIMER v5: User Manual/Tutorial; Primer-E Ltd.: Plymouth, UK, 2001. [Google Scholar]
  40. Holleley, C.E.; Geerts, P.G. Multiplex Manager 1.0: A Cross-Platform Computer Program That Plans and Optimizes Multiplex PCR. BioTechniques 2009, 46, 511–517. [Google Scholar] [CrossRef]
  41. Kalinowski, S.T.; Taper, M.L.; Marshall, T.C. Revising How the Computer Program cervus Accommodates Genotyping Error Increases Success in Paternity Assignment. Mol. Ecol. 2007, 16, 1099–1106. [Google Scholar] [CrossRef]
  42. Van Oosterhout, C.; Hutchinson, W.F.; Wills, D.P.M.; Shipley, P. Micro-Checker: Software for Identifying and Correcting Genotyping Errors in Microsatellite Data. Mol. Ecol. Notes 2004, 4, 535–538. [Google Scholar] [CrossRef]
  43. Yeh, F.C.; Yang, R.C.; Boyle, T.; Ye, Z.H. PopGene, the User-Friendly Shareware for Population Genetic Analysis; Molecular Biology and Biotechnology Centre, University of Alberta: Edmonton, AB, Canada, 1997. [Google Scholar]
  44. Nagy, S.; Poczai, P.; Cernák, I.; Gorji, A.M.; Hegedűs, G.; Taller, J. PICcalc: An Online Program to Calculate Polymorphic Information Content for Molecular Genetic Studies. Biochem. Genet. 2012, 50, 670–672. [Google Scholar] [CrossRef]
  45. Arthofer, W.; Steiner, F.M.; Schlick-Steiner, B.C. Rapid and Cost-Effective Screening of Newly Identified Microsatellite Loci by High-Resolution Melting Analysis. Mol. Genet. Genom. 2011, 286, 225. [Google Scholar] [CrossRef] [PubMed]
  46. Chen, C.; Zhou, P.; Choi, Y.A.; Huang, S.; Gmitter, F.G. Mining and Characterizing Microsatellites from Citrus ESTs. Theor. Appl. Genet. 2006, 112, 1248–1257. [Google Scholar] [CrossRef] [PubMed]
  47. Jung, H.; Lyons, R.E.; Dinh, H.; Hurwood, D.A.; McWilliam, S.; Mather, P.B. Transcriptomics of a Giant Freshwater Prawn (Macrobrachium rosenbergii): De Novo Assembly, Annotation and Marker Discovery. PLoS ONE 2011, 6, e27938. [Google Scholar] [CrossRef] [PubMed]
  48. Varshney, R.K.; Graner, A.; Sorrells, M.E. Genic Microsatellite Markers in Plants: Features and Applications. Trends Biotechnol. 2005, 23, 48–55. [Google Scholar] [CrossRef] [PubMed]
  49. Duan, B.; Mu, S.; Guan, Y.; Liu, W.; Kang, T.; Cheng, Y.; Li, Z.; Tian, Y.; Kang, X. Development of Microsatellite Markers Based on Transcriptome Sequencing and Evaluation of Genetic Diversity in Swimming Crab (Portunus trituberculatus). Front. Genet. 2022, 13, 932173. [Google Scholar] [CrossRef]
  50. Zhao, Y.; Zhu, X.; Li, Z.; Xu, W.; Dong, J.; Wei, H.; Li, Y.; Li, X. Genetic Diversity and Structure of Chinese Grass Shrimp, Palaemonetes sinensis, Inferred from Transcriptome-Derived Microsatellite Markers. BMC Genet. 2019, 20, 75. [Google Scholar] [CrossRef]
  51. Lerceteau-Köhler, E.; Weiss, S. Development of a Multiplex PCR Microsatellite Assay in Brown Trout Salmo trutta, and Its Potential Application for the Genus. Aquaculture 2006, 258, 641–645. [Google Scholar] [CrossRef]
  52. Sekino, M.; Kurokawa, T.; Sasaki, K. Multiplex PCR Panels of Novel Microsatellites for the Ark Shell Scapharca Broughtonii (Pteriomorphia, arcoida). Conserv. Genet. Resour. 2010, 2, 39–42. [Google Scholar] [CrossRef]
  53. Scribner, K.T.; Pearce, J.M. Microsatellites: Evolutionary and Methodological Background and Empirical Applications at Individual, Population, and Phylogenetic Levels. In Molecular Methods in Ecology; Baker, A.J., Ed.; Blackwell Science: Hoboken, NJ, USA, 2000; pp. 235–273. [Google Scholar]
  54. Yu, J.; Liao, S.; Liu, H.; Wang, W.; Cao, X. The Development of Novel EST-SSRs and Construction of Multiplex PCR Panels for Parentage Assignment in Macrobrachium rosenbergii. Aquaculture 2023, 570, 739440. [Google Scholar] [CrossRef]
  55. Ding, Q.; Shi, M.; Ji, P.; Qin, L.; Gao, X.; Zhang, X.; Jiang, Q. Genetic Diversity and Differentiation of Cultured Macrobrachium rosenbergii in China Using Newly Developed Microsatellite Multiplex PCR Panels. Aquacult. Int. 2024, 32, 7895–7910. [Google Scholar] [CrossRef]
  56. Wang, X.; Hua, Q.; Wu, L.; Weng, Z.; Huang, W.; Meng, Z. Development of a Multiplex Microsatellite Assay for Parentage Assignment in Orange-spotted Groupers (Epinephelus coioides). J. World Aquac. Soc. 2021, 52, 435–444. [Google Scholar] [CrossRef]
  57. Yılmaz, D.; Atasaral, Ş. Microsatellite-Based Parentage Assignment in Tiger Trout (Salmo trutta × Salvelinus fontinalis). Turk. J. Fish. Aquat. Sci. 2024, 24. [Google Scholar] [CrossRef]
  58. Wang, Q.; Hu, Q.; Yang, S.; Li, Y. Isolation of Tetrameric Microsatellite Markers and Its Application on Parentage Identification in Procambarus clarkii. Aquacult. Int. 2023, 31, 2099–2111. [Google Scholar] [CrossRef]
  59. Zhang, J.; Ma, W.; Wang, W.; Gui, J.-F.; Mei, J. Parentage Determination of Yellow Catfish (Pelteobagrus fulvidraco) Based on Microsatellite DNA Markers. Aquacult. Int. 2016, 24, 567–576. [Google Scholar] [CrossRef]
  60. Satyanarayana, Y.; Babu, P.G.; Jahageerdar, S. Parentage Determination in the Freshwater Prawn Macrobrachium rosenbergii de Man, 1879 Using Microsatellite Markers. Indian J. Fish. 2015, 62, 29–32. [Google Scholar]
  61. Yang, M.; Tian, C.; Liang, X.-F.; Lv, L.; Zheng, H.; Yuan, Y.; Zhao, C.; Huang, W. Parentage Determination of Mandarin Fish (Siniperca chuatsi) Based on Microsatellite DNA Markers. Biochem. Syst. Ecol. 2014, 54, 285–291. [Google Scholar] [CrossRef]
  62. Weinman, L.R.; Solomon, J.W.; Rubenstein, D.R. A Comparison of Single Nucleotide Polymorphism and Microsatellite Markers for Analysis of Parentage and Kinship in a Cooperatively Breeding Bird. Mol. Ecol. Resour. 2015, 15, 502–511. [Google Scholar] [CrossRef]
  63. Zhang, C.-L.; Tong, G.-X.; Kuang, Y.-Y.; Zhang, C.; Yin, J.-S. Applicability of Microsatellite DNA Markers to the Parental Identification of Hucho taimen (Pallas). Zool. Res. 2010, 31, 395–400. [Google Scholar] [CrossRef]
  64. Wesmajervi, M.S.; Westgaard, J.-I.; Delghandi, M. Evaluation of a Novel Pentaplex Microsatellite Marker System for Paternity Studies in Atlantic Cod (Gadus morhua L.). Aquac. Res. 2006, 37, 1195–1201. [Google Scholar] [CrossRef]
  65. Jones, A.G.; Small, C.M.; Paczolt, K.A.; Ratterman, N.L. A Practical Guide to Methods of Parentage Analysis. Mol. Ecol. Resour. 2010, 10, 6–30. [Google Scholar] [CrossRef]
  66. Hedgecock, D.; Li, G.; Hubert, S.; Bucklin, K.; Ribes, V. Widespread Null Alleles and Poor Cross-Species Amplification of Microsatellite Dna Loci Cloned from the Pacific Oyster, Crassostrea gigas. J. Shellfish Res. 2004, 23, 379–385. [Google Scholar]
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Figure 1. Distribution of different SSR types in Eriocheir sinensis transcriptome. A total of 1,521,519 putative SSRs were identified from the transcriptome data of E. sinensis, with 45.72% being di-nucleotide repeats, 42.42% tri-nucleotide repeats, 9.31% tetra-nucleotide repeats, 2.26% penta-nucleotide repeats, and 0.29% hexa-nucleotide repeats.
Figure 1. Distribution of different SSR types in Eriocheir sinensis transcriptome. A total of 1,521,519 putative SSRs were identified from the transcriptome data of E. sinensis, with 45.72% being di-nucleotide repeats, 42.42% tri-nucleotide repeats, 9.31% tetra-nucleotide repeats, 2.26% penta-nucleotide repeats, and 0.29% hexa-nucleotide repeats.
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Figure 2. Part of gene scan results of four sets of multiplex PCR in Eriocheir sinensis. The blue peak is FAM fluorescent dye and the green peak is HEX fluorescent dye.
Figure 2. Part of gene scan results of four sets of multiplex PCR in Eriocheir sinensis. The blue peak is FAM fluorescent dye and the green peak is HEX fluorescent dye.
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Figure 3. The real success assignment rate and simulation success assignment rate of four sets of multiplex PCR. The actual identification rates were 70.05%, 41.55%, 46.38%, and 36.23% for sets 1, 2, 3, and 4, respectively, while simulated rates were 79.71%, 50.07%, 41.55%, and 54.59%.
Figure 3. The real success assignment rate and simulation success assignment rate of four sets of multiplex PCR. The actual identification rates were 70.05%, 41.55%, 46.38%, and 36.23% for sets 1, 2, 3, and 4, respectively, while simulated rates were 79.71%, 50.07%, 41.55%, and 54.59%.
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Figure 4. Assignment success rate of Eriocheir sinensis with different combinations of multiplex PCR panels. Combination 1 is set 1 + set 3, combination 2 is set 1 + set 3 + set 2, and combination 3 is set 1 + set 3 + set 2 + set 4. At a 95% confidence level, using the two highest-performing multiplex PCR sets (set 1 and set 3) achieved assignment accuracy rates of over 90.34%, while using three or four sets resulted in 100% correct offspring–parent allocation.
Figure 4. Assignment success rate of Eriocheir sinensis with different combinations of multiplex PCR panels. Combination 1 is set 1 + set 3, combination 2 is set 1 + set 3 + set 2, and combination 3 is set 1 + set 3 + set 2 + set 4. At a 95% confidence level, using the two highest-performing multiplex PCR sets (set 1 and set 3) achieved assignment accuracy rates of over 90.34%, while using three or four sets resulted in 100% correct offspring–parent allocation.
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Table 1. The number of progeny assigned to six families of Eriocheir sinensis.
Table 1. The number of progeny assigned to six families of Eriocheir sinensis.
FamiliesNumber of FemalesNumber of Males
C184
C21515
C31325
C41425
C51515
C64117
Table 2. Characteristics of the four sets of multiplex PCR panels of microsatellites in Eriocheir sinensis.
Table 2. Characteristics of the four sets of multiplex PCR panels of microsatellites in Eriocheir sinensis.
LocusRepeat MotifPrimer Sequences (5′−3′) aAnnealing Temperature Size Range (bp)Concentration in Multiplex-PCR (µmol·L−1)(GenBanK Accession No.)
PCR multiplex set 1 60 (°C)
CX140(CAA)4F: FAM-TCACCACCATCAGCAACA 196–2230.2MF686621
R: AGAAAGGACAGGGATAACC
CX287(GGC)5F: FAM-TCACTAGGATAGGTAACGGA 292–3270.4MF686652
R: CACTCAACACACGACAGGA
CX256(CCT)4F: HEX-ATGGCTCTCTCTGGTGGCT 359–4290.28MF686639
R: TGCTGAAATCGGAAGGTT
CX416(TGG)4F: FAM-GTAGTGTGAAGCAAGGAGGA 381–4450.36MF686673
R: GGAGTTTGAGGTGAGCGA
PCR multiplex set 2 60 (°C)
CX003(TATT)4F: FAM-CCCTCAACCTGAACCTCT 119–1870.2MF686585
R: ACTCTTTTTTGCTGCCCT
CX254(TCC)9F: FAM-CTATCCATCTCTCTACCTCCC 357–3740.24MF686638
R: GCAAAAACAAACCACTAACC
CX400(GAG)7F: HEX-ACCCTATCAAACTATTACGACC 401–4110.24MF754123
R: ATGAACACCGAGACCACG
CX427(GTG)4F: FAM-GGCATTCAATCTTAAACCC 415–4350.36MF686679
R: ATCTCCTCCTTCCCTCGT
PCR multiplex set 3 55 (°C)
CX074(CA)6F: FAM-GCAAAACTTGGCAAACGA 110–1190.2MF686606
R: ACCTCCACCCAGCACACT
CX013(GGA)4F: FAM-ACTACGACTTTTCTACTTACCC 167–1790.2MF686590
R: CCTCCTTATCCTCTTGCTC
CX298(CAC)4F: HEX-ACCCAAGCACACCTTCTCC 332–3810.2MF686657
R: CCAACCCTCCCAACCTAAT
CX312(TGG)6F: FAM-ATCACCAACACTGCTCTCG 399–4110.24MF686661
R: CTCAATGCTGCTGCCTCT
PCR multiplex set 4 55 (°C)
CX071(TGG)8F: HEX-GCTGGAGTCAGGGCTAAA 148–1710.24MF686604
R: CAAAACACACACGGAAGAA
CX118(AAG)4F: HEX-CCACAACTGCTGCTGATG 214–2640.2MF686612
R: TGTAGACGAAGGAAGGCT
CX325(CTC)5F: HEX-GAGCGATTAGTTACGGGA 322–3570.4MF686664
R: GAGGAAGGTGTGTTTGGA
CX435(CTC)5F: HEX-CATCATCTCCATCTCCTCTG 405–4470.4MF686683
R: ATCTGCACCCCTTCTCAC
Note: ‘a’ denotes the primer direction from 5′ to 3′ end.
Table 3. The universal and polymorphic potential of the 272 microsatellite markers in Eriocheir sinensis.
Table 3. The universal and polymorphic potential of the 272 microsatellite markers in Eriocheir sinensis.
Type of SSRNumber of SSRUniversalityPolymorphism
NumberProbability (%)NumberProbability (%)
Di-nucleotide5360.03100
Tri-nucleotide25919776.19347.2
Tetra-nucleotide4375.03100
Penta-nucleotide3133.300.0
Hexa-nucleotide111001100
Total27220575.410048.8
Table 4. Characteristics of the 16 microsatellite markers in Eriocheir sinensis.
Table 4. Characteristics of the 16 microsatellite markers in Eriocheir sinensis.
LocusNaHoHePICHWF(Null)
CX140150.4350.7240.697***0.2385
CX28790.8220.8670.850***0.0261
CX256210.9350.9090.900NS−0.0182
CX416100.4880.7670.736***0.2296
CX00380.3560.7580.731***0.3725
CX254150.8770.8520.833***−0.0179
CX40090.8490.7920.762***−0.0389
CX42790.6170.8380.816***0.1503
CX07490.6210.8580.840***0.1583
CX01370.6560.8100.785***0.0957
CX29870.6600.7780.746***0.0876
CX31270.7570.8080.780***0.0395
CX07190.6260.7340.701***0.0928
CX118110.5870.6560.633NS0.0334
CX325160.9510.8980.887NS−0.0314
CX435110.6810.8290.804**0.0929
Mean value10.810.6820.8050.781 0.0944
Note: Na, number of alleles; Ho, observed heterozygosity; He, expected heterozygosity; PIC, polymorphism information content; HW, Hardy–Weinberg equilibrium p value, NS: nonsignificant. All the p values of deviations from the HW equilibrium marked by asterisks are significant; significant deviations at ** p < 0.01, *** p < 0.001. F(Null), the null allele frequency.
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Xiao, Q.; Tang, M.; Li, Q.; Dong, P.; Cheng, Y.; Deng, D.; Wu, X. Development of Microsatellite Markers from Transcriptome of Eriocheir sinensis and Their Application in Multiplex PCR Panels. Animals 2024, 14, 3200. https://doi.org/10.3390/ani14223200

AMA Style

Xiao Q, Tang M, Li Q, Dong P, Cheng Y, Deng D, Wu X. Development of Microsatellite Markers from Transcriptome of Eriocheir sinensis and Their Application in Multiplex PCR Panels. Animals. 2024; 14(22):3200. https://doi.org/10.3390/ani14223200

Chicago/Turabian Style

Xiao, Qizhen, Meijun Tang, Qingqing Li, Pengsheng Dong, Yongxu Cheng, Deng Deng, and Xugan Wu. 2024. "Development of Microsatellite Markers from Transcriptome of Eriocheir sinensis and Their Application in Multiplex PCR Panels" Animals 14, no. 22: 3200. https://doi.org/10.3390/ani14223200

APA Style

Xiao, Q., Tang, M., Li, Q., Dong, P., Cheng, Y., Deng, D., & Wu, X. (2024). Development of Microsatellite Markers from Transcriptome of Eriocheir sinensis and Their Application in Multiplex PCR Panels. Animals, 14(22), 3200. https://doi.org/10.3390/ani14223200

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