Application of a set of SNP sites in detecting anti-nnv character of swai

Abstract

The application belongs to the technical field of aquatic genetics, and particularly relates to application of a group of SNP sites in detection of anti-NNV traits of Hippocampus kuda. The application provides a group of molecular marker combinations containing 680 core SNP sites, and sequences of the SNP sites are shown as SEQ ID NO. 1 to SEQ ID NO. 680. The SNP sites are obtained by whole genome resequencing of a reference population of H. kuda, and are screened by using MLE-rank algorithm to be significantly associated with anti-NNV traits. Based on the SNP combinations, the application further provides corresponding liquid phase chips, genomic selection breeding methods and kits. The technology can realize early and accurate evaluation of anti-NNV traits of H. kuda, significantly improve breeding efficiency, reduce detection cost, and provide an effective molecular breeding tool for breeding of H. kuda disease-resistant fine varieties.

Description

Technical Field

[0001] This invention belongs to the field of aquatic genetics technology, specifically involving the application of a set of SNP loci in detecting the NNV resistance trait in bloated seahorses. Background Technology

[0002] In recent years, the seahorse industry has developed rapidly, with seahorse farming in some regions forming a significant industrial cluster effect, effectively driving regional economic development. Meanwhile, as a traditional and valuable medicinal material, it is rich in various active ingredients and has broad application prospects in the fields of biomedicine and health.

[0003] However, with increasing stocking densities, frequent disease outbreaks have become a key bottleneck restricting the sustainable development of the industry. In particular, viral necrosis (VNN), primarily caused by Nervous Necrosis Virus (NNV), frequently breaks out in seahorse farming. This disease is characterized by high infectivity, rapid spread, and extremely high mortality; the mortality rate after infection in the seedling stage often reaches 100%, causing huge economic losses to the aquaculture industry. Since there are currently no effective drug treatments for NNNV, genetically improving breeds and cultivating superior varieties with resistance to NNNV has become an urgent priority to address this industry pain point. Traditional breeding mainly relies on phenotypic observation, but disease resistance traits (especially resistance to viral infection) are difficult to directly measure, resulting in long breeding cycles and low efficiency. In contrast, Genomic Selection (GS) breeding technology has significant advantages. This technology utilizes high-density molecular markers covering the entire genome to accurately assess the genomic breeding value (GEBV) of an individual without relying on phenotype. This makes it possible to screen for parents with high resistance to NNV at the genetic level in the early stages of breeding, which can significantly shorten the breeding cycle, reduce breeding costs, and provide a core driving force for the high-quality development of the seahorse seed industry.

[0004] The key to implementing genomic selection breeding lies in low-cost, high-throughput genotyping. However, the high cost of whole-genome resequencing for large-scale candidate populations hinders its widespread adoption in industry. cGBS (Capture-based Genotyping by Sequencing) liquid-phase chip technology, based on next-generation sequencing's targeted capture, can perform high-depth, high-precision genotyping of target SNP loci, offering high throughput, low cost, and high flexibility. However, in the field of hippocampal breeding, no dedicated genotyping chip has been reported outside of existing technologies. Therefore, there is an urgent need to develop a low-cost, high-throughput whole-genome SNP liquid-phase chip for hippocampal breeding and establish a supporting genomic selection breeding technology system against neural necrosis virus (NNV) to fill the industry gap and meet the pressing need for breeding superior hippocampal strains resistant to NNV. Summary of the Invention

[0005] The purpose of this invention is to provide a set of SNP sites for the detection of anti-NNV traits in the distended hippocampus.

[0006] To achieve the above-mentioned objectives, the present invention provides the following technical solution:

[0007] This invention provides the application of a set of SNP sites for anti-neural necrosis virus in the detection of anti-neural necrosis virus phenotype in the hippocampus. The SNP sites are located in the reference genome of the hippocampus, GCA_018466805.1, and the SNP site sequences are shown in SEQ ID NO.1~SEQ ID NO.680.

[0008] This invention also provides the application of the above-mentioned SNP sites in the preparation of liquid-phase chips for genomic selection breeding of bloated hippocampus against nerve necrosis virus.

[0009] This invention provides a liquid-phase chip for genomic selection breeding of bloated hippocampus against neuronecrosis virus, the chip comprising probes that specifically capture / detect the aforementioned SNP sites.

[0010] In this invention, the SNP sites are obtained through screening using the following method:

[0011] (1) Immersion challenge experiment was conducted on the bloated seahorse. After the experiment, live tissue was collected and preserved to obtain the reference population.

[0012] In the actual operation of this invention, a reference population of 672 bloated seahorses was constructed, and an immersion challenge experiment was conducted in seawater containing neuronecrosis virus (NNV). During the experiment, fin tissue from dead individuals was collected daily, and the time of death was recorded; after the experiment, fin tissue from all surviving individuals was collected. All samples were stored in anhydrous ethanol for subsequent DNA extraction.

[0013] (2) Perform whole-genome resequencing on the reference population to obtain high-quality SNP loci;

[0014] The quality control criteria for high-quality SNP sites include:

[0015] Sites with an average sequencing depth < 10X were removed; sites with a deletion rate ≥ 0.05 were removed; sites with a minimum allele frequency ≤ 0.05 were removed; and sites with Hardy-Weinberg equilibrium P ≤ 0.00001 were removed.

[0016] In the actual operation of this invention, whole-genome resequencing was performed on the aforementioned 672 samples using the MGI DNBSEQ-T7 (BGI T7) sequencing platform. The sequencing data were aligned to the bloated hippocampus reference genome (version number: GCA_018466805.1), and variant detection (SNP calling) was performed using GATK software. Strict quality control was then implemented, with the following filtering criteria: removal of sites with a missing rate > 0.05, removal of sites with a minimum allele frequency (MAF) < 0.05, and removal of sites significantly deviating from Hardy-Weinberg equilibrium (HWE, P value < 0.00001). Ultimately, 4,161,222 high-quality SNP sites were obtained.

[0017] (3) Analyze high-quality SNP sites and extract significantly associated sites as SNP sites;

[0018] In the actual operation of this invention, the MLE-rank (maximum likelihood estimation ranking) algorithm is used to analyze high-quality SNP sites, extracting the top 680 significantly associated sites to form a core SNP marker combination for low-density liquid-phase microarrays. This combination includes probes capable of specifically capturing or detecting 680 SNP sites in the reference genome of the bloated hippocampus; the sequences of the 680 SNP sites are SEQ ID NO.1 to SEQ ID NO.680, with the 36th base indicated by the degenerate base y / r; the SNP sites of this invention are shown in Table 1.

[0019] This invention provides the application of liquid phase chips in the genomic selection breeding of expanded hippocampus against nerve necrosis virus.

[0020] This invention also provides a method for breeding bloated seahorses resistant to nerve necrosis virus, the breeding method comprising the following steps:

[0021] (1) Genotyping of the bloated hippocampus individuals was performed using the liquid phase chip provided above to obtain genotypic data of SNP molecular markers;

[0022] (2) Calculate the genomic breeding value of an individual based on genotype data;

[0023] (3) Based on the genomic breeding value, individuals with high resistance to nerve necrosis virus were selected as parents for breeding;

[0024] When the genomic breeding value ranks high in the candidate population, the bloated hippocampus individual is considered to be a bloated hippocampus with high resistance to nerve necrosis virus.

[0025] In the actual operation of this invention, the ranking is preferably among the top 680.

[0026] To verify the effectiveness of the 680 core SNP loci in genomic selection breeding, this invention implemented multi-dimensional verification:

[0027] Prediction accuracy verification: In an independent validation population, the prediction accuracy and genetic parameter estimation effects based on genomic breeding value (GEBV) based on 680 core SNP loci and all SNP loci in the whole genome were compared and analyzed to evaluate the information representativeness of low-density microarrays.

[0028] Verification of actual breeding effects: BayesA and GBLUP prediction models were constructed using phenotypic and genotypic data from a reference population. The genotypes of 22 parents from 11 families were detected using the liquid-phase chip described in this invention. The average GEBV of the paternal and maternal parents was calculated as the predicted breeding value for each family, and correlation analysis was performed between this value and the actual survival rate of the families. The results showed a significant correlation, confirming that the 680 core SNP loci provided by this invention can be efficiently applied to disease-resistant genomic selection breeding.

[0029] Beneficial effects:

[0030] 1. Few markers, significant genetic effects: The SNP molecular marker combinations provided by this invention have the characteristics of "low density and high effect". The number of core SNP loci screened by the algorithm is small (only 680), but the genetic effect is strong. It can capture key genetic variation information in the population with a very small amount of data, thereby achieving extremely high evaluation accuracy in genomic selection breeding.

[0031] 2. High marker detection rate: The SNP markers described in this invention have high minimum allele frequency, high detection rate, and high genotyping accuracy. These characteristics ensure that the chip has extremely high reliability and universality in the detection of bloated hippocampal populations from different batches and sources, overcoming the instability of traditional markers in complex backgrounds.

[0032] 3. Accelerated Genetic Progress: Addressing the challenges of directly observing and detecting the high mortality rate of resistance to neuronecrosis virus in the bloated hippocampus, this invention achieves early, non-destructive determination of disease resistance traits through precise estimation of genomic breeding values. This effectively avoids losses caused by late-stage infections, significantly shortens the breeding generation interval, and dramatically accelerates the genetic improvement process of disease resistance traits.

[0033] 4. Outstanding cost-effectiveness, promoting industrial application: Compared with whole-genome resequencing, the chip described in this invention reduces costs by more than 50%, while improving prediction accuracy by more than 50% compared with traditional methods. This "low-cost, high-throughput, and high-accuracy" technology system makes it possible to perform low-cost testing on a large number of individuals, laying a solid foundation for the large-scale and industrial application of molecular breeding for disease resistance in swollen hippocampus. Attached Figure Description

[0034] Figure 1 The physical location of high-quality SNP sites in the hippocampal genome;

[0035] Figure 2 To estimate the accuracy of GEBV predictions in the reference population based on BayesA and GBLUP. Detailed Implementation

[0036] The present invention will be further described in detail below with reference to specific embodiments. These embodiments are only used to explain the present invention and are not intended to limit the scope of the present invention. Unless otherwise specified, the experimental methods used in the following embodiments are conventional methods; the materials and reagents used are commercially available unless otherwise specified.

[0037] Example 1

[0038] Scheme for obtaining and preparing core SNP sites in a chip

[0039] 1. Construction and phenotypic determination of a reference population of distended hippocampus

[0040] The bloated seahorse population used in this invention originated from a family established by the Fujian Provincial Fisheries Research Institute. To screen for disease resistance traits, a method simulating a natural infection environment was employed to conduct nerve necrosis virus immersion challenge experiments.

[0041] Sample composition: The experiment selected 672 bloated seahorses from 50 families bred in 2022 and 2024 as the research subjects. Among them, 600 individuals were randomly selected to form the reference population, and the remaining 71 individuals were used as the validation population.

[0042] Phenotypic data collection: All individuals were infected with neuronecrosis virus by immersion. During the experiment, fin tissue was collected from all subjects' hippocampi, and the time of death of deceased individuals, the survival status of surviving individuals, and other relevant phenotypic data were recorded in detail.

[0043] 2. High-throughput sequencing and SNP variant detection

[0044] Sequencing library construction: Genomic DNA was extracted from the collected samples and, after passing quality testing, a standard next-generation sequencing DNA library was constructed. High-throughput sequencing was performed using the MGI DNBSEQ-T7 sequencing platform, with a sequencing depth of approximately 7 Gb per sample.

[0045] Alignment and SNP detection: The filtered clean reads were aligned to the bloated hippocampus reference genome (version GCA_018466805.1) using BWA software. Based on the alignment results, variant detection (SNP calling) was performed using the GATK software workflow.

[0046] Quality control and genotyping: The generated raw VCF files underwent rigorous quality control using VCFtools software (v0.1.13). Filtering criteria included: removal of sites with an average sequencing depth < 10X; removal of sites with a missing rate ≥ 0.05; removal of sites with a minimum allele frequency (MAF) ≤ 0.05; and removal of sites significantly deviating from Hardy-Weinberg equilibrium (HWE, P ≤ 0.00001). Subsequently, genotyping was performed on the missing sites using Beagle software, ultimately yielding a genotype file (named HI.vcf) containing the reference population and 4,161,222 high-quality SNP sites.

[0047] 3. Screening of highly genetically related loci

[0048] Using filtered high-quality SNP loci and obtained phenotypes, the importance of each SNP was calculated based on the MLE-rank model for the reference population samples collected in 2022 / 2024. and The final SNP was obtained by weighting the number of individuals from 181 in 2022 to 491 in 2024. The value is implemented as follows:

[0049]

[0050] according to The SNPs were sorted from largest to smallest value. The top 680 SNPs were selected and merged with the markers obtained in the first step to form the 680 core SNP loci described in this invention (as shown in Table 1). The physical locations of high-quality SNP loci on the hippocampal genome are shown in Table 1. Figure 1 As shown.

[0051] Table 1 SNP loci

[0052] NO. Position Mutation Type NO. Position Mutation Type NO. Position Mutation Type NO.1 1:25867171 G / A NO.228 7:12303334 C / A NO.455 11:18574330 C / A NO.2 4:4752162 A / G NO.229 4:4444944 A / C NO.456 5:12346065 C / T NO.3 7:12318922 G / A NO.230 3:23271084 C / T NO.457 7:13611578 G / A NO.4 4:4564264 T / C NO.231 5:15162398 G / C NO.458 4:865436 G / A NO.5 5:2224357 C / T NO.232 5:15344962 G / A NO.459 7:15887805 T / G NO.6 4:1143076 C / G NO.233 6:10348828 A / T NO.460 4:5564966 G / T NO.7 4:4723452 G / A NO.234 8:18195760 G / A NO.461 4:3763845 G / A NO.8 4:4602301 G / A NO.235 5:14582765 C / A NO.462 4:4782047 G / C NO.9 8:11296650 G / A NO.236 14:753636 A / T NO.463 5:14773112 C / G NO.10 7:19605980 C / A NO.237 8:14511191 A / T NO.464 4:4523323 G / A NO.11 8:11841904 T / C NO.238 9:9509792 C / A NO.465 4:4528587 G / A NO.12 9:9267054 A / T NO.239 1:26093206 A / T NO.466 8:14391331 T / C NO.13 5:3456940 C / T NO.240 11:8979037 C / A NO.467 6:1748537 G / T NO.14 4:4769578 A / G NO.241 5:12743621 A / G NO.468 6:13657347 T / A NO.15 12:19128615 A / C NO.242 17:8440842 C / T NO.469 18:6208531 G / A NO.16 6:11814253 G / T NO.243 2:9197646 T / G NO.470 4:4588697 T / G NO.17 8:8542961 G / A NO.244 1:25851923 A / T NO.471 13:15894178 C / T NO.18 1:28021830 T / A NO.245 21:7083464 G / A NO.472 4:7203284 A / T NO.19 11:17904771 T / A NO.246 6:24429778 C / A NO.473 6:1205213 T / C NO.20 4:17415962 C / T NO.247 11:9839257 T / A NO.474 14:17290443 C / G NO.21 9:9801343 T / G NO.248 18:473482 A / T NO.475 18:6416942 C / T NO.22 5:13878995 G / T NO.249 13:5669952 C / T NO.476 15:9310738 C / T NO.23 6:13657250 G / A NO.250 15:7397566 A / T NO.477 9:9267058 A / G NO.24 4:5168745 C / T NO.251 11:9277549 A / T NO.478 13:15304169 T / C NO.25 4:4437424 A / C NO.252 4:4565333 A / C NO.479 14:3239886 C / A NO.26 4:5172272 A / T NO.253 4:4437596 G / A NO.480 4:3997430 T / C NO.27 4:4527675 C / T NO.254 4:5172323 A / T NO.481 5:15342197 C / G NO.28 3:8425607 G / A NO.255 8:11410187 G / A NO.482 9:9509806 C / G NO.29 11:18295480 C / A NO.256 4:11181025 G / T NO.483 4:4752167 G / T NO.30 6:13657306 T / G NO.257 4:4561865 G / A NO.484 4:4723104 A / G NO.31 7:13261956 T / C NO.258 5:15153177 T / C NO.485 14:5425680 T / C NO.32 15:8373563 G / T NO.259 4:4697400 T / C NO.486 13:16152377 C / T NO.33 4:4579841 C / T NO.260 1:25852805 G / A NO.487 4:4708635 C / A NO.34 4:4414542 C / T NO.261 4:4642945 A / T NO.488 8:11841900 C / T NO.35 17:8409488 T / A NO.262 11:18574271 T / A NO.489 11:10493004 C / T NO.36 3:7386974 G / T NO.263 5:13268226 C / T NO.490 4:4401209 G / C NO.37 4:4386306 G / A NO.264 18:6700143 A / G NO.491 4:4528846 A / T NO.38 6:13657294 G / A NO.265 4:4701338 C / T NO.492 4:5056080 T / A NO.39 4:4719948 T / C NO.266 11:9641581 A / T NO.493 4:4600460 T / A NO.40 6:13288867 C / T NO.267 4:4529932 G / C NO.494 5:13848946 G / A NO.41 1:31949593 A / T NO.268 4:17218672 T / A NO.495 4:4636613 A / C NO.42 5:18319067 G / A NO.269 4:4522733 C / T NO.496 5:14078483 C / T NO.43 12:6176977 G / A NO.270 17:5684432 G / A NO.497 10:2643111 A / T NO.44 5:12034585 G / A NO.271 4:4558537 A / T NO.498 4:5169555 G / A NO.45 15:8824469 G / A NO.272 18:6700197 G / A NO.499 8:14548582 T / A NO.46 1:25852816 C / T NO.273 4:4630412 A / G NO.500 9:9267155 T / A NO.47 5:12281019 C / T NO.274 17:5684427 G / T NO.501 15:8373522 C / T NO.48 7:13822565 G / A NO.275 4:4642949 A / T NO.502 14:17263747 C / T NO.49 11:18538100 G / T NO.276 7:6609572 A / C NO.503 8:18195800 C / G NO.50 1:26214588 A / T NO.277 5:12743579 C / T NO.504 1:29932567 C / T NO.51 8:11792376 T / A NO.278 4:4605067 C / T NO.505 20:143182 A / T NO.52 5:15887212 C / T NO.279 1:31949595 A / T NO.506 1:25851941 C / G NO.53 4:4722238 C / T NO.280 4:4568874 A / G NO.507 5:14773133 C / A NO.54 5:14773116 G / T NO.281 12:19128618 A / T NO.508 3:13946447 A / T NO.55 8:11841897 T / A NO.282 11:8979048 C / T NO.509 18:5290987 A / T NO.56 6:13657349 C / T NO.283 1:11583995 G / T NO.510 6:19858570 C / T NO.57 4:4717480 A / C NO.284 11:18574268 G / T NO.511 18:6416940 C / A NO.58 11:9912594 G / T NO.285 4:4667734 G / A NO.512 11:10252748 G / A NO.59 11:10131661 A / T NO.286 5:12208644 T / G NO.513 5:12165555 T / C NO.60 5:14773066 T / G NO.287 5:15316092 T / A NO.514 7:13610179 G / T NO.61 4:5443884 A / T NO.288 5:2908373 C / A NO.515 14:4838910 G / A NO.62 12:16156511 C / A NO.289 4:4592839 C / T NO.516 11:18241770 A / G NO.63 3:23666317 C / A NO.290 4:5202484 T / C NO.517 5:14056383 A / G NO.64 1:25617062 C / T NO.291 14:6027421 G / T NO.518 1:23536597 T / A NO.65 4:17218679 C / A NO.292 8:18195828 A / G NO.519 8:11402072 T / C NO.66 7:6312041 T / C NO.293 4:4722242 T / C NO.520 1:26664339 C / A NO.67 15:5871754 T / C NO.294 4:4550623 G / A NO.521 4:4840534 T / A NO.68 7:13883660 G / A NO.295 15:236782 C / T NO.522 1:26093112 G / A NO.69 4:4783621 C / A NO.296 7:13097583 C / T NO.523 8:14511193 A / T NO.70 1:28922312 G / C NO.297 13:2222587 C / A NO.524 10:5695757 C / T NO.71 9:9801369 A / G NO.298 5:14582877 C / T NO.525 1:25622710 C / T NO.72 4:5057288 C / T NO.299 5:14559366 G / C NO.526 4:4708893 G / A NO.73 4:5464241 G / A NO.300 5:13930603 G / A NO.527 8:8423052 G / A NO.74 3:8694329 A / T NO.301 4:4437334 G / A NO.528 4:4627153 T / C NO.75 8:11717334 A / C NO.302 6:11466125 T / G NO.529 5:3456947 G / T NO.76 9:9267016 G / A NO.303 11:9278191 G / A NO.530 1:26160870 C / A NO.77 13:16586559 C / T NO.304 15:8053229 G / T NO.531 4:4758186 A / G NO.78 5:12034832 T / C NO.305 15:236760 A / T NO.532 8:14391210 G / T NO.79 18:13433510 T / G NO.306 18:10945604 T / A NO.533 4:161793 G / T NO.80 9:9266971 C / T NO.307 10:18105355 G / A NO.534 5:12743560 T / C NO.81 4:4602254 A / T NO.308 9:16174756 C / T NO.535 1:23536569 T / A NO.82 11:9303515 C / A NO.309 11:10131649 C / T NO.536 11:18574318 C / T NO.83 1:29248932 A / T NO.310 4:4604334 A / T NO.537 12:6177018 G / A NO.84 5:15342209 A / G NO.311 8:14391349 A / T NO.538 11:18574298 T / C NO.85 4:1847947 G / A NO.312 5:12743564 G / A NO.539 15:8824402 T / C NO.86 11:9303541 A / G NO.313 4:5165627 G / C NO.540 14:4839606 A / G NO.87 4:4683303 C / T NO.314 12:17028721 A / G NO.541 1:25442637 C / A NO.88 14:17263737 G / T NO.315 14:6027425 C / G NO.542 4:4705208 C / G NO.89 5:14772991 G / C NO.316 4:4692219 C / T NO.543 11:10131702 T / A NO.90 6:6269815 A / T NO.317 4:4684246 G / A NO.544 11:18597874 A / C NO.91 5:13879009 A / G NO.318 8:8423068 T / G NO.545 11:18463283 C / T NO.92 4:5167557 G / T NO.319 7:12303222 G / C NO.546 3:14385795 A / G NO.93 1:25897495 A / G NO.320 5:14773081 C / A NO.547 13:17885819 C / T NO.94 18:5821930 C / T NO.321 4:4625908 G / T NO.548 9:9267170 G / A NO.95 1:28831014 C / T NO.322 11:18538078 G / A NO.549 4:4576720 C / T NO.96 6:24429763 C / T NO.323 4:5438670 A / C NO.550 4:4687791 A / G NO.97 14:6027410 A / G NO.324 7:13819690 C / T NO.551 6:11466135 C / T NO.98 4:4665025 G / A NO.325 11:18574289 C / T NO.552 4:4561538 G / A NO.99 4:4399866 A / T NO.326 10:18254770 T / C NO.553 4:4592875 G / A NO.100 1:28922273 A / T NO.327 1:27007495 T / A NO.554 6:16106917 C / A NO.101 5:13923073 C / A NO.328 4:1397091 G / A NO.555 4:4445578 A / T NO.102 4:4840476 C / T NO.329 2:27096124 G / A NO.556 8:7798721 G / A NO.103 7:12303832 G / A NO.330 11:8786860 C / T NO.557 8:8036298 G / T NO.104 14:17263752 A / T NO.331 4:4765859 T / A NO.558 10:4428351 T / C NO.105 1:25006632 C / T NO.332 17:8458390 A / T NO.559 4:4681473 A / G NO.106 1:25442600 C / A NO.333 12:6177021 T / C NO.560 5:19935552 G / A NO.107 4:4572481 C / T NO.334 8:11436412 G / T NO.561 5:14056404 C / A NO.108 9:9509830 C / T NO.335 18:7638558 A / C NO.562 5:14559325 G / T NO.109 4:5740710 G / C NO.336 5:12293944 C / T NO.563 9:9801358 C / T NO.110 4:4738008 C / A NO.337 4:1011734 G / T NO.564 11:16025735 A / T NO.111 4:4713021 T / A NO.338 4:4698188 T / C NO.565 4:4712687 T / C NO.112 6:11466116 A / G NO.339 1:26028376 C / G NO.566 14:2531694 T / A NO.113 10:19521171 T / A NO.340 15:9847753 G / A NO.567 4:4633408 C / T NO.114 1:26160867 G / T NO.341 4:4388607 G / T NO.568 9:9267116 C / T NO.115 11:14574844 C / T NO.342 15:2781707 C / T NO.569 6:9826125 A / T NO.116 11:18574251 A / G NO.343 1:26093216 C / A NO.570 10:915868 C / T NO.117 4:4516023 G / A NO.344 4:4692193 T / A NO.571 3:14385812 G / A NO.118 5:12743623 A / C NO.345 18:5821947 T / G NO.572 4:4568297 C / G NO.119 11:18378505 T / C NO.346 1:11254777 T / C NO.573 18:6729271 T / G NO.120 4:3205015 T / G NO.347 6:11466122 C / A NO.574 1:28922271 A / T NO.121 7:12303736 C / T NO.348 18:7294334 G / T NO.575 3:23666290 C / A NO.122 17:9329480 G / C NO.349 15:6344280 G / A NO.576 4:4709495 T / C NO.123 17:11059894 T / C NO.350 7:13132250 C / T NO.577 14:15990318 C / T NO.124 18:473478 A / T NO.351 8:6344172 C / A NO.578 1:32139359 T / A NO.125 21:7776898 T / G NO.352 7:13261978 C / T NO.579 6:14681993 G / T NO.126 15:236747 A / C NO.353 1:28021846 C / T NO.580 8:15289152 A / T NO.127 3:23657848 C / A NO.354 4:5325019 C / T NO.581 4:4466902 T / G NO.128 4:663272 A / C NO.355 4:4557807 T / G NO.582 11:18574294 A / T NO.129 7:12303755 T / C NO.356 18:6416917 A / T NO.583 5:11854285 C / T NO.130 4:5684337 C / A NO.357 4:4650594 T / A NO.584 1:24686411 A / G NO.131 5:14056391 C / T NO.358 4:4625356 C / A NO.585 3:16265185 G / A NO.132 7:12776516 C / T NO.359 9:9266965 C / T NO.586 5:15670741 C / T NO.133 1:26093037 T / C NO.360 14:17263440 G / A NO.587 7:13261963 A / C NO.134 4:4548213 A / C NO.361 1:26093084 A / C NO.588 6:13657321 C / G NO.135 4:5139496 G / C NO.362 18:6700168 G / A NO.589 4:4764408 C / T NO.136 7:12692589 C / A NO.363 8:8289536 T / A NO.590 7:12318925 C / A NO.137 7:14095641 T / C NO.364 11:9277704 G / A NO.591 9:9267082 A / C NO.138 8:10159538 G / T NO.365 3:25641914 G / A NO.592 11:14788762 A / G NO.139 4:4523105 C / G NO.366 4:1260668 G / A NO.593 8:2575887 C / T NO.140 4:5141913 T / C NO.367 3:23666288 G / A NO.594 4:5641644 C / T NO.141 9:9509798 C / T NO.368 6:13657326 T / A NO.595 5:12375608 T / G NO.142 10:915905 C / T NO.369 1:26318705 G / A NO.596 1:28021842 G / T NO.143 4:4517712 T / C NO.370 11:9303509 A / C NO.597 11:10244021 G / T NO.144 7:13820338 T / C NO.371 10:4428346 T / A NO.598 6:1297108 A / G NO.145 4:5561937 C / T NO.372 9:13704762 G / T NO.599 12:6177029 A / G NO.146 11:18378263 G / A NO.373 4:4707821 A / G NO.600 11:18580077 C / T NO.147 21:10805080 G / T NO.374 6:11614872 A / G NO.601 14:5425682 A / T NO.148 5:11790848 C / T NO.375 8:8543032 C / A NO.602 1:26664335 T / G NO.149 4:4402194 A / C NO.376 1:8162026 C / T NO.603 4:4589605 A / T NO.150 4:4742319 G / A NO.377 1:25936443 T / A NO.604 18:6416937 A / G NO.151 6:11416234 G / A NO.378 6:9972472 C / T NO.605 10:19729923 G / A NO.152 5:14773023 A / G NO.379 19:7033500 G / A NO.606 11:18382256 G / A NO.153 7:7008897 C / T NO.380 8:8446888 C / G NO.607 4:4840245 C / T NO.154 4:4692468 C / G NO.381 4:4642752 C / T NO.608 5:12743609 C / T NO.155 4:4389722 C / A NO.382 8:10774246 C / T NO.609 14:6027457 G / A NO.156 4:5638312 G / A NO.383 10:915911 A / G NO.610 4:4399648 C / A NO.157 5:18319031 T / C NO.384 5:14559378 C / G NO.611 5:16189824 G / A NO.158 4:4582514 G / T NO.385 6:9980695 C / A NO.612 3:25641900 C / T NO.159 5:2271356 C / A NO.386 8:11436375 G / A NO.613 7:14095602 G / A NO.160 1:32958386 A / C NO.387 11:9598310 A / G NO.614 6:13101669 C / A NO.161 11:18325179 A / G NO.388 10:915877 T / G NO.615 11:17904814 G / A NO.162 4:4572147 A / G NO.389 6:13657344 C / T NO.616 4:4781102 C / G NO.163 16:5979399 C / T NO.390 5:14559312 C / T NO.617 7:12303232 G / A NO.164 6:13657323 T / C NO.391 7:13255458 C / A NO.618 15:9310729 C / A NO.165 11:13783175 G / A NO.392 5:12743588 C / A NO.619 5:15380003 C / T NO.166 12:17456617 A / C NO.393 4:5313538 T / C NO.620 12:15967964 C / T NO.167 11:18225970 G / A NO.394 4:4704091 G / A NO.621 11:9277577 T / C NO.168 10:4018448 C / T NO.395 9:9801362 C / T NO.622 8:14390890 G / C NO.169 4:4527590 A / C NO.396 14:19178097 G / T NO.623 5:7245380 G / C NO.170 6:9980686 C / G NO.397 6:1205166 T / C NO.624 5:15341159 C / A NO.171 14:17290388 G / A NO.398 6:10206171 T / A NO.625 8:10901450 A / G NO.172 4:2490209 G / T NO.399 4:5740714 C / T NO.626 6:1205177 C / T NO.173 7:12303829 G / T NO.400 14:16115460 G / A NO.627 11:9303524 T / C NO.174 4:4667486 G / C NO.401 4:4751949 A / G NO.628 9:9266967 A / G NO.175 7:13794652 A / C NO.402 6:11614883 C / T NO.629 9:9267099 A / T NO.176 7:19619230 T / C NO.403 5:15670743 C / A NO.630 4:5438698 G / A NO.177 4:4386656 C / T NO.404 5:24029756 C / T NO.631 4:1946925 C / T NO.178 1:23536577 C / T NO.405 4:4437544 T / C NO.632 15:8824471 G / A NO.179 10:5732011 T / A NO.406 6:13657258 T / G NO.633 4:4681476 T / G NO.180 4:4563226 T / A NO.407 4:21186 A / C NO.634 12:18336599 C / A NO.181 7:9875961 G / T NO.408 4:4715300 A / G NO.635 21:7776877 G / T NO.182 5:3456929 C / T NO.409 19:8054128 T / C NO.636 4:5134952 C / T NO.183 1:28922277 A / T NO.410 4:4667353 A / C NO.637 6:24429754 C / T NO.184 10:17227951 C / G NO.411 15:17498167 C / T NO.638 14:753642 G / T NO.185 1:23536582 A / C NO.412 5:12602972 G / T NO.639 6:24429782 A / G NO.186 5:7245493 G / A NO.413 7:14095621 A / C NO.640 7:13794781 A / C NO.187 4:13999854 T / G NO.414 4:2376096 G / T NO.641 14:17290368 C / A NO.188 6:19711315 G / A NO.415 1:25525988 A / G NO.642 5:6050668 G / C NO.189 18:6457043 C / T NO.416 1:23536589 G / A NO.643 10:915898 T / A NO.190 3:18436120 A / C NO.417 5:14559322 A / G NO.644 14:16828656 A / T NO.191 8:10774278 A / G NO.418 4:4701299 C / T NO.645 4:4394519 A / C NO.192 6:9980689 G / T NO.419 6:24429746 G / T NO.646 4:4687163 C / T NO.193 4:4636825 C / T NO.420 4:663292 C / T NO.647 5:12743614 T / A NO.194 14:3153895 A / C NO.421 1:25772646 A / C NO.648 8:14391361 C / G NO.195 4:906720 T / A NO.422 3:23666279 G / T NO.649 6:10287034 A / T NO.196 5:18319057 C / G NO.423 5:7245516 C / T NO.650 8:8414459 C / G NO.197 9:9267150 T / C NO.424 6:13657285 A / T NO.651 8:8289590 A / T NO.198 4:4437387 G / A NO.425 8:14391345 C / A NO.652 13:16463477 T / G NO.199 4:4660473 C / G NO.426 5:3140946 C / A NO.653 4:5134896 C / G NO.200 4:4722265 A / G NO.427 5:11956541 C / A NO.654 8:18195786 T / A NO.201 4:680006 A / C NO.428 5:13923102 C / T NO.655 18:5821917 G / T NO.202 4:4592866 G / A NO.429 7:12305756 T / C NO.656 4:4683604 A / G NO.203 3:22893858 C / T NO.430 19:2124195 G / T NO.657 6:11466114 G / T NO.204 9:9267146 G / A NO.431 1:25781325 G / A NO.658 19:7033510 C / A NO.205 4:4604566 A / G NO.432 6:1748578 G / A NO.659 4:4385912 T / G NO.206 1:33273902 G / A NO.433 18:6208558 G / T NO.660 5:3037114 C / G NO.207 5:22371115 C / T NO.434 11:10493006 A / C NO.661 6:13657328 G / A NO.208 5:3038079 G / A NO.435 5:12602967 C / T NO.662 6:22730377 C / A NO.209 5:13037738 A / T NO.436 8:8289529 T / A NO.663 8:11792355 T / A NO.210 5:11853106 A / G NO.437 4:4553355 C / A NO.664 5:19935571 T / G NO.211 4:4550669 C / T NO.438 8:18195824 G / T NO.665 5:2466611 G / A NO.212 6:1205216 T / G NO.439 1:26007629 G / A NO.666 4:4708325 A / C NO.213 4:5058395 C / T NO.440 8:8542968 T / C NO.667 4:4530873 G / A NO.214 7:13261952 C / T NO.441 5:14559319 C / G NO.668 4:5056168 G / A NO.215 15:8824433 C / T NO.442 4:4752189 T / A NO.669 4:4517901 C / T NO.216 17:8508045 C / A NO.443 1:28922275 G / T NO.670 4:4712673 A / T NO.217 8:11410163 T / C NO.444 4:5740701 G / A NO.671 7:20272437 G / C NO.218 5:15380075 T / C NO.445 14:753610 G / T NO.672 11:8967264 T / A NO.219 15:6619246 C / T NO.446 5:12375612 G / T NO.673 6:10387011 C / T NO.220 13:16463437 G / T NO.447 11:9316702 C / T NO.674 4:4413437 C / A NO.221 7:6340695 A / T NO.448 10:19730009 C / T NO.675 1:27007487 A / G NO.222 4:4631193 A / C NO.449 9:9267126 T / C NO.676 8:8646295 T / C NO.223 4:4717817 C / G NO.450 5:19935592 G / A NO.677 4:4579806 G / C NO.224 4:4707640 C / T NO.451 7:12303403 A / C NO.678 4:4697530 C / T NO.225 11:10252753 T / G NO.452 4:5166825 A / T NO.679 15:17498179 G / T NO.226 6:13657287 A / G NO.453 13:2254325 G / A NO.680 6:10865505 C / T NO.227 8:10159535 C / T NO.454 8:10901429 A / G

[0053] Example 2

[0054] Application of this invention in heritability assessment

[0055] This embodiment utilizes phenotypic and genotypic data from a reference population, along with the SNP loci described in this invention, to evaluate the genetic parameters of the bloated hippocampal anti-neural necrosis virus, employing the GBLUP model:

[0056]

[0057] Representing phenotypic values, in this embodiment, for binary disease resistance traits (such as survival or non-survival). , and The fixed effects represent the experimental batch, fish weight, and fish age, respectively. This refers to the genotype kinship matrix proposed by VanRaden in 2008. Represents individual random effects.

[0058] The heritability assessment equation is as follows:

[0059]

[0060] in Represents heredity, Represents the variance of additive effects. The residual variance is represented by (in this example, the logit link function is used, so the residual variance is fixed at π^2 / 3). The Reml method (implemented using Asreml-R4.2) was used for fitting in this example. The specific genetic parameters estimated using the 680 core SNP loci and all SNP loci described in this invention are shown in Table 2.

[0061] Table 2. Genetic parameters estimated using the MLE-rank model with 680 preselected SNPs.

[0062] SNP combination Phenotype Additive variance residual Heredity All 4M SNP Survival status 1.382（0.109） π^2 / 3 0.295(0.054） This invention SNP Survival status 3.38（0.807） π^2 / 3 0.507(0.059）

[0063] The results show that the SNPs described in this invention can achieve higher additive genetic variance and heritability. This is because the SNP loci described in this invention have eliminated a large number of neutral or noisy loci compared to all SNP loci. The elimination of neutral or noisy loci reduces "false signals" in the model, making the estimation of the genetic effect of the core SNPs more accurate, thereby improving the reliability of heritability. In genetic prediction and breeding selection, using the core SNPs provided by this invention can reduce breeding costs, improve breeding efficiency, and ensure the accuracy of the results. (Table 2)

[0064] Example 3

[0065] Application of this invention in GEBV estimation

[0066] 1. Genotype Dataset Construction

[0067] Two independent genotype datasets were constructed for comparative evaluation:

[0068] Experimental group (Target Panel): Contains only the 680 highly significant / functional SNP markers described in this invention;

[0069] Control Panel: Based on physical location information, 50,000 high-density SNP markers are uniformly selected across the entire genome, representing the marker density of a conventional genome selection chip.

[0070] 2. Cross-Validation Scheme

[0071] To eliminate random errors introduced by sample partitioning and ensure robustness of the results, 10 replicates of 10-fold cross-validation were employed.

[0072] Sample partitioning: The reference group is randomly divided into 10 subsets (Folds). Nine subsets are selected in turn as the training set to build the model, and the remaining subset is used as the validation set for prediction.

[0073] Repeated validation: The above 10-fold partitioning process was independently repeated 10 times (i.e., a total of 100 model training and predictions were performed), and the final result was averaged to evaluate the stability of the model.

[0074] 3. Statistical Model Construction

[0075] For the two datasets mentioned above, the following two mainstream genomic selection models were applied for parameter estimation:

[0076] The GBLUP (Genomic Best Linear Unbiased Prediction) model constructs a genomic relationship matrix (G matrix) using SNP markers and directly estimates the individual's genomic breeding value (GEBV) through a mixture of linear models. This model assumes that all markers contribute equally to phenotypic variation and is suitable for traits controlled by small-effect polygenic genes.

[0077] The BayesA model employs a hierarchical Bayesian framework, assuming that the label effects follow a scaled t-distribution. Iterative sampling is performed using a Markov chain Monte Carlo (MCMC) method (e.g., setting the number of iterations to 30,000, with the first 10,000 as burn-in) to more accurately capture label contributions with different effect sizes.

[0078] 4. Prediction accuracy evaluation indicators

[0079] Prediction accuracy is defined as the Pearson correlation coefficient between phenotypic observations of individuals in the validation set and the genomic breeding value (GEBV) calculated by the model. .

[0080] The results showed that the BayesA phenotypic model slightly outperformed the GBLUP model. Specifically, based on the BayesA model, the predicted relevance of the 680 SNPs described in this invention was 0.626, while the predicted relevance of the 50,000 SNPs uniformly selected across the entire genome was 0.403. Using the 680 SNPs described in this invention improved prediction accuracy by approximately 50%. Figure 2 )

[0081] Example 4

[0082] Testing in actual breeding populations of this invention

[0083] To further evaluate the versatility and detection performance of the liquid-phase chip containing 680 SNP loci constructed in this invention in non-reference populations, 48 ​​bloated hippocampal samples were randomly collected for genotyping testing in this embodiment. The statistical results are shown in Table 3. The results indicate that the chip exhibits excellent stability in non-reference samples, with a minimum detection rate of 95.00% and an average detection rate as high as 98.51%, meeting the quality requirements for high-throughput genotyping.

[0084] Table 3. SNP detection rate in 48 bloated seahorse samples

[0085] sample Number of missing sites Total number of sites Detection rate sample Number of missing sites Total number of sites Detection rate A2F 2 680 99.71% A36-48 21 680 96.91% A2M 2 680 99.71% A2022-7F 21 680 96.91% A5F 16 680 97.65% A2022-7M 1 680 99.85% A5M 3 680 99.56% A2022-17F 11 680 98.38% A8-2 26 680 96.18% A2022-17M 34 680 95.00% A11F 24 680 96.47% A2022-18F 8 680 98.82% A11M 11 680 98.38% A2022-18M 5 680 99.26% A11-11 5 680 99.26% A2022-21F 1 680 99.85% A13-11 14 680 97.94% A2022-21M 4 680 99.41% A13-12 18 680 97.35% A2022-24F 26 680 96.18% A15F 34 680 95.00% A2022-24M 2 680 99.71% A15M 6 680 99.12% A2022-27F 8 680 98.82% A17-17 12 680 98.24% A2022-27M 2 680 99.71% A20F 2 680 99.71% A2022-28F 6 680 99.12% A20M 6 680 99.12% A2022-28M 7 680 98.97% A23-68F 4 680 99.41% A2022-30F 21 680 96.91% A23-68M 3 680 99.56% A2022-30M 2 680 99.71% A27-6 13 680 98.09% A2022-33F 3 680 99.56% A30F 7 680 98.97% A2022-33M 2 680 99.71% A30M 3 680 99.56% A2022-35F 7 680 98.97% A30-13 22 680 96.76% A2022-36F 3 680 99.56% A31F 14 680 97.94% A2022-36M 9 680 98.68% A31M 5 680 99.26% A2022-40F 9 680 98.68% A33-7 14 680 97.94% A2022-40M 6 680 99.12%

[0086] Example 5

[0087] Actual breeding populations of GEBVs are expected to be tested

[0088] Table 4 shows the validation results of the 680 SNP markers described in this invention in practical breeding applications. Eleven representative full-sib families were selected for the study. The genomic breeding value (GEBV) of the paternal and maternal parents was calculated using this SNP panel, and the family GEBV (mean GEBV of both parents) was then extrapolated. Simultaneously, the survival rate data of each family under actual breeding conditions were recorded to assess the accuracy of the predicted values. To quantitatively evaluate the consistency between the GEBV estimated based on this SNP panel and the actual phenotypic trait (survival rate), the Pearson correlation coefficient was calculated. The correlation coefficient between family survival rate and family GEBV was 0.8029, reaching a highly significant level. ,specific The value was 0.002919. The results showed a highly significant positive correlation between family GEBV and actual survival rate. That is, the higher the family's genomic breeding value (the larger the value / the closer to a positive value), the higher the survival rate of its offspring in actual breeding. Taking the best-performing family 2022A33 as an example, it had the highest predicted GEBV value (-0.5115) and also observed the highest actual survival rate (81.70%); conversely, the family 2022A17 with the lowest GEBV also had the lowest survival rate. This result strongly demonstrates that the 680 SNP loci described in this invention can accurately reflect the genetic potential of the target trait, have high predictive accuracy, and can be effectively applied to early selection work in molecular design breeding.

[0089] Table 4. Accuracy of GEBV estimation for the 680 SNPs described in this invention tested in actual breeding families.

[0090] FID Father Paternal GEBV Mother Parent GEBV Family survival rate Family GEBVs 2022A17 A2022-17M -1.032 A2022-17F -1.089 18.75% -1.0605 2022A18 A2022-18M -0.683 A2022-18F -1.005 20.45% -0.844 2022A21 A2022-21M -0.925 A2022-21F -0.659 50.00% -0.792 2022A24 A2022-24M -0.879 A2022-24F -0.826 50.00% -0.8525 2022A27 A2022-27M -0.81 A2022-27F -0.902 47.19% -0.856 2022A28 A2022-28M -0.578 A2022-28F -0.898 32.26% -0.738 2022A30 A2022-30M -0.832 A2022-30F -0.683 57.14% -0.7575 2022A33 A2022-33M -0.539 A2022-33F -0.484 81.70% -0.5115 2022A36 A2022-36M -0.724 A2022-36F -0.878 40.47% -0.838 2022A40 A2022-40M -0.742 A2022-40F -0.882 30.48% -0.812 2022A7 A2022-7M -0.597 A2022-7F -0.785 60.82% -0.691

[0091] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the scope of protection of the present invention. For those skilled in the art, other variations or modifications can be made based on the above description and ideas, and it is neither necessary nor possible to exhaustively describe all implementation methods here. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the scope of protection of the claims of the present invention.

Claims

1. The application of a set of SNP sites of the inflated hippocampus anti-neural necrosis virus in detecting the anti-neural necrosis virus phenotype of the inflated hippocampus, characterized in that, The SNP site is located in the reference genome of the bloated hippocampus, GCA_018466805.1, and the SNP site sequence is shown in SEQ ID NO.1~SEQ ID NO.

680.

2. The application of the SNP site described in claim 1 in the preparation of a liquid-phase chip for genomic selection breeding of bloated hippocampus against neuronecrosis virus.

3. A liquid-phase chip for genomic selection breeding of bloated hippocampus against nerve necrosis virus, characterized in that, The chip includes probes that specifically capture / detect the SNP sites of claim 1.

4. The liquid phase chip according to claim 3, characterized in that, The SNP sites were obtained through screening using the following method: (1) Immersion challenge experiment was conducted on the bloated seahorse. After the experiment, live tissue was collected and preserved to obtain the reference population. (2) Perform whole-genome resequencing on the reference population to obtain high-quality SNP loci; (3) Analyze high-quality SNP sites and extract significantly associated sites as SNP sites.

5. The liquid phase chip according to claim 4, characterized in that, The quality control criteria for high-quality SNP sites include: Sites with an average sequencing depth < 10X were removed; sites with a deletion rate ≥ 0.05 were removed; sites with a minimum allele frequency ≤ 0.05 were removed; Hardy-Weinberg equilibrium sites were removed. P Sites ≤0.00001.

6. The application of the liquid phase chip according to claim 3 in the genomic selection breeding of the bloated hippocampus against nerve necrosis virus.

7. A method for breeding bloated hippocampus resistant to nerve necrosis virus, characterized in that, The breeding method includes the following steps: (1) Genotyping of bloated hippocampal individuals using the liquid phase chip described in claim 3 to obtain genotype data of SNP molecular markers; (2) Calculate the genomic breeding value of an individual based on genotype data; (3) Based on the genomic breeding value, individuals with high resistance to nerve necrosis virus were selected as parents for breeding.

8. The breeding method according to claim 7, characterized in that, When the genomic breeding value ranks high in the candidate population, the bloated hippocampus individual is considered to be a bloated hippocampus with high resistance to nerve necrosis virus.

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