Microsatellite panel and application thereof in genetic relationship identification of micropterus salmoides

Abstract

The invention provides a microsatellite panel and application thereof in genetic relationship identification of micropterus salmoides, and belongs to the technical field of aquatic genetic breeding. The SSR panel based on multiple PCR targeted sequencing is constructed around genetic relationship identification of the micropterus salmoides, the advantages in the aspects of operation efficiency, cost control, result accuracy, application range and the like are remarkable, and genetic research of the micropterus salmoides and related aquatic products is powerfully promoted. Compared with a traditional SSR analysis method and an SNP technology, the multiple PCR targeted sequencing technology is applied, the cost advantage is obvious in the construction and analysis process, the problem that the construction and analysis cost of the SNP technology is high is solved, and large-scale genetic relationship analysis is more feasible.

Description

Technical Field

[0001] The invention relates to the technical field of aquatic genetic breeding, in particular to a microsatellite panel and an application thereof in identification of genetic relationship of largemouth bass. Background Art

[0002] Microsatellite (SSR) markers, with their unique advantages of high polymorphism and codominance, occupy an important position in the field of genetic research, especially in genetic diversity analysis and kinship identification. Traditional SSR analysis methods mostly rely on manual operations, require cumbersome electrophoresis detection and manual interpretation, which is not only complicated in process but also extremely time-consuming. Each experiment from sample processing to result acquisition often takes several days, which is difficult to meet the timeliness requirements of large-scale genetic research. In addition, traditional typing methods rely on the length of the amplified sequence, which limits the number of multiplex PCR, resulting in a throughput of only 3-4 multiplexes. This limitation restricts the rapid analysis of large-scale population genetic characteristics and limits its application in areas such as species evolution research and large-scale germplasm resource screening. Although the SNP technology that has emerged in recent years has shown the advantages of high throughput and automation in some aspects, its construction and analysis costs are very high. To build a comprehensive and high-quality SNP marker system, a large amount of funds need to be invested in chip design, reaction system construction, and professional data analysis software, which is an unbearable economic burden for many scientific research institutions and enterprises. At the same time, with the same number of markers, SSR markers have higher polymorphism than SNP markers, and SNP markers cannot completely replace the role of SSR markers in specific genetic analysis scenarios. Focusing on largemouth bass, an important aquaculture species, there is currently a lack of efficient SSR panels based on multiplex PCR targeted sequencing technology. The scale of largemouth bass farming industry continues to expand, and high-throughput kinship analysis will help achieve accurate seed selection and breeding, improve farming efficiency, and reduce the problem of germplasm degradation caused by inbreeding. However, existing analysis technologies can hardly meet the urgent needs of largemouth bass farming populations for high-throughput kinship analysis, and cannot provide strong technical support for industrial development, which seriously hinders the scientific and efficient development of largemouth bass farming industry. Summary of the invention

[0003] The purpose of the present invention is to provide a microsatellite panel and its application in identification of kinship of largemouth bass, which solves the problems of low automation, low efficiency and difficulty in achieving high throughput in traditional SSR analysis methods.

[0004] In order to achieve the above-mentioned object of the invention, the present invention provides the following technical solutions:

[0005] The present invention provides a microsatellite panel, wherein the microsatellite panel contains a plurality of primer pairs targeting microsatellite loci;

[0006] The plurality of primer pairs targeting microsatellite loci include primers with nucleotide sequences as shown in SEQ ID NOs. 1 to 80.

[0007] The present invention provides a microsatellite panel, wherein the microsatellite panel contains a plurality of primer pairs targeting microsatellite loci;

[0008] The multiple primer pairs for microsatellite loci include:

[0009] A primer pair having nucleotide sequences as shown in SEQ ID NO.27 to SEQ ID NO.28;

[0010] A primer pair having nucleotide sequences as shown in SEQ ID NO.33 to SEQ ID NO.34;

[0011] A primer pair having nucleotide sequences as shown in SEQ ID NO.35 to SEQ ID NO.36;

[0012] A primer pair having nucleotide sequences as shown in SEQ ID NO.37 to SEQ ID NO.38;

[0013] A primer pair having nucleotide sequences as shown in SEQ ID NO.41 to SEQ ID NO.42;

[0014] A primer pair having nucleotide sequences as shown in SEQ ID NO.47 to SEQ ID NO.48;

[0015] A primer pair having nucleotide sequences as shown in SEQ ID NO.53 to SEQ ID NO.54;

[0016] A primer pair having nucleotide sequences as shown in SEQ ID NO.55 to SEQ ID NO.56;

[0017] A primer pair having nucleotide sequences as shown in SEQ ID NO.57 to SEQ ID NO.58;

[0018] A primer pair having nucleotide sequences as shown in SEQ ID NO.59 to SEQ ID NO.60;

[0019] A primer pair having nucleotide sequences as shown in SEQ ID NO.61 to SEQ ID NO.62;

[0020] A primer pair having nucleotide sequences as shown in SEQ ID NO.63 to SEQ ID NO.64;

[0021] A primer pair having nucleotide sequences as shown in SEQ ID NO.69 to SEQ ID NO.70;

[0022] A primer pair having nucleotide sequences as shown in SEQ ID NO.71 to SEQ ID NO.72;

[0023] The nucleotide sequences of the primer pairs are shown as SEQ ID NO.75 to SEQ ID NO.76.

[0024] The present invention provides a primer set, which is composed of a plurality of primer pairs targeting microsatellite loci;

[0025] The plurality of primer pairs targeting microsatellite loci include primers with nucleotide sequences as shown in SEQ ID NOs. 1 to 80.

[0026] The present invention provides a primer set, which is composed of a plurality of primer pairs targeting microsatellite loci;

[0027] The multiple primer pairs for microsatellite loci include:

[0028] A primer pair having nucleotide sequences as shown in SEQ ID NO.27 to SEQ ID NO.28;

[0029] A primer pair having nucleotide sequences as shown in SEQ ID NO.33 to SEQ ID NO.34;

[0030] A primer pair having nucleotide sequences as shown in SEQ ID NO.35 to SEQ ID NO.36;

[0031] A primer pair having nucleotide sequences as shown in SEQ ID NO.37 to SEQ ID NO.38;

[0032] A primer pair having nucleotide sequences as shown in SEQ ID NO.41 to SEQ ID NO.42;

[0033] A primer pair having nucleotide sequences as shown in SEQ ID NO.47 to SEQ ID NO.48;

[0034] A primer pair having nucleotide sequences as shown in SEQ ID NO.53 to SEQ ID NO.54;

[0035] A primer pair having nucleotide sequences as shown in SEQ ID NO.55 to SEQ ID NO.56;

[0036] A primer pair having nucleotide sequences as shown in SEQ ID NO.57 to SEQ ID NO.58;

[0037] A primer pair having nucleotide sequences as shown in SEQ ID NO.59 to SEQ ID NO.60;

[0038] A primer pair having nucleotide sequences as shown in SEQ ID NO.61 to SEQ ID NO.62;

[0039] A primer pair having nucleotide sequences as shown in SEQ ID NO.63 to SEQ ID NO.64;

[0040] A primer pair having nucleotide sequences as shown in SEQ ID NO.69 to SEQ ID NO.70;

[0041] A primer pair having nucleotide sequences as shown in SEQ ID NO.71 to SEQ ID NO.72;

[0042] The nucleotide sequences of the primer pairs are shown as SEQ ID NO.75 to SEQ ID NO.76.

[0043] The present invention provides a multiple microsatellite identification system, wherein the multiple microsatellite identification system comprises a microsatellite panel A, a microsatellite panel B, a microsatellite panel C and a microsatellite panel D;

[0044] The microsatellite panel A contains a plurality of primer pairs targeting microsatellite loci, including primers with nucleotide sequences as shown in SEQ ID NOs. 1 to 20;

[0045] The microsatellite panel B contains a plurality of primer pairs targeting microsatellite loci, including primers with nucleotide sequences as shown in SEQ ID NOs. 21 to 40;

[0046] The microsatellite panel C contains a plurality of primer pairs targeting microsatellite loci, including primers with nucleotide sequences as shown in SEQ ID NOs. 41 to 60;

[0047] The microsatellite panel D contains a plurality of primer pairs targeting microsatellite loci, including primers with nucleotide sequences as shown in SEQ ID NOs. 61 to 80.

[0048] The present invention provides a multiple microsatellite identification system, wherein the multiple microsatellite identification system comprises a microsatellite panel 1, a microsatellite panel 2 and a microsatellite panel 3;

[0049] The microsatellite panel 1 contains:

[0050] A primer pair having nucleotide sequences as shown in SEQ ID NO.27 to SEQ ID NO.28;

[0051] A primer pair having nucleotide sequences as shown in SEQ ID NO.33 to SEQ ID NO.34;

[0052] A primer pair having nucleotide sequences as shown in SEQ ID NO.35 to SEQ ID NO.36;

[0053] A primer pair having nucleotide sequences as shown in SEQ ID NO.37 to SEQ ID NO.38;

[0054] A primer pair having nucleotide sequences as shown in SEQ ID NO.41 to SEQ ID NO.42;

[0055] The microsatellite panel 2 contains:

[0056] A primer pair having nucleotide sequences as shown in SEQ ID NO.47 to SEQ ID NO.48;

[0057] A primer pair having nucleotide sequences as shown in SEQ ID NO.53 to SEQ ID NO.54;

[0058] A primer pair having nucleotide sequences as shown in SEQ ID NO.55 to SEQ ID NO.56;

[0059] A primer pair having nucleotide sequences as shown in SEQ ID NO.57 to SEQ ID NO.58;

[0060] A primer pair having nucleotide sequences as shown in SEQ ID NO.59 to SEQ ID NO.60;

[0061] The microsatellite panel 3 contains:

[0062] A primer pair having nucleotide sequences as shown in SEQ ID NO.61 to SEQ ID NO.62;

[0063] A primer pair having nucleotide sequences as shown in SEQ ID NO.63 to SEQ ID NO.64;

[0064] A primer pair having nucleotide sequences as shown in SEQ ID NO.69 to SEQ ID NO.70;

[0065] A primer pair having nucleotide sequences as shown in SEQ ID NO.71 to SEQ ID NO.72;

[0066] The nucleotide sequences of the primer pairs are shown as SEQ ID NO.75 to SEQ ID NO.76.

[0067] The invention provides a multiplex PCR detection kit for identifying the kinship of largemouth bass, which is characterized by comprising the above primer set.

[0068] The present invention provides the use of the microsatellite panel, primer set, multiple microsatellite identification system or multiple PCR detection kit in largemouth bass individual identification, parentage identification or population genetic analysis.

[0069] Beneficial effects of the present invention:

[0070] The present invention focuses on the identification of kinship of largemouth bass and constructs an SSR panel based on multiplex PCR targeted sequencing. It has significant advantages in operational efficiency, cost control, result accuracy and application scope, and has effectively promoted the genetic research of largemouth bass and related aquatic species.

[0071] The present invention uses multiplex PCR targeted sequencing technology to achieve high throughput and automation levels comparable to SNP panels. Compared with traditional SSR analysis methods, it greatly improves operating efficiency, overcomes the problems of low automation and difficulty in achieving high throughput in traditional methods, and can meet the needs of large-scale genetic research. It greatly reduces manual participation and reduces cost investment. Compared with traditional SSR analysis methods and SNP technology, the present invention has obvious cost advantages in the construction and analysis process, alleviates the problem of high construction and analysis costs of SNP technology, and makes large-scale kinship analysis more feasible.

[0072] The present invention uses optimized primer sets and bioinformatics algorithms to ensure the accuracy of kinship analysis in multiple links. When screening SSR sites, a variety of software and algorithms are used to construct primer matrices and calculate scores to select appropriate primer sets; in the data analysis stage, CERVUS software is used to perform a rigorous parentage analysis process, and thresholds are set by simulation and other methods to ensure that the analysis results are reliable. The parentage exclusion probability evaluation of four groups of ten-fold SSR panels showed that under the conditions of different known parents, the cumulative exclusion probability was high, and the accuracy of parentage assignment was significantly improved through the verification of different panel combinations and optimized primer sets. The SSR panel constructed by the present invention is not only suitable for largemouth black bass, but can also be extended to the kinship and genetic analysis of other aquatic species, providing a general and effective tool and method for genetic research of aquatic species. BRIEF DESCRIPTION OF THE DRAWINGS

[0073] Figure 1 is the chromosomal distribution of the loci in the SSR marker panel. These loci are distributed on 15 chromosomes (Chr1-Chr23), with no loci on chromosomes 2, 3, 5, 7, 11, 13, 19, and 22;

[0074] Figure 2 Heat map representation of sequencing reads for genotypes of four ten-plex SSR panels. Figures (A) to (D) show the distribution of reads for each locus and sample in each SSR panel, respectively. The color depth (from yellow to blue) represents the number of reads;

[0075] Figure 3 Cumulative parentage exclusion probabilities for four 10-fold SSR marker combinations. Figures (A) to (D) show the exclusion probabilities of each locus under three conditions: unknown parent (CE-1P), one parent known (CE-2P), and two parents known (CE-PP). The cumulative exclusion probability ranges from 0 to 1, where 0 indicates no exclusion ability and 1 indicates complete exclusion ability;

[0076] Figure 4 Cumulative exclusion probabilities and parentage accuracy rates at different loci and sample sizes are shown for the three optimized five-fold SSR marker combinations. (A) The figure shows the cumulative exclusion probabilities for a single parent (CE-1P), two parents (CE-2P), and a known parent pair (CE-PP) as the loci increase in sequence. The exclusion probability ranges from 0 to 1, where 0 indicates no exclusion ability and 1 indicates complete exclusion ability. (B) The figure shows the parentage accuracy rates for different situations (maternal only, paternal only, and parent pairs of known sex) and is evaluated as a function of sample size. The accuracy rate also ranges from 0 to 1, where 0 indicates a completely wrong identification result and 1 indicates a completely accurate identification result based on a complete consistency with the known parent pairing. DETAILED DESCRIPTION

[0077] The technical solutions provided by the present invention are described in detail below in conjunction with the embodiments, but they should not be construed as limiting the protection scope of the present invention.

[0078] Example

[0079] 1. Sample collection and DNA extraction:

[0080] The experimental samples of largemouth bass discussed in this study were obtained from four families with known pedigrees constructed in 2023 at the Zhejiang Provincial Fisheries Technology Extension Station in Hangzhou, Zhejiang Province, China. Four single-pair families (each family contained 1 father, 1 mother, and 10 offspring) were used for paternity testing. The pectoral fins or muscle tissues of all individuals were stored in anhydrous ethanol at -20°C. Genomic DNA was extracted using a magnetic bead-based tissue genomic DNA extraction kit (Wuhan Nanomagnetic Biotechnology Co., Ltd.). The concentration of each DNA sample was determined by an ND5000 spectrophotometer (Beijing Baitai Scientific Instrument Co., Ltd.). All DNA samples were diluted to a working concentration of 100 ng / μL and diluted with deionized water, and then stored at -20°C until subsequent use.

[0081] 2. SSR screening and primer design:

[0082] Six individuals of largemouth bass from three different populations were selected for simple sequence repeat (SSR) screening and development of multiplex PCR system. The samples were collected from Bolongkeng Reservoir in Quzhou City, Zhejiang Province, China, Anhui Zhanglin Fishery Co., Ltd. in Zhejiang Province, China, and Laibei Fishery Co., Ltd. in Panzhihua City, Sichuan Province, China, with two samples from each population. Genomic DNA was extracted by collecting pectoral fin tissue and preserving it in anhydrous ethanol using a magnetic bead-based tissue genomic DNA extraction kit (Wuhan Nanomagnetic Biotechnology Co., Ltd.).

[0083] Sequencing library preparation and sequencing were performed according to the Illumina whole-genome next-generation sequencing (NGS) protocol. Sequencing was performed by Shafei Bioinformatics Co., Ltd., Wuhan, China. Raw reads were quality controlled using Fastp v0.23.1 with the parameter set to -cD to ensure the generation of high-quality, clean reads. Clean reads were aligned to the largemouth bass reference genome GCA_022435785.1 using bwa-memev1.0.5 to generate SAM files. These SAM files were converted to BAM format using samtools v1.6, and duplicate reads were removed using sambamba v1.0.

[0084] The identification of single nucleotide polymorphisms (SNPs) was done by using the mpileup and call commands in the bcftools v1.8 software package, and the variant call format (VCF) files containing the SNPs were generated. For simple sequence repeats (SSRs), the VCF files were generated using the lobSTR v4.0.6 software. Based on these VCF files of SNPs and SSRs, the MultiplexSSR software was used to operate, setting the maximum amplified fragment length to 230 bp and the annealing temperature to 60°C to construct the SSR multiplex primer matrix, which was generated with the help of MultiPLX v1.4 software. MultiplexSSR can mask SNPs and SSRs in the target region, thereby improving the effectiveness of primer design. Subsequently, the SADDLE algorithm was applied to process the matrix, and the cumulative Badness score of each primer combination was calculated and arranged in ascending order of the score. This process made it possible to construct four decaplex primer combinations, and the badness score of each primer pair was calculated using Equation 1.

[0085]

[0086] When defining Badness, the summation process takes into account all reverse complementary subsequences between primers pa and pb that have at least four consecutive complementary bases. The parameters are defined as follows: len represents the length of the complementary subsequence. d1 and d2 represent the distance from the 3' end of primers pa and pb to the start of the complementary subsequence, respectively. numGC refers to the number of G / C bases in the subsequence. Together, these parameters form a Badness scoring system that assesses the likelihood of undesirable primer interactions based on the complementary regions of the primers, especially near the 3' end, which may affect PCR efficiency. A lower Badness score means that there is less likelihood of dimer formation between primer pairs, which results in less interference with multiplex PCR. Conversely, the formation of a large number of dimers will inhibit the multiplex PCR reaction and reduce the amplification efficiency of the target region.

[0087] We used a small amount (usually 2-6) of low-depth whole-genome resequencing data, adopted the MultiplexSSR method, set the maximum amplified fragment length to 230bp, and the annealing temperature to 60°C. With the help of MultiPLX v1.4 software, we generated an SSR multiplex primer matrix containing 40 primer sets, each of which contained 10 to 11 primers. Based on this multiplex primer matrix, we calculated all possible ten-plex primer combinations. The Badness score of each primer set was calculated using the SADDLE algorithm. According to the ascending order of the Badness score, the four ten-plex primer sets with the lowest scores were selected to construct four SSR panels. The chromosomal position of the corresponding locus is Figure 1 These loci cover 15 of the 23 chromosomes, with chromosomes 2, 3, 5, 7, 11, 13, 19, and 22 not involved.

[0088] Table 1 Characteristics of the four ten-fold SSR panels for largemouth bass

[0089]

[0090]

[0091]

[0092] The Badness score refers to the cumulative sum of the Badness values ​​of the ten PCR primers. The score is calculated for the forward primer and the reverse primer after adding the bridging sequence "ggagtgagtacggtgtgc (SEQ ID NO.81)" to the forward primer and the bridging sequence "gagttggatgctggatgg" (SEQ ID NO.82) to the reverse primer.

[0093] 3. Library construction and amplification:

[0094] Based on four groups of ten-plex primer sets, in order to meet the needs of Hi-TOM amplicon library preparation, a bridge sequence was added to each pair of primers: "ggagtgagtacggtgtgc" for the forward primer and "gagttggat gctggatgg" for the reverse primer. The purpose of introducing the bridge sequence is to connect with the Hi-TOM sequencing adapter for amplicon sequencing. Subsequently, the cumulative Badness score of each primer set was recalculated (the Badness score in Table 1 refers to the score obtained after the introduction of the bridge sequence.). PCR amplification was performed in a reaction volume of 50 μl, including 20 μl of each primer set (2 μl of each primer pair, 10 μM), 25 μl of Gold Multiplex PCR Mix (Jiangsu Kewen Biotechnology Co., Ltd.), and 5 μl of sample DNA. The PCR program included an initial denaturation at 95 °C for 10 min, followed by 10 cycles, each cycle consisting of 30 s at 95 °C, 30 s at the annealing temperature, and 30 s at 72 °C. This was followed by an additional 20 cycles, each cycle consisting of 30 s at 95 °C, 30 s at 50 °C, and 30 s at 72 °C. Finally, a final extension step was performed at 72 °C for 5 min. The initial annealing temperature was set at 60 °C and decreased by 1 °C per cycle. Equal amounts of PCR products were submitted for next-generation sequencing (NGS) on the Hi-TOM platform at the State Key Laboratory of Rice Biology, National Rice Research Institute, Chinese Academy of Agricultural Sciences, Hangzhou, China.

[0095] The raw paired-end reads obtained from Hi-TOM sequencing were quality controlled using Fastp v0.23.1 with parameters set to -c and -D to ensure the generation of high-quality, clean reads for subsequent analysis. These cleanreads were used as input for SSRseq for SSR genotyping. SSRseq first merged overlapping clean reads using FLASH v1.2.11, and only the merged overlapping reads were retained for downstream SSR genotyping. SSRseq then detected SSRs using BLAST v2.12 and applied its internal algorithm to mitigate the impact of PCR slippage effects during SSR genotyping. For each sample, the slippage ratio of the SSR sequence at the site was calculated according to the following formula:

[0096]

[0097] At a specific site, the slip ratio (SRn) of the SSR sequence is related to the number of repeats n, Fn-1 represents the reading frequency of the stutter peak with a repeat number of n-1, and Fn represents the reading frequency of the SSR sequence. The slip ratio is modeled as varying with the number of site repeats, and its mathematical expression is as follows:

[0098] SR n =an 2 +bn

[0099] a and b represent locus-specific coefficients. The coefficients a and b were estimated for each SSR locus by regression analysis between the mean slip ratio observed across samples and the number of SSR alleles replicated at each locus. For SSR sequences with missing observed slip ratios, the expected slip ratios were calculated using the equation. These slip ratios estimated based on high-quality samples were then used to correct for slip artifacts in all samples. The final output was an Excel file containing the SSR genotyping results with slip artifacts removed.

[0100] Based on the genotype data, the allele frequencies were analyzed using CERVUS v3.0.7 software. The genetic parameters calculated included the number of alleles (Na), observed heterozygosity (Ho), expected heterozygosity (He), polymorphism information content (PIC), Hardy-Weinberg equilibrium (HWE), and null allele frequency (Fnull). The HWE was evaluated using a minimum expected frequency of 3, and the significance test was performed using the Bonferroni correction method. The calculation formula for the comprehensive exclusion probability (CEP) is as follows:

[0101] CEP=1-NEP1×NEP2×NEP3×...×NEPn

[0102] Where NEPn represents the non-exclusion probability of the nth SSR locus.

[0103] Four single-pair families (each family contains 1 father, 1 mother, and 10 offspring) were genotyped by SSRseq technology, involving four ten-plex PCR SSR panels. SSRse q technology first uses FLASH to merge paired-end reads, and then uses BLAST to screen the merged reads containing SSR for genotyping. When using SSRseq for typing, it is necessary to ensure that the SSR is a perfect SSR, that is, the repeat sequence must be an integer multiple of the motif and cannot contain SNPs or any insertions or deletions. Therefore, for SSRs with multiple motifs at the same position, we split them. Among them, the number before the "_" symbol represents the site number of the SSR, and the number after the "_" symbol represents the number of different motifs at the site.

[0104] In panel A, the average percentage of pooled reads was 92.33%, of which 77.38% were usable for genotyping. Panel B had an average pooled read percentage of 96.17%, of which 84.94% were usable for genotyping. The corresponding percentages for panel C were 95.90% and 80.58%, respectively, while panel D had an average pooled read percentage of 95.79%, of which 79.40% were usable for genotyping. We then quantified the number of reads per locus that were usable for genotyping in each SSR panel and present the results in Figure 2 Of note, we observed significant differences in read counts per locus between samples within each SSR panel. Panel B had the lowest coefficient of variation in read counts, with a mean of 0.347, while panel D had the highest coefficient of variation, at 0.749. Despite this variability, all loci in all panels were successfully genotyped, with the exception of locus 440 in panel D. As SSRseq has a minimum read requirement of 30 for reliable genotyping, this locus failed genotyping in ten samples due to insufficient numbers of reads. All other loci exceeded this threshold, with minimum read counts well above the limit: locus 362 had a minimum of 34 reads, locus 870 had 93, locus 164 had 279, locus 304 had 367, locus 881 had 503, locus 614 had 520, locus 760 reached 747, and so on, as Figure 2 shown.

[0105] CERVUS v3.0.7 software was used to perform polymorphism statistical analysis on amplicon sequencing data. As shown in Table 2, the sample size for genotyping ranged from 38 to 48, and the success rate of locus genotyping for all samples reached 98.2%. The number of alleles (Na) varied from 2 to 7, with an average of 4.06. Among them, the average Na of the C group panel was the highest, at 4.36, while the average Na of the D group panel was the lowest, at 3.69. The observed heterozygosity (Ho) ranged from 0.104 to 1.000, with an average of 0.929, while the expected heterozygosity (He) ranged from 0.101 to 0.754, with an average of 0.589. The polymorphism information content (PIC) ranged from 0.098 to 0.713, with an average of 0.507. In addition, we found that most sites had high Ho values, usually close to or equal to 1, but a few sites (such as 614_3, 42, and 730) had low Ho values, which may be the reason for the deviation from Hardy-Weinberg equilibrium.

[0106] Table 2. Polymorphism statistics of four ten-fold SSR marker panels for largemouth bass

[0107]

[0108]

[0109] Note: NS: no significant difference (P>0.05); ***: extremely significant difference (P<0.001); **: highly significant difference (P<0.01); *: significant difference (P<0.05); ND: no significant difference analysis was performed; HWE: Hardy-Weinberg equilibrium; F (Null): null allele frequency.

[0110] 4. Data analysis and kinship identification:

[0111] Parentage analysis was performed using CERVUS v3.0.7 software in a three-stage process. First, the allele frequency was calculated for each SSR locus using the allele frequency module as described previously. Second, the simulation module was used to estimate the threshold log-likelihood ratio (LOD) score for parental pairings, assuming that the sex was known. In this stage, 10,000 offspring were simulated, with four candidate mothers and four candidate fathers. Based on the allele frequency analysis, the loci proportions were genotyped, and the minimum number of genotyped loci was set to 5. The simulation process used the default genotyping error rate of 1%, and set a strict confidence level of 95% and a loose confidence level of 90% to calculate the LOD score. Finally, the parental assignment module assigned the parental pairing to each offspring based on the genotype data and the simulated LOD threshold. The accuracy of the parentage test was verified by comparing the results with the known pedigrees, ensuring the reliability and consistency of the results.

[0112] The parentage exclusion probabilities were evaluated for four 10-fold SSR panels. When both parents were unknown (E-1P), the exclusion probabilities for individual loci ranged from 0.005 to 0.353; when one parent was known (E-2P), the exclusion probabilities ranged from 0.051 to 0.536; and when both parents were known (E-PP), the exclusion probabilities ranged from 0.096 to 0.728. Figure 3 It revealed that when both parents were unknown, the cumulative exclusion probabilities of all panels exceeded 0.9, with the cumulative exclusion probability of panel A reaching 0.964. If one parent was known, the cumulative exclusion probabilities of panels A, B, C, and D were 0.998, 0.987, 0.997, and 0.992, respectively. When both parents were known, the cumulative exclusion probabilities of each panel were close to 1.0: 0.99997 for panel A, 0.99928 for panel B, 0.99995 for panel C, and 0.99961 for panel D.

[0113] Simulation analysis of parental pairing assignments was performed for the four panels, assuming that parental information was known. Parental pairings were assigned to each offspring based on the LOD thresholds simulated for each panel. In panel A, the simulated and actual assigned parental pairing rates reached 100% in all categories (sole mother, sole father, or parental pairing) at both strict and relaxed confidence levels. In panel B, the simulated and actual assigned parental pairing rates reached 100% at the relaxed confidence level. However, at the strict confidence level, the simulated assigned parental pairing rates were 98%, 95%, and 100%, respectively, while the actual assigned parental pairing rates were 60%, 57%, and 100%, respectively. In panel C, the simulated assigned parental pairing rates were 100% at both confidence levels, while the actual assigned parental pairing rates were 93%, 100%, and 95%, respectively. In panel D, the simulated and actual assigned parental pairing rates reached 100% at both strict and relaxed confidence levels.

[0114] To verify the accuracy of the four SSR panels in parentage identification, four single-pair pedigrees (each family contains 1 father, 1 mother, and 10 offspring) were used for verification. As shown in Table 3, although each SSR panel achieved the capacity of ten-plex PCR and contained more than 10 loci, the accuracy of any individual panel was low. When the sex was known, the highest accuracy of identifying parent pairs was only 0.5, while the accuracy was slightly improved when the mother or father was evaluated separately. By testing multiple combinations of the four SSR panels, it was found that the B+C+D panel combination had the highest accuracy: the accuracy of the mother alone was 0.775, the accuracy of the father alone was 0.975, and the accuracy of the parent pair of known sex was 0.750. On the basis of the B+C+D combination, the site selection was further optimized by removing sites that may interfere with paternity identification. This resulted in an optimal primer set containing 15 primer pairs (368, 555, 565, 760, 12, 317, 715, 768, 795, 861, 172, 42, 700, 703, 777) covering a total of 22 loci. This optimized set improved the accuracy to 0.90 for the mother alone, 0.95 for the father alone, and 0.85 for parent pairs of known sex.

[0115] Table 3 Accuracy of parentage determination of four sets of ten-fold SSR marker panels for largemouth bass

[0116]

[0117]

[0118] Note: To verify the accuracy of parentage identification of four ten-fold SSR marker combinations (A, B, C, and D), we used 48 individuals (40 offspring and 8 parents) for verification. Finally, a set of the best 15 primer pairs (368, 555, 565, 760, 12, 317, 715, 768, 795, 861, 172, 42, 700, 703, and 777) was selected, covering a total of 22 loci. The accuracy value ranges from 0 to 1, where 0 indicates a completely wrong identification result and 1 indicates a completely accurate identification result based on the alignment with the true parent pair.

[0119] Among the 15 pairs of primers initially designed, the cumulative Badness score was as high as 8372.0163, indicating that potential problems such as primer dimer formation may be encountered in the multiplex PCR reaction, which may reduce the amplification efficiency of the multiplex PCR. In order to alleviate these problems, the present invention reorganized the primers into three five-fold SSR panels: Panel 1 (368, 555, 565, 760, 12), Panel 2 (317, 715, 768, 795, 861) and Panel 3 (172, 42, 700, 703, 777). The Badness scores of these panels were 45.8104, 40.0378 and 11.8396, respectively, which effectively reduced the interference between primers. The optimized primer set was validated by using 104 offspring samples to evaluate its accuracy in parentage assignment. Figure 4 As shown, the accuracy of the optimized primer set was 0.9423 in the assignment of mother alone, 0.9712 in the assignment of father alone, and 0.9135 in the assignment of parent pairs with known sex.

[0120] As can be seen from the above embodiments, the present invention aims at the deficiencies of traditional SSR analysis methods and prior art in the identification of largemouth bass kinship, and constructs an SSR panel based on multiplex PCR targeted sequencing for largemouth bass kinship identification through a series of steps. It has the advantages of high efficiency, low cost, high accuracy and wide applicability.

[0121] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A microsatellite panel, characterized in that: The microsatellite panel contains a plurality of primer pairs targeting microsatellite loci; The plurality of primer pairs targeting microsatellite loci include primers with nucleotide sequences as shown in SEQ ID NOs. 1 to 80.

2. A microsatellite panel, characterized in that: The microsatellite panel contains a plurality of primer pairs targeting microsatellite loci; The multiple primer pairs for microsatellite loci include: A primer pair having nucleotide sequences as shown in SEQ ID NO.27 to SEQ ID NO.28; A primer pair having nucleotide sequences as shown in SEQ ID NO.33 to SEQ ID NO.34; A primer pair having nucleotide sequences as shown in SEQ ID NO.35 to SEQ ID NO.36; A primer pair having nucleotide sequences as shown in SEQ ID NO.37 to SEQ ID NO.38; A primer pair having nucleotide sequences as shown in SEQ ID NO.41 to SEQ ID NO.42; A primer pair having nucleotide sequences as shown in SEQ ID NO.47 to SEQ ID NO.48; A primer pair having nucleotide sequences as shown in SEQ ID NO.53 to SEQ ID NO.54; A primer pair having nucleotide sequences as shown in SEQ ID NO.55 to SEQ ID NO.56; A primer pair having nucleotide sequences as shown in SEQ ID NO.57 to SEQ ID NO.58; A primer pair having nucleotide sequences as shown in SEQ ID NO.59 to SEQ ID NO.60; A primer pair having nucleotide sequences as shown in SEQ ID NO.61 to SEQ ID NO.62; A primer pair having nucleotide sequences as shown in SEQ ID NO.63 to SEQ ID NO.64; A primer pair having nucleotide sequences as shown in SEQ ID NO.69 to SEQ ID NO.70; A primer pair having nucleotide sequences as shown in SEQ ID NO.71 to SEQ ID NO.72; The nucleotide sequences of the primer pairs are shown as SEQ ID NO.75 to SEQ ID NO.

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3. A primer set, characterized in that: It is composed of multiple primer pairs targeting microsatellite loci; The plurality of primer pairs targeting microsatellite loci include primers with nucleotide sequences as shown in SEQ ID NOs. 1 to 80.

4. A primer set, characterized in that: It is composed of multiple primer pairs targeting microsatellite loci; The multiple primer pairs for microsatellite loci include: A primer pair having nucleotide sequences as shown in SEQ ID NO.27 to SEQ ID NO.28; A primer pair having nucleotide sequences as shown in SEQ ID NO.33 to SEQ ID NO.34; A primer pair having nucleotide sequences as shown in SEQ ID NO.35 to SEQ ID NO.36; A primer pair having nucleotide sequences as shown in SEQ ID NO.37 to SEQ ID NO.38; A primer pair having nucleotide sequences as shown in SEQ ID NO.41 to SEQ ID NO.42; A primer pair having nucleotide sequences as shown in SEQ ID NO.47 to SEQ ID NO.48; A primer pair having nucleotide sequences as shown in SEQ ID NO.53 to SEQ ID NO.54; A primer pair having nucleotide sequences as shown in SEQ ID NO.55 to SEQ ID NO.56; A primer pair having nucleotide sequences as shown in SEQ ID NO.57 to SEQ ID NO.58; A primer pair having nucleotide sequences as shown in SEQ ID NO.59 to SEQ ID NO.60; A primer pair having nucleotide sequences as shown in SEQ ID NO.61 to SEQ ID NO.62; A primer pair having nucleotide sequences as shown in SEQ ID NO.63 to SEQ ID NO.64; A primer pair having nucleotide sequences as shown in SEQ ID NO.69 to SEQ ID NO.70; A primer pair having nucleotide sequences as shown in SEQ ID NO.71 to SEQ ID NO.72; The nucleotide sequences of the primer pairs are shown as SEQ ID NO.75 to SEQ ID NO.

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5. A multiple microsatellite identification system, characterized in that: The multiple microsatellite identification system comprises microsatellite panel A, microsatellite panel B, microsatellite panel C and microsatellite panel D; The microsatellite panel A contains a plurality of primer pairs targeting microsatellite loci, including primers with nucleotide sequences as shown in SEQ ID NOs. 1 to 20; The microsatellite panel B contains a plurality of primer pairs targeting microsatellite loci, including primers with nucleotide sequences as shown in SEQ ID NOs. 21 to 40; The microsatellite panel C contains a plurality of primer pairs targeting microsatellite loci, including primers with nucleotide sequences as shown in SEQ ID NOs. 41 to 60; The microsatellite panel D contains a plurality of primer pairs targeting microsatellite loci, including primers with nucleotide sequences as shown in SEQ ID NOs. 61 to 80.

6. A multiple microsatellite identification system, characterized in that: The multiple microsatellite identification system comprises microsatellite panel 1, microsatellite panel 2 and microsatellite panel 3; The microsatellite panel 1 contains: A primer pair having nucleotide sequences as shown in SEQ ID NO.27 to SEQ ID NO.28; A primer pair having nucleotide sequences as shown in SEQ ID NO.33 to SEQ ID NO.34; A primer pair having nucleotide sequences as shown in SEQ ID NO.35 to SEQ ID NO.36; A primer pair having nucleotide sequences as shown in SEQ ID NO.37 to SEQ ID NO.38; A primer pair having nucleotide sequences as shown in SEQ ID NO.41 to SEQ ID NO.42; The microsatellite panel 2 contains: A primer pair having nucleotide sequences as shown in SEQ ID NO.47 to SEQ ID NO.48; A primer pair having nucleotide sequences as shown in SEQ ID NO.53 to SEQ ID NO.54; A primer pair having nucleotide sequences as shown in SEQ ID NO.55 to SEQ ID NO.56; A primer pair having nucleotide sequences as shown in SEQ ID NO.57 to SEQ ID NO.58; A primer pair having nucleotide sequences as shown in SEQ ID NO.59 to SEQ ID NO.60; The microsatellite panel 3 contains: A primer pair having nucleotide sequences as shown in SEQ ID NO.61 to SEQ ID NO.62; A primer pair having nucleotide sequences as shown in SEQ ID NO.63 to SEQ ID NO.64; A primer pair having nucleotide sequences as shown in SEQ ID NO.69 to SEQ ID NO.70; A primer pair having nucleotide sequences as shown in SEQ ID NO.71 to SEQ ID NO.72; The nucleotide sequences of the primer pairs are shown as SEQ ID NO.75 to SEQ ID NO.

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7. A multiplex PCR detection kit for identifying the relationship of largemouth bass, characterized in that: Comprising the primer set as described in claim 3.

8. A multiplex PCR detection kit for identifying the genetic relationship of largemouth bass, characterized in that: Comprising the primer set as described in claim 4.

9. Use of the microsatellite panel of claim 1 or 2, the primer set of claim 3 or 4, the multiple microsatellite identification system of claim 5 or 6, or the multiple PCR detection kit of claim 7 or 8 in individual identification, parentage testing, or population genetic analysis of largemouth bass.

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