SNP marker combination for identifying resistance to streptococcus agalactiae disease in rohu and application thereof

Abstract

The present application relates to the field of biotechnology, and particularly relates to SNP marker combination for identifying resistance to streptococcus agalactiae disease of tilapia and application thereof. The present application discloses 11 SNP sites in UTRN gene of tilapia which are significantly related to resistance to streptococcus agalactiae disease, and constructs 18 SNP marker combinations for identifying resistance phenotype of streptococcus agalactiae disease, which can effectively improve the accuracy and genetic stability of resistance phenotype identification. In addition, the SNP typing primer and method based on MALDI-TOF mass spectrometry platform provided by the present application can realize high-throughput and high-precision genotyping, and has important application value in resistance phenotype identification and assisted selection breeding.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of biotechnology, and particularly relates to a SNP marker combination for identifying the resistance of Oreochromis spp. to Streptococcus agalactiae disease and application thereof. BACKGROUND

[0002] Oreochromis spp. is a main characteristic freshwater fish species in China, and the yield accounted for 6.76% of the total yield of freshwater fish in 2023. However, since 2009, Streptococcus agalactiae disease has broken out on a large scale in China, the morbidity of the disease is 20%-30%, and the mortality is as high as 25%-80%, and it shows an upward trend year by year. The typical symptoms include exophthalmos, skin hemorrhage, internal organ hyperemia and encephalitis, and the infected fish shows abnormal behaviors such as spiral swimming, which is particularly harmful to adult fish. The current industry prevention and control mainly relies on antibiotic drugs, but long-term use has led to the emergence of multiple drug-resistant strains, and the vaccine immunization strategy is limited by the narrow window of susceptibility period of juvenile fish and the risk of environmental residue. Therefore, improving the disease resistance of the core population through breeding is the core path that meets the goal of sustainable development of the industry.

[0003] In the field of genetic breeding, although traditional phenotype selection can reduce the mortality rate of Streptococcus agalactiae disease in breeding populations by 30%-50%, the breeding efficiency is limited by the long period of phenotype determination, many environmental interference factors and other technical bottlenecks. With the increasing maturity of genomic selection breeding in livestock and poultry and aquatic animals, the breeding time is greatly shortened, and the accuracy of individual phenotype prediction is greatly improved. However, efficient genomic selection breeding requires accurate phenotype determination and high-density single nucleotide polymorphism (SNP) to obtain phenotype-related molecular markers through genome-wide association analysis (GWAS) and quantitative trait locus (QTL) mapping for molecular marker-assisted selection (MAS) or genomic selection (GS).

[0004] Genomic selection is one of the best ways to breed disease resistance because it can avoid the impact of toxicological challenge experiments on the health of parents. The resistance of tilapia to streptococcosis is a quantitative trait controlled by multiple genes. For such traits, genomic selection often relies on effect SNPs to improve prediction accuracy and reliability. Although some studies have reported whole-genome association analysis for tilapia resistance to streptococcosis, candidate SNP sites are characterized by a small number, scattered distribution, and low genetic effect interpretation. Recently, domestic scholars constructed a high-density disease-resistant genetic linkage map of tilapia and located a QTL related to streptococcosis resistance on linkage group 9, with a confidence interval of 0.67-6.67 cM, containing five immune-related genes, but the authors believe that it is still necessary to improve the mapping precision and narrow the identification range of QTL related to tilapia resistance to streptococcosis. Therefore, for tilapia resistance to streptococcosis, it is urgent to develop more effect SNP sites related to the phenotype to achieve efficient and accurate resistance identification and genomic-assisted selection breeding. SUMMARY

[0005] Therefore, the present application provides a SNP marker combination for identifying the resistance of tilapia to streptococcosis and its application.

[0006] The technical scheme of the present application is as follows:

[0007] In the first aspect, the present application provides a SNP marker combination for identifying the resistance of tilapia to streptococcosis, which consists of a first SNP molecular marker and a second SNP molecular marker, and the SNP marker combination comprises at least one of A1-A18 (Table 1) as follows:

[0008] Table 1 SNP marker combination for identifying the resistance of tilapia to streptococcosis

[0009]

[0010]

[0011] The dominant genotype of tilapia resistance to streptococcosis in A1-A18 is as follows:

[0012]

[0013] The dominant genotype of tilapia resistance to streptococcosis in A11 is as follows: the first SNP molecular marker genotype is AA, and the second SNP molecular marker genotype is GG; or the first SNP molecular marker genotype is GA, and the second SNP molecular marker genotype is AA.

[0014] The SNP sites are shown in Table 2 as follows:

[0015] Table 2 Information of 11 SNP loci of Tilapia

[0016]

[0017]

[0018] Reference Nile Tilapia genome O_niloticus_UMD_NMBU.

[0019] In a second aspect, the present application provides use of the SNP marker combination in identifying resistance of Tilapia to Streptococcus agalactiae.

[0020] In a third aspect, the present application provides use of the SNP marker combination in breeding Tilapia resistant to Streptococcus agalactiae.

[0021] In a fourth aspect, the present application provides use of a product for detecting the SNP marker combination in identifying resistance of Tilapia to Streptococcus agalactiae or breeding Tilapia resistant to Streptococcus agalactiae.

[0022] In a fifth aspect, the present application provides a method for identifying resistance of Tilapia to Streptococcus agalactiae, which comprises determining the genotype of a sample to be tested, wherein the genotype is the genotype of the SNP marker combination.

[0023] In a sixth aspect, the present application provides a method for breeding Tilapia resistant to Streptococcus agalactiae, which comprises determining the genotype of the SNP marker combination of claim 1 in an individual to be tested, selecting an individual with a superior genotype as a parent for breeding, and obtaining offspring with improved resistance to Streptococcus agalactiae.

[0024] Further, in some specific embodiments, the genotype determination is performed using MALDI-TOF based on time-of-flight mass spectrometry. The PCR primer sequences used are selected from SEQ ID NO: 1-33. Specifically, the required primers are selected according to the SNP loci to be detected, and the correspondence is as follows: SEQ ID NO: 1-3 are used to detect the genotype of SNP31, SEQ ID NO: 4-6 are used to detect the genotype of SNP28,

[0025] SEQ ID NO: 7-9 are used to detect the genotype of SNP34, and SEQ ID NO: 10-12 are used to detect the genotype of SNP49,

[0026] SEQ ID NO: 13-15 are used to detect the genotype of SNP27, and SEQ ID NO: 16-18 are used to detect the genotype of SNP30,

[0027] SEQ ID NO: 19-21 are used to detect the genotype of SNP29, and SEQ ID NO: 22-24 are used to detect the genotype of SNP32,

[0028] The SNP32 genotype was detected using SEQ ID NO:19-21, and the SNP50 genotype was detected using SEQ ID NO:22-24.

[0029] The SNP52 genotype was detected using SEQ ID NO:25-27, and the SNP33 genotype was detected using SEQ ID NO:28-30.

[0030] The SNP35 genotype was detected using SEQ ID NO:31-33.

[0031] The beneficial effects of the present invention include at least the following:

[0032] This invention successfully constructed 18 SNP marker combinations (A1-A18) related to Streptococcus agalactiae resistance in the functional region of the UTRN gene in tilapia. Specifically, the resistance-dominant genotype combinations formed by specific SNP sites enabled precise identification of the Streptococcus agalactiae resistance phenotype. The study showed that individuals carrying the dominant genotype combinations of the molecular markers had significantly longer survival times and significantly lower phenotypic variation coefficients after Streptococcus agalactiae challenge, indicating that the molecular markers can effectively improve the accuracy and genetic stability of resistance phenotype identification.

[0033] This invention simultaneously developed based on The SNP genotyping primers and methods of the MALDI-TOF mass spectrometry platform can meet the technical requirements of high-throughput and high-precision genotyping in modern aquaculture breeding. In terms of breeding applications, using individuals carrying advantageous resistance genotype combinations as core breeding parents through targeted selection can effectively shorten the breeding cycle and has significant industrial application value. Attached Figure Description

[0034] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0035] Figure 1 Distribution of survival time of 494 Nile tilapia after challenge with Streptococcus agalactiae;

[0036] Figure 2 The resequencing SNP density distribution of 494 Nile tilapia (challenged by Streptococcus agalactiae);

[0037] Figure 3Whole genome association analysis of survival time of tilapia infected with S. agalactiae, showing SNP sites significantly associated with survival time after S. agalactiae infection; Figure 3 Fig. 2: a Manhattan plot; b a Quantile-Quantile plot (Q-Q plot);

[0038] Figure 4 Functional enrichment analysis of S. agalactiae resistance candidate genes of tilapia; Figure 4 Fig. 3: a GO biological process (BP) enrichment analysis; b a KEGG pathway enrichment analysis; (Note: the size of the bubble is proportional to the number of enriched genes, and the red intensity is proportional to the negative logarithm of significance, and the dark red bubble represents strong association);

[0039] Figure 5 Fig. 4: SNP-based survival time prediction of tilapia infected with S. agalactiae; Fig. 5: MALDI-TOF typing verification and phenotype association analysis of S. agalactiae resistance candidate SNP of tilapia; a-k (subplots) respectively represent the typing results of the individual to be tested at the corresponding SNP site, and in each subplot: the upper part represents the scatter plot of the typing results, and each color represents a genotype; the lower part is the interval plot of the association analysis between genotype and survival time, and if there is no significant correlation, no significance is marked;

[0040] Figure 6 Fig. 6: Association analysis between the genotype combination of SNP52 and another 10 SNPs and the corresponding survival time of tilapia, the horizontal coordinate represents the genotype combination in 012 format (see Table 11) (for example Figure 6 In subplot a, the horizontal coordinate value "00", the left digit "0" represents the genotype GG of SNP52, and the right digit "0" represents the genotype AA of SNP27), and the vertical coordinate represents the survival time; the column interval represents the survival time interval (greater than 95% confidence) corresponding to the genotype; the significance statistics are represented by lowercase letters, and if the lowercase letters are different, it means that there is statistical significance (p<0.05) between the two groups;

[0041] Figure 7 Fig. 7: Correlation between the genotype combination of the remaining 10 SNPs except SNP52 and the survival time; among the 15 SNP pairs displayed in this figure, there are significant differences in survival time of individuals with different genotypes; on the contrary, the SNP pairs not displayed in this figure indicate that there is no significant difference in survival time of individuals with different genotypes formed by the SNP pairs; the horizontal coordinate represents the genotype combination in 012 format, and the vertical coordinate represents the survival time; the column interval represents the survival time interval (greater than 95% confidence) corresponding to the genotype; the significance statistics are represented by lowercase letters, and if the lowercase letters are different, it means that there is statistical significance (p<0.05) between the two groups; the black arrow represents the genotype combination significantly associated with S. agalactiae resistance after screening. Detailed Implementation

[0042] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below. Obviously, the described embodiments are only some embodiments of this invention, not all embodiments. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this invention. Where specific conditions are not specified in the embodiments, conventional conditions or conditions recommended by the manufacturer shall be followed. Reagents or instruments whose manufacturers are not specified are all conventional products that can be purchased commercially.

[0043] Example 1

[0044] I. Materials and Methods

[0045] 1. Collection of laboratory animals and experimental samples

[0046] The Nile tilapia strain used in this invention was purchased from a tilapia breeding farm in Guangdong. 1710 healthy and uniformly sized tilapia were evenly reared in four cement culture ponds (length:width:height = 3:3:1.5m). During the feeding period, the water temperature was maintained at 28±2℃, dissolved oxygen was controlled at 6.0–7.5 mg / L, pH was stabilized at 7.5–8.5, and total ammonia nitrogen was maintained at <0.1 mg / L. The fish were fed twice daily, at 7:00 AM and 7:00 PM, using commercially available formulated feed. One month prior to the experiment, all fish were tagged with a passive integrated transponder (PIT), and approximately 2 cm of their tail fins were collected and immersed in anhydrous ethanol, then stored at -20℃ for later use.

[0047] 2. Preparation of Streptococcus agalactiae bacterial suspension

[0048] The *Streptococcus agalactiae* WC1535 in the examples can be found in the reference (Zhang Meiyan, Zhu Weijuan, Liu Zhigang, et al. Differential expression analysis of plasma proteomics before and after infection with *Streptococcus agalactiae* in tilapia [J]. Journal of Dalian Ocean University, 2024, 39(04): 559-567. DOI: 10.16535 / j.cnki.dlhyxb.2023-288.), which can be obtained by the public from the Pearl River Fisheries Research Institute of the Chinese Academy of Fishery Sciences. The applicant promises to distribute the biological material to the public within twenty years from the date of application.

[0049] Streptococcus agalactiae WC1535 was stored at -80°C. The activated strain was streaked on Brain-Heart Infusion Broth (BHI) solid medium and incubated at 37°C overnight. Single colony was picked and put into 10 mL liquid BHI medium and incubated at 37°C with shaking for 24 hours (h) until the colony grew to log phase (OD460nm ~ 1.5, about 10 600 colony forming units, CFU / mL). The bacterial solution was washed 3 times and resuspended in sterile 1x PBS. The bacterial solution concentration was measured using BioMerieux turbidity meter. Then, the final concentration of the bacterial solution was adjusted to 3.8 x 10 8 CFU / mL. The final concentration of the bacterial solution was obtained from pre-experiment. Briefly, 4 groups of WC1535 challenge experiments were set up, with 10 biological replicates for each group. The challenge doses were 10 6 , 10 9 , 10 8 , 10 7 , 10 6 CFU / mL, respectively. After the challenge of S. agalactiae, the number of dead individuals in each group was recorded within 7 days, and then the median lethal dose (LD 50 ) was calculated using online software LD 50 calculator (https: / / www.aatbio.com / tools / ld50-calculator) with default parameters. The LD 50 of this experiment was 3.8 x 10 6 CFU / mL.

[0050] 3. Challenge experiment, phenotype recording

[0051] From the above population, 494 fish were randomly selected and evenly distributed in 5 500L fish tanks as the challenge group. Another 100 fish were kept in a 500L tank as the control group. The water quality and feeding conditions were the same as in 1, but the temperature was increased to 31±1°C. After two weeks of training, the S. agalactiae challenge experiment was carried out. In the challenge group, 100 uL of bacterial solution was injected into the abdominal cavity of the fish. In the control group, 100 uL of sterile 1xPBS was injected into the abdominal cavity of the fish. The survival time of each group was recorded in hpi. When recording the survival time, the typical S. agalactiae disease symptoms such as exophthalmos, skin ulceration, and spiral swimming were observed, and the fish condition was compared with the control group to determine the death caused by S. agalactiae. The survival time was recorded immediately after the last fish was injected with the bacterial solution. For example, if a fish died 1 hour after infection, the survival time was recorded as 1 hpi. During this period, the survival time was recorded every 0.5 hours. After 5 days of infection, the survival time was recorded every 12 hours. In this experiment, the challenge group still had individuals with typical S. agalactiae disease symptoms that died after 14 days of challenge, but no individuals died in the following 5 days. During this period, the control group fish did not have S. agalactiae disease symptoms and no fish died. Therefore, the phenotype of the dead fish in the challenge group was collected up to 14 days after challenge, and the fish that survived after 14 days were recorded as 336 hpi. The above 494 individuals were resequenced. At the same time, the remaining 1116 fish in the cement pond were subjected to MassARRAY genotyping verification. The specific experimental grouping and phenotype collection method are shown in Example 3.

[0052] 4. DNA extraction, library construction and resequencing

[0053] Genomic DNA was extracted from the caudal fin according to the instructions of the HiPure Tissue DNA Micro Kit (Magen, Guangzhou, China). The DNA integrity and concentration were detected by 1.5% agarose gel electrophoresis and Nanodrop 2000 spectrophotometry. Double-end libraries were constructed according to the NEBNext Ultra II DNA Library Preparation Kit (NEB, Beijing, China). High-throughput sequencing was performed by Bomei Biotechnology Co., Ltd. (Beijing, China) on the BGISEQ-500 platform (Huada Gene, Shenzhen, China) with a read length of 150 bp.

[0054] 5. SNP scanning, genotyping and heritability analysis

[0055] Raw data were quality controlled using fastQC (v 0.12.1) with default parameters. Then, the quality controlled data were back-mapped to the reference genome of O. niloticus UMD_NMBU (https: / / www.ncbi.nlm.nih.gov / datasets / genome / GCF_001858045.2 / ) using bwa (v0.7.17) with default parameters. Then, the mapped files were sorted and indexed using Samtools (v 1.21). After the bam files for indexing were generated, GATK HaplotypeCaller (v4.0) was used to call SNPs and generate gVCF files for each individual. High quality SNPs were filtered from each individual’s gVCF using the following parameters: QD < 2.0, MQ < 40.0, FS > 60.0, QUAL < 30.0, MQrankSum < -12.5, ReadPosRankSum < -8.0, -clusterSize 2, -clusterWindowSize 5. Finally, all gVCF files were merged using GATK GenomicsDBImport (v4.0) and GATK GenotypeGVCF (v4.0) was used to genotype the merged gVCF data to generate a single VCF file containing the detected variants for all samples.

[0056] SNP position annotation was performed using snpEff software (v3.6c) based on the reference genome O_niloticus_UMD_NMBU. The positions of SNPs were classified into intergenic regions, upstream or downstream regions, and exonic or intronic regions. In addition, SNPs in coding exons were further classified into synonymous or nonsynonymous mutations.

[0057] Genetic analyses of heritability were performed using GCTA (v1.94.4). First, a GRM matrix was generated using the --make-grm parameter. The generated matrix was used to calculate the heritability under the --grm qc parameter. Principal component analysis (PCA) was performed on SNPs using the smartPCA program in EIGENSOFT (v.7.2.1) with default parameters.

[0058] II. Results

[0059] 1. Descriptive statistics of binary disease resistance phenotype and resequencing data

[0060] A total of 494 O. niloticus were challenged with S. agalactiae and survival time after infection was used as the phenotype. The results showed that there were 257 surviving individuals and 237 dead individuals (Table 1). Figure 1). After resequencing the individuals, about 5,794 Gb of high-quality data was obtained. The GC content was 40.24%, and Q30>97.00%. A total of 4,058,381 SNPs were obtained, which were unevenly distributed in all 22 LGs. The SNP marker density of LG3 was the highest, and that of LG19 was the lowest Figure 2

[0061] 2. Estimation of genetic force of survival time of tilapia infected with Streptococcus agalactiae

[0062] After infection with Streptococcus agalactiae, SNP-based genetic parameter estimation found that the genetic force of survival time was 0.0805, with low genetic force (Table 3). This result shows that the population has not been artificially selected. Through selective breeding, the genetic force of survival time can be improved, and the resistance of the offspring of the population to Streptococcus agalactiae disease can be improved. Therefore, it is necessary to explore the SNP sites related to survival time to improve the selection efficiency of the population.

[0063] Table 3 SNP-based genetic parameter estimation of disease-resistant binary phenotypes

[0064] Genetic parameters Coefficient of variation Standard error Additive variance component 1203.289 1072.114 Residual variance component 13743.58 1362.215 Phenotypic variance component 14946.86 952.3558 Heritability 0.080504 0.071407

[0065] Example 2 GWAS analysis and functional gene annotation of candidate SNP sites

[0066] I. Methods

[0067] PLINK 1.9 was used to filter low-quality variations with the parameters: --maf 0.05, --max-missing 0.8. The filtered VCF file was converted to PLINK format using VCFtools (v0.1.16) for GWAS analysis. The GWAS used the LM model. The present application used the GEMMA (v.0.96) program to run the LM model. The influence of population stratification was corrected by adjusting the first three PCs.

[0068] y = Wα + xβ + μ + e (1)

[0069] In this formula, the covariate W is the first 3 PCs, X is the genotype, and Y is the phenotype. The genomic kinship μ is obtained by the -gk1 program in the GEMMA software.

[0070] ​The present application annotates the function of the candidate SNP site, queries the function gene within 100 kb upstream and downstream of the SNP according to the decay distance between SNPs, uses annnovar (v4.7) software, takes O_niloticus_UMD_NMBU as a reference genome, and encodes the SNP obtained by association analysis by using the function of annotate_variation.pl based on the SNP region (-regionanno) to perform gene annotation.

[0071] The annotated gene set is subjected to GO and KEGG enrichment analysis. The background annotation set is all coding genes in O_niloticus_UMD_NMBU. The protein sequences of all genes are extracted by TBtools (v1.6) and subjected to GO and KEGG annotation in EGGNOG (http: / / eggnog-mapper.embl.de / ). Then, the candidate gene is taken as an input sequence, and all annotated genes are taken as a background gene set, and GO and KEGG enrichment analysis is performed on the OmicShare (https: / / www.omicsmart.com / # / ) platform.

[0072] II. Results

[0073] After GWAS analysis, the SNPs significantly associated with survival time are located in 13 different chromosomes (Table 4). Figure 3 ) But the clustered SNPs are mainly located in LG15, LG19 and LG20 (clustered SNPs ≥ 8) Figure 3 a) Among them, LG15 is rich in 15 SNPs significantly associated with the disease resistance phenotype, and forms a SNP cluster of 0.15M Figure 3 a, Table 4). The 9 phenotype significantly associated SNPs on LG19 form a SNP cluster of 3.17M Figure 3 a, Table 4). The 14 phenotype significantly associated SNPs on LG20 form a SNP cluster of 1.93M Figure 3 a, Table 4). But compared with the SNP cluster on LG15, the SNP density in the cluster on LG19 and LG20 is lower, and the distance between SNPs is farther (Table 4).

[0074] Table 4 SNPs significantly associated with survival time of tilapia after streptococcus agalactiae infection

[0075]

[0076]

[0077]

[0078] As shown in Table 4, a total of 201 coding genes were annotated in the candidate SNPs. GO enrichment analysis showed that the annotated genes were significantly related to the interleukin-27-mediated signaling pathway Figure 4 In a), and KEGG analysis showed that the above genes were significantly enriched in the intestinal immune network for IgA production, SNARE interactions in vesicular transport, and Notch signaling pathway Figure 4 In b). Both interleukin-27 and the intestinal immune network for IgA production are important immune regulation pathways in the body, indicating that the above candidate SNP annotated genes are related to the immune regulation of Streptococcus agalactiae disease in tilapia.

[0079] Since the number of trait-related SNPs distributed on LG15 is the largest, and they are closely distributed in clusters. Therefore, the present application further performs gene annotation analysis on the 0.15M SNP cluster formed by LG15. The results show that the SNP cluster is located on utrophin (UTRN) (Table 4). UTRN contains 79 exons and is expressed in various tissues of mammals, and is highly expressed in the lung, intramuscular fat, and skeletal muscle. Knockout of this gene will cause inflammation and fibrosis in the skeletal muscle of mice. This is similar to the pathological symptoms of skin and muscle tissue ulceration in Streptococcus agalactiae disease in tilapia, indicating that this gene may play an important regulatory function in streptococcosis.

[0080] Example 3 MassARRAY mixed pool genotyping of mutation sites

[0081] I. Method

[0082] 1. Preparation of Streptococcus agalactiae solution, challenge experiment, and collection of survival time phenotypes

[0083] The preparation of Streptococcus agalactiae solution is the same as in Example 1. The challenge and phenotype collection methods are as follows. 1116 fish in the cement pond were divided into two groups, of which 1016 tilapia were in the experimental group (challenge group), and 100 tilapia were in the control group. The experimental group of tilapia was trained in three cement ponds with the specifications shown in Example 1, and the control group was raised in a 500L glass fish tank. The feeding conditions were the same as in Example 1. After two weeks of training, the water temperature was raised to 31±1℃, and then the experimental and control groups were injected with bacterial solution or PBS according to the method shown in Example 1. Individuals that died due to infection with Streptococcus agalactiae were screened according to typical symptoms of streptococcosis such as bulging eyes, skin ulceration, and spiral swimming.

[0084] Survival time phenotype collection was as shown in Example 1. In this experiment, the last individual showing typical symptoms of streptococcosis was at 10 days post challenge. During this period, no symptoms or death was observed in the control group. Therefore, the survival time phenotype of the dead fish in the challenge group was recorded as 10 days post challenge (≤240hpi), and the survival time phenotype of the surviving fish in the challenge group was recorded as 250hpi.

[0085] 2. Correlation analysis between SNPs in UTRN gene and survival time after S. iniae infection

[0086] Firstly, based on the evaluation of the sequence information of 15 candidate SNPs in UTRN gene and 100 bp upstream and downstream thereof, it was found that a mass pool typing method of 11 SNPs therein could be constructed based on MassARRAY (Table 5). Each SNP was typed into three genotypes, and corresponded to the genotypes shown in Table 3. Using the primer design software Assaydesign 3.1 of Agena Bioscience Company, PCR reaction and single base extension primers (Table 6) were designed, and were synthesized by Shanghai Sangon.

[0087] Table 5 15 candidate SNPs located in UTRN gene

[0088]

[0089] Table 6 Primer sequences used for MassARRAY typing (5'-3')

[0090]

[0091]

[0092] Note: 2nd-PCR P and 1st-PCR P are the PCR upstream and downstream primers for amplifying each SNP; UEP_SEQ is a single base extension primer.

[0093] Then, the DNA of each sample was placed in a 384-well PCR plate for PCR reaction, shrimp alkaline phosphatase (SAP) digestion, single base extension reaction, resin purification and mass spectrometry detection. The PCR reaction system is shown in Table 7, and the reaction program is 94℃, 2 minutes (min); 94℃, 20 seconds (s), 56℃, 30s, 72℃, 60s, 45 cycles; 72℃, 3min. The PCR reaction used a hot start enzyme Max DNA Polymerase was used for amplification (R047A, TAKARA, Dalian). The reaction system of SAP (783901000UN, Thermo Fisher Scientific, Shanghai) is shown in Table 8. The reaction program was 37℃ for 40 min; 85℃ for 5 min. The single-base extension reaction was performed according to... The Pro Sample Identification Kit was used according to the operating instructions (13116F, Agena Bioscience, Shanghai). The reaction system is shown in Table 9, and the reaction procedure is shown in Table 10. For resin purification, first centrifuge the 384-well plate at 2000 rpm for 2 min. Then, add 16 μL of sterile double-distilled water to each well and centrifuge at 2000 rpm for 2 min. Next, evenly spread the resin on a 6MG 384-well plate and invert it above the 384-well PCR plate. Tap the 6MG plate to allow the resin to fall into the 384-well plate containing the single-base extension product. Invert the sealed PCR plate to ensure the resin fully integrates with the PCR product. Finally, centrifuge at 2000 rpm for 5 min and set aside. For mass spectrometry detection, first arrange the samples in the 384 wells according to the Typer (v4.0) software. Then, add the resin-purified samples sequentially according to the arranged sample order, adding 10 μL of sample to each well. Finally, following the operating instructions of the Typer Chip Linker (v2.0) software, the MassArray SNP typing mass spectrometer (SEQUENOM, San Diego, USA) was used to type and visualize the 11 SNPs to be detected in each sample.

[0094] Table 7 PCR reaction system

[0095] Reaction reagents Concentration Volume (μL) Double distilled water - 927.5 PCR Buffer (15 mM MgCl2) 10× 331.25 MgCl2 25 mM 172.25 dNTP Mix 25 mM 53 Primer Mix 0.5 uM 530 Hotstar Taq 5 U / μL 106 DNA template 10 ng / μL 1 / well Total - 5 / well

[0096] Table 8. Reaction system for SAP digestion

[0097]

[0098]

[0099] Table 9. Monobase Extension Reaction System

[0100] Reaction reagents Concentration Volume (μL) Double distilled water - 400.2 iPLEX Buffer plus 10× 106 iPLEX terminator NA 106 Primer Mix 0.6-1.3 μM 426.1 iPLEX enzyme NA 21.7 Total - 2 / well

[0101] Table 10 Cyclic parameters for monobase extension reaction

[0102]

[0103] 3. Data Statistics

[0104] After MassARRAY genotyping, the survival time between different genotypes was statistically analyzed using Minitab software (v21.0). The test model was Fisher's exact test. P<0.05 was considered statistically significant.

[0105] II. Results

[0106] The present application conducted a challenge experiment on 1016 Nile Tilapia, and obtained 918 surviving fish and 98 dead fish. After the challenge, the survival time of these fish was recorded. At the same time, the fin DNA of the above 1016 fish was used to perform MassARRAY genotyping on 11 SNPs. The results showed that the average genotyping success rate of the SNPs reached 95.81% (Table 11). However, statistical analysis found that the above individual SNPs had no significant correlation with survival time (p>0.05). Figure 5

[0107] Table 11 MassARRAY genotyping efficiency statistics

[0108]

[0109] The results of the present application on the correlation analysis of the genotype combination of the SNPs in the UTRN gene of Tilapia with Streptococcus agalactiae disease resistance showed that:

[0110] As shown in Figure 6 , the results used an interval chart to show the survival time interval of each genotype with more than 95% confidence. As shown in Figure 6 , the SNP marker 00 composed of SNP52 and SNP27 had a confidence of more than 95% in the interval of ~240-~250hpi. From the results, it can be seen that although SNP52 alone was not significantly correlated with survival time, among the 9 genotype combinations formed by SNP52 and SNP50, the survival time of genotypes 10, 20 and 21 (in 012 format) was significantly higher than that of other genotypes. More noteworthy is that the recessive homozygous genotype GG of SNP52 and the recessive homozygous genotypes of the other 9 SNPs (except SNP50) significantly improved the survival time of Tilapia after challenge. This is reflected in: (1) The survival time of 00 genotype fish was significantly higher than that of other genotypes; (2) The survival time interval of 00 genotype fish with more than 95% confidence was near 240-250hpi, which was the interval of surviving fish. The survival time intervals of other genotypes with more than 95% confidence all contained the interval below 240hpi, which was the interval of dead fish. Therefore, the 00 genotype combination was identified as the Streptococcus agalactiae disease resistance genotype of Tilapia, corresponding to SNP marker combinations A1-A9 and their dominant resistance genotypes in Table 12.

[0111] ​Table 12 SNP marker combinations and dominant resistance genotypes used to identify resistance to Streptococcus agalactiae in tilapia.

[0112]

[0113] like Figure 7 As shown, among the 10 SNPs (excluding SNP52) on UTRN, after pairing, 15 SNP pairs showed significant differences in survival time among individuals with different genotypes. Unlike the SNP combinations formed by SNP52, the number of individuals with a survival time range of 240-250 hpi for a particular genotype combination was small, which may be related to the low linkage effect between SNPs. Therefore, in addition to the confidence interval and significance criteria, this invention uses a third criterion of >20 individuals corresponding to a genotype to determine SNP combinations associated with streptococcal resistance. Based on these three criteria, this study identified 9 SNP marker combinations A10-A18 against streptococcal disease and their dominant resistance genotypes from the above 15 SNP pairs, as shown in Table 12. Figure 7 ).

[0114] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. Use of a product for detecting a combination of SNP markers in identifying resistance to Streptococcus agalactiae disease in Tilapia or in breeding Tilapia resistant to Streptococcus agalactiae disease, characterized in that, The SNP marker combination consists of a first SNP molecular marker and a second SNP molecular marker, and the SNP marker combination comprises at least one of A1-A9 as follows: ； The resistant dominant genotypes of A1-A9 of the Nile tilapia against Streptococcus agalactiae disease are as follows: ； The SNP sites are as follows: ； Reference: Nile tilapia genome O_niloticus_UMD_NMBU.

2. A method for identifying resistance to Streptococcus agalactiae disease in Tilapia, characterized in that, The genotype of the sample to be tested is determined, which is the genotype of the SNP marker combination of claim 1.

3. The method of claim 2, wherein, Genotyping was performed using MALDI-TOF mass spectrometry based on time-of-flight mass spectrometry using MassARRAY ® MALDI-TOF.

4. The method of claim 3, wherein, The PCR primer sequences used are selected from SEQ ID NO: 1-21 or SEQ ID NO: 25-33.

5. A method for breeding a rohu fish resistant to streptococcosis, characterized in that, The genotype of the SNP marker combination of claim 1 in the individual to be tested is determined, and the individual with the dominant genotype is selected as the parent for breeding to obtain offspring with improved resistance to Streptococcus agalactiae disease.

6. The method of claim 5, wherein, Genotype determination is performed using MassARRAY® MALDI-TOF based on time-of-flight mass spectrometry.

7. The method of claim 6, wherein, The PCR primer sequences used are selected from SEQ ID NO: 1-21 or SEQ ID NO: 25-33. Genotype determination is performed using MassARRAY® MALDI-TOF based on time-of-flight mass spectrometry.

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