Liquid chip for genomic selection breeding of red grouper against nervous necrosis virus and application thereof

Abstract

The application belongs to the technical field of aquatic genetic breeding, and particularly relates to a red nine-spine grouper (Plectropomus leopardus) anti-neurological necrosis virus genome selection breeding liquid phase chip and application thereof. The red nine-spine grouper anti-neurological necrosis virus genome selection breeding liquid phase chip contains 500 SNP sites, and through high-efficiency capture of core sites, lays a technical foundation for establishing a rapid and low-cost anti-neurological necrosis virus genome selection breeding process, and powerfully promotes the industrialization process of red nine-spine grouper (Plectropomus leopardus) breeding.

Description

Technical Field

[0001] This invention belongs to the field of aquatic genetic breeding technology, specifically relating to a liquid phase chip for genomic selection breeding of red spiny perch against nerve necrosis virus and its application. Background Technology

[0002] Red nine-spined perch ( Cephalopholis sonnerati This is an important marine aquaculture fish, prized for its delicious flavor and rich nutritional value, which has led to its large-scale farming. The development of this industry has not only optimized the fishery economic structure and significantly increased fishermen's income, but also plays an irreplaceable role in meeting the demands of the high-end consumer market.

[0003] Nerve necrosis virus (NHV) Viral Nervous Necrosis VNN (Vibrio nephrolepis) is one of the main pathogens in red snapper aquaculture, posing a significant threat to the industry. This virus can spread rapidly through vertical transmission (carried by parent fish) and horizontal transmission (water, feed), causing typical symptoms in infected fish such as abnormal behavior and nerve tissue necrosis. Mortality rates in juvenile fish can exceed 80%, resulting in devastating damage to aquaculture production. Current control measures mainly rely on traditional methods such as water disinfection and antibiotic use, but these methods are limited by high costs, complex operations, and difficulty in large-scale implementation. Therefore, breeding disease-resistant strains has become a core requirement for the green and sustainable development of the red snapper industry.

[0004] Traditional population selection, family selection, and hybridization breeding techniques have had limited effectiveness in improving the VNN resistance trait in red snapper. This is due, in part, to the biological characteristics of red snapper, which make it difficult to construct stable families, and in part, because disease resistance is a complex trait regulated by multiple genes, making it difficult for traditional methods to accurately elucidate its genetic mechanisms. In recent years, Genomic Selection (GS), based on whole-genome sequencing technology, has provided a new technical pathway for improving economic traits in fish. This technology, through genome-wide single nucleotide polymorphism (SNP) marker association analysis with target traits, can rapidly assess the genomic estimated breeding value (GEBV) of candidate individuals, thus significantly shortening the breeding cycle and improving selection efficiency. In the breeding of aquatic animals such as Atlantic salmon, turbot, and large yellow croaker, genomic selection technology has demonstrated unparalleled advantages over traditional methods, providing an important reference for disease resistance breeding in red snapper.

[0005] The core of genomic selection lies in precise genotyping technology. Whole-genome SNP genotyping, as a key means of analyzing the association between genetic variation and traits, can achieve high-depth resequencing of target loci through targeted capture sequencing (such as cGBS liquid-phase chip technology), ensuring the accuracy of genotype detection. However, for farmed fish with relatively low individual economic value, the cost of genotyping remains a major obstacle limiting the large-scale application of genomic selection technology. It is noteworthy that there are currently no reports, either domestically or internationally, of dedicated gene chips for the VNN resistance trait in red snapper. Therefore, developing low-cost, high-throughput, and highly flexible whole-genome SNP chips and supporting disease-resistant genomic selection technologies has become an urgent priority to overcome the bottleneck in the breeding technology of disease-resistant red snapper. Summary of the Invention

[0006] To address the aforementioned technical problems, this invention provides a liquid-phase chip for genomic selection breeding of red spiny perch against nerve necrosis virus and its applications.

[0007] The technical solution of the present invention is as follows: This invention provides a liquid-phase chip for genomic selection breeding of red spiny perch against nerve necrosis virus. The genotyping targets of the chip include 500 SNP sites on the red spiny perch reference genome (GenBank: GCA_043388425.1). The positions and mutation types of these sites on the red spiny perch reference genome are shown in NO.001-NO.500.

[0008] Table 1. Locations and mutation types of 500 SNP sites on the reference genome of the red snapper.

[0009] The application of the liquid phase chip described in this invention in the genomic selection breeding of red spiny perch against nerve necrosis virus.

[0010] Based on the locations of the 500 SNP sites in Table 1, extend 35 bp before and after the reference genome, and use the reverse complementary sequence of 71 bp, including the target site, as the probe sequence for that site.

[0011] Compared with the prior art, the present invention has the following beneficial effects: Addressing the technical bottlenecks of low efficiency and high cost in traditional methods for breeding red snapper resistant to neuronecrosis virus (NSV), this invention innovatively develops a liquid-phase chip, "Red Core 1," for genomic selection breeding technology. By integrating genome-wide association study (GWAS) and MLE-rank model, 500 core SNP loci with high genetic effects are precisely screened from a 50k liquid-phase chip. This optimized strategy effectively reduces practical application and computational costs while ensuring the accuracy of genomic selection, significantly improving the chip's ease of use and industrial adaptability. This invention, through the efficient capture of core loci, lays the technical foundation for establishing a rapid and low-cost genomic selection breeding process for NSV resistance, powerfully promoting the industrialization of red snapper breeding and providing an important technological innovation paradigm for disease-resistant breeding of marine fish. Attached Figure Description

[0012] Figure 1 Comparison of the ROC curves of GEBV prediction performance based on core SNP sites and uniformly distributed 40,345 SNPs in a 200-tailed validation population. Figure 2 Compare the population mean of GEBV estimated from the core SNP sites in the 200-tailed validation population with that of the uniformly distributed 40,345 SNPs. Figure 3 GEBV population mean analysis of core SNP sites in the 80 candidate population. Detailed Implementation

[0013] To enable those skilled in the art to better understand the technical content of this invention, the invention will be further described below with reference to specific embodiments. Where specific techniques or conditions are not specified in the embodiments, they shall be performed in accordance with the techniques or conditions described in the literature in the art or according to the product instructions. Unless otherwise specified, the equipment and reagents used in each embodiment are all commercially available.

[0014] Example 1 Acquisition of core SNP sites and chip fabrication 1. Construction of a reference population and establishment of a phenotypic dataset for red spiny perch against nerve necrosis virus This embodiment constructed a reference population for red snapper resistant to neuronecrosis virus (NSV). This population consisted of 1378 NSV-infected fish collected from Hainan and Qingdao, Shandong, China, between 2023 and 2024, with an additional 200 fish added as a validation population, totaling 1578 individuals. Fin samples were collected from all subjects for genomic analysis. Individual mortality / survival status and biological data were systematically recorded, providing important phenotypic evidence for subsequent breeding of disease-resistant superior varieties. The genomic data of the reference population were obtained through whole-genome resequencing using the BGI T7 sequencing platform, laying the foundation for subsequent molecular marker development.

[0015] 2. Development and quality control process for high-quality SNP markers Genomic DNA was extracted from 1578 individuals. After rigorous quality control, a second-generation sequencing paired-end DNA library was constructed, and high-throughput sequencing was performed using the BGI T7 sequencing platform. After obtaining clean reads through quality control, the sequencing data were aligned with the red spiny perch reference genome (GenBank: GCA_043388425.1) using BWA software. After removing repetitive sequences, basic data information statistics and mapping analysis were performed. Based on the alignment results between the sequencing sequences and the reference genome, SNP calling was performed using GATK software to generate the original VCF file. Further multi-level quality filtering was implemented using VCFtools software: first, sites with a sequencing depth lower than 15× were removed; then, sites with a deletion rate exceeding 5%, a minimum allele frequency (MAF) lower than 0.05, and those not conforming to Hardy-Weinberg equilibrium (P-value threshold set to 1×10⁻) were filtered. 5 The study identified 11,898,532 high-quality SNP markers. Genotyping was performed using Beagle v5.1 software, significantly improving data integrity and providing reliable genetic marker resources for subsequent genome-wide association studies (GWAS) and marker-assisted breeding.

[0016] 3. Extract SNP locus information Based on the quality-controlled SNP dataset, Plink v2.0 software was used to analyze data from the red spiny perch reference population (…). Cep.s.RefAll rigorously quality-controlled SNP loci were selected from the genome. Then, using the `-bp-space` command in Plink v1.9 software, evenly spaced sampling was performed based on the physical location of the SNPs, ultimately obtaining 40,345 SNP markers evenly distributed throughout the genome, stored as "geno1.bin" (Geno1); simultaneously, all 11,898,532 high-quality SNP markers in the genome were saved as geno2.bin (Geno2). Based on this, 31 genetic loci significantly associated with anti-neuronal necrosis virus were identified using a genome-wide association analysis (GWAS) model, and the MLE-rank model was used to calculate the rank of each SNP. The values ​​(effect value vector) are sorted from largest to smallest by absolute value. Finally, the 31 high-genetic-effect loci selected by GWAS are integrated with the 469 core loci selected by this invention based on the MLE-rank model, resulting in a high-genetic-effect marker set (NO.001-NO.500) containing 500 loci, denoted as geno3.bin. The GWAS model is implemented using Plinkv2.0, with the first three principal components as covariates. The GWAS model is as follows:

[0017] in, It is a phenotypic vector. It is the genotype matrix of the current mutation site. It is a fixed covariate matrix. This is the error term.

[0018] The MLE-rank model is as follows:

[0019] in, This represents the phenotypic value of the nth sample. Let be the genotype of the j-th SNP on the n-th sample (0 / 1 / 2 represents AA / Aa / aa). This represents the total sample population obtained. This represents the proportion of surviving individuals in the sample. It was obtained from the MLE-rank model. Representing the The effect of each SNP site on the phenotype.

[0020] Example 2 Narrowly sensed heritability assessment of the anti-neurone necrosis virus trait in red spiny perch This embodiment assesses the narrow-sense heritability of the anti-neural necrosis virus trait in red spiny perch based on 500 core SNP markers. Three marker datasets constructed in Example 1 were used: 40,345 uniformly distributed SNPs (Geno1, geno1.bin), 11,898,532 high-quality SNPs from the whole genome (Geno2, geno2.bin), and 500 SNPs from the core loci of this invention (Geno3, geno3.bin). Heritability was estimated using the GBLUP method (implemented via asreml4-R software) based on phenotypic data from a reference population. The specific animal model is defined as follows:

[0021] Φ(·) is the cumulative standard normal distribution function. VanRaden used a genotype relationship matrix in 2008. M defines the genotype matrix, where AA / Aa / aa are 2 / 1 / 0. This is an allele frequency matrix. It is a diagonal matrix. is a standardization constant. This is the effect value. The method for calculating genomic heritability is as follows:

[0022] For additive variance, The residual is 1, which is fixed in this model.

[0023] Table 2 shows that there are significant differences in heritability estimates among different marker sets: the heritability estimate based on Geno1 is 0.241±0.033, the heritability estimate based on Geno2 is 0.243±0.033, while the heritability estimate based on the core marker set Geno3 is the highest, reaching 0.407±0.034. This indicates that the Geno3 marker set at the patent core locus can more efficiently capture the genetic variation of the neuronecrosis virus resistance trait, providing a better marker combination for subsequent molecular marker-assisted breeding.

[0024] Table 2. Evaluation of genetic parameters for resistance to nerve necrosis in red sea bass using three groups of SNP markers.

[0025] The results show that the core SNP marker set developed in this embodiment exhibits a significant advantage in assessing the genetic parameters of red sea bass resistant to neuronecrosis virus (NSV), capturing more additive genetic variance. This is because the core markers screened by integrating GWAS and the MLE-rank model in this invention have high genetic effect associations, while the irrelevant or low-effect genetic markers contained in Geno1.bin and Geno2.bin introduce noise, leading to biases in the animal model's estimation of genetic parameters. Therefore, the core SNP marker set screened in this invention can effectively improve the accuracy of genetic parameter estimation, providing a more reliable theoretical basis for genomic selection breeding of red sea bass resistant to NSV and ensuring the efficient advancement of breeding work.

[0026] Example 3 Validation of the detection rate of the core SNP site of red spiny perch against neuronecrosis virus To evaluate the detection efficacy of the 500 core SNP loci described in this invention, a liquid-phase chip containing the core loci was used to perform genotyping verification on 19 individuals from the reference population that had completed whole-genome resequencing. The experimental results are shown in Table 3. It can be seen that the core loci designed in this invention exhibit extremely high detection performance: the average genotype detection rate of the 19 samples reached 99.2%, and the consistency between the genotypes of all loci and the whole-genome resequencing data reached 99.5%. This result fully verifies the high accuracy and stability of the core SNP loci in genotyping. The results indicate that the high detection rate and accurate genotype determination capability of the core loci selected in this invention provide solid technical support for genomic selection breeding of red spiny perch resistant to neuronecrosis virus, and provide a reliable genomic tool for the breeding of disease-resistant superior varieties.

[0027] Table 3. Detection rate of core SNP sites against nerve necrosis virus in different red sea bass populations

[0028] Example 4 Evaluation of the accuracy of core loci in predicting population genome-estimated breeding value (GEBV). To evaluate the predictive accuracy of the core SNP loci of this invention for an unknown population, 200 red spiny perch were selected as a validation population. The 100 individuals that died after infection with neuronecrosis virus were defined as the susceptible population, and the surviving individuals were defined as the resistant population. Genotyping was performed on these 200 validation population samples using the core SNP locus set Geno3 and the locus set Geno1 of this invention, respectively.

[0029] Based on the genotyping results, the GEBV of these 200 non-reference population red spiny perch was estimated and normalized to 0-1. Subsequently, the normalized GEBV was correlated with the infection experimental phenotype using receiver operating characteristic (ROC) curve analysis.

[0030] The analysis results showed that when using the core SNP locus set Geno3, the mean GEBV in the resistant population was 0.542, and in the susceptible population it was 0.385. Figure 2 The area under the curve (AUC) for GEBV and the disease-resistant phenotype reached 0.799. Figure 1 When using the Geno1 locus set, the GEBV of the resistant population was 0.659, while that of the susceptible population was 0.589. Figure 2 The AUC value for GEBV and the disease-resistant phenotype was 0.63 ( Figure 1 ).

[0031] In summary, the 500 core loci provided by this invention can more accurately predict the disease resistance of unknown populations in genomic selection breeding for red sea bass with resistance to nerve necrosis virus, and can meet the needs of genomic selection breeding for red sea bass with resistance to nerve necrosis virus.

[0032] Example 5 Further validation of the predictive accuracy of core sites for anti-neural necrosis virus (GEBV) in red spiny perch. To further verify the effectiveness of the core SNP locus (Geno3) of this invention in genomic selection against neuronecrosis virus (GNV), 80 red spiny perch individuals infected with GNV were selected as an independent validation population. Among them, 37 individuals who died after infection were defined as the susceptible population, and 43 surviving individuals were defined as the resistant population. Geno3 was used to genotype all individuals, and the GEBV of the candidate population was estimated based on the genotyping data. Subsequently, the GEBV was normalized to 0-1.

[0033] Experimental results showed that the average GEBV value of the resistant group was 0.562, significantly higher than that of the susceptible group (0.469). Figure 3 Further analysis of the top 10 individuals with GEBV positivity revealed that 8 were surviving and 2 were deceased; the chi-square test (using the R language function chisq.test) showed that this result was statistically significant (P=0.025). Expanding the analysis to the top 15 individuals with GEBV positivity, 12 were surviving and only 3 were deceased; the chi-square test P-value was 0.0034, indicating that the core locus described in this invention can effectively distinguish between resistant and susceptible individuals.

[0034] In summary, the core SNP loci described in this invention can effectively distinguish the disease resistance of red spiny perch individuals, and can significantly enrich disease-resistant individuals through GEBV ranking, providing a reliable technical tool for genomic selection breeding of red spiny perch resistant to nerve necrosis virus disease.

[0035] Terminology Explanation: SNP: Single Nucleotide Polymorphism, an abbreviation for DNA sequence polymorphism caused by a single nucleotide variation at the genomic level.

[0036] Reference population: In genome selection, a population with phenotype and genotype is used to train the model. The phenotype is obtained through manual measurement, and the genotype is obtained through genome sequencing.

[0037] Candidate population: In genomic selection, a population with only genotypes but no phenotypes is usually a candidate individual in the breeding process. After the genotypes are determined, the breeding value of these individuals can be estimated by combining them with a reference population, and the best individuals can be selected and bred based on the level of their breeding values.

[0038] GBLUP: Genomic Best Linear Unbiased Prediction, is a method for estimating individual breeding values ​​by constructing a genomic kinship matrix G using high-density molecular markers covering the genome.

[0039] GEBV: Genomic Estimated Breeding Value, an abbreviation for breeding value estimated at the genomic level using genomic selection methods.

[0040] GWAS: Genome-wide association study. GWAS is an hypothesis-free method for identifying associations between genetic regions (locus) and traits (including diseases).

[0041] MLE-rank: A ranking method based on maximum likelihood estimation, which may be used to rank the importance of SNP markers or individuals, assisting in the screening of core loci or optimizing breeding value estimation.

[0042] BayesA: A Bayesian method that assumes the marker effect follows a normal distribution and is applicable to complex traits controlled by multiple genes. In genomic selection, it may be used to estimate the marker effect.

[0043] ROC: Receiver Operating Characteristic Curve, is a visualization tool used to evaluate the predictive power of classification models. Its horizontal axis represents the False Positive Rate (FPR), which indicates the proportion of non-target categories that are incorrectly predicted as target categories; the vertical axis represents the True Positive Rate (TPR), which indicates the proportion of correct identification of the target category.

[0044] AUC: Area Under the Curve. AUC (Area Under the Curve) is the area under the ROC curve, and its value ranges from 0 to 1. The closer the AUC value is to 1, the higher the accuracy of the model's prediction; if the AUC is 0.5, it means that the model's prediction effect is no different from random guessing.

Claims

1. A liquid-phase chip for genomic selection breeding of red spiny perch against nerve necrosis virus, characterized in that, The genotyping target of the chip includes 500 SNP sites on the red spiny perch reference genome GenBank: GCA_043388425.

1. The location and mutation type of these sites on the red spiny perch reference genome are shown as NO.001-NO.

500. 。 2. The application of the liquid phase chip as described in claim 1 in the genomic selection breeding of red spiny perch against nerve necrosis virus.

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