Edwardsiella tarda-resistant genome selective breeding liquid chip for oplegnathus punctatus and application thereof

Abstract

The invention belongs to the technical field of aquatic genome selective breeding, and particularly relates to an edwardsiella tarda-resistant genome selective breeding liquid-phase chip for oplegnathus punctatus and application of the liquid-phase chip. According to the method, 500 core SNP loci highly associated with the Edwardsiella tarda resistance character are screened out from 2415773 high-quality SNPs by obtaining the whole genome SNP marker of the oplegnathus punctatus and combining the three methods of MME-rank, GWAS and PVE, and the low-density liquid phase chip prepared by taking the 500 core SNP loci as the core shows extremely high prediction accuracy in a reference group and an independent verification group and can be used for detecting the Edwardsiella tarda resistance character of the Edwardsiella tarda resistance character of the Edwardsiella tarda resistance character of the Edwardsiella tarda resistance character of the Edwardsiella tarda resistance character. The method is obviously superior to a whole genome set and a uniformly distributed marker set; family verification shows that the family GEBV calculated based on the chip is significantly related to the actual survival rate of the offspring. According to the method, high-precision disease resistance performance prediction is realized at extremely low cost, and a key technical tool and a solution are provided for rapid breeding of disease-resistant improved varieties of oplegnathus punctatus.

Description

Technical Field

[0001] This invention belongs to the field of aquatic genomic selection breeding technology, specifically relating to liquid phase chip for genomic selection breeding of spotted sea bream resistant to Edwardsiella tarda and its application. Background Technology

[0002] Spotted sea bream ( Oplegnathus punctatus The spotted rock seabream is an important marine economic fish, prized for its delicate flesh, unique flavor, and high nutritional and medicinal value. Its aquaculture industry is rapidly developing due to its low farming costs, high market demand, and significant economic value. However, the presence of Edwardsiella tarda (…) has led to its decline. Edwardsiella tarda Bacterial diseases caused by pathogens such as [unspecified pathogens] are becoming increasingly frequent, especially during the high temperatures of summer. These diseases can cause skin ulcers, organ lesions, and other symptoms in farmed fish, resulting in extremely high mortality rates and severely hindering the healthy and sustainable development of the spotted sea bream industry. Traditional drug treatments pose risks of water pollution and drug resistance. Therefore, breeding disease-resistant strains is the fundamental way to achieve the green and sustainable development of the spotted sea bream industry.

[0003] In fish genetic breeding, traditional population selection, family selection, and hybridization breeding techniques are phenotype-dependent, have long breeding cycles, and struggle to guarantee selection accuracy for complex traits such as disease resistance, which are controlled by multiple genes and have low heritability, resulting in limited trait improvement. In recent years, Genomic Selection (GS) has utilized genome-wide genetic markers to construct a reference population and associate its genetic marker information with the target trait, estimating the breeding value (GEBV) of candidate populations without relying on their phenotypes. Compared to traditional methods, GS utilizes all SNP information to estimate the GEBV of candidate populations, significantly shortening generation intervals and greatly improving the accuracy and efficiency of selecting complex traits. Currently, this technology has demonstrated great potential in disease resistance breeding of various economically important fish species, such as large yellow croaker, turbot, and Atlantic salmon, providing a new technical pathway for improving disease resistance traits in fish.

[0004] A key factor limiting the predictive applications of genomic selection breeding is the cost of genotyping. Liquid-phase capture-based targeted SNP genotyping chips (cGPS) can significantly reduce genotyping costs. This technology designs probes to perform high-depth sequencing on pre-selected SNP loci highly associated with the target trait, thereby reducing genotyping costs while maintaining detection accuracy. This is a crucial pathway to promoting the industrial application of genomic selection. In practice, low-density chips developed using this technology have been successful in species such as golden pomfret and mandarin fish. The success of low-density chips depends on the quality of the genetic information of the screened SNP loci. High-quality SNP combinations can significantly reduce genotyping costs while maintaining accuracy. However, chips containing a large number of invalid SNP loci show a significant decrease in predictive accuracy. Therefore, developing a low-cost genotyping chip targeting the Edwardsiella tarda resistance trait in spotted rockfish, containing high-value core SNP markers, is crucial for the sustainable development of the industry. Summary of the Invention

[0005] The purpose of this invention is to overcome the shortcomings of the prior art and provide a liquid phase chip for genomic selection breeding of spotted sea bream resistant to Edwardsiella tarda and its application.

[0006] The technical solution of the present invention is as follows: A liquid-phase chip for genomic selection breeding of spotted sea bream resistant to Edwardsiella tarda contains a probe array capable of specifically capturing or detecting 500 SNP sites in the reference genome of spotted sea bream (CNGBdb:CNA0019300). The 500 SNP sites are shown in Table 1. The sequences of the 500 SNP sites are SEQ ID NO.1-SEQ ID NO.250, with each sequence corresponding to two SNP sites, represented by the 36th and 107th bases of each sequence, respectively. The 36th and 107th bases are indicated by the degenerate base y / r.

[0007] Table 1: Information on 500 SNP loci

[0008] The liquid phase chip is used in any of the following applications: (a) Assessing the resistance of individual spotted sea bream to Edwardsiella tarda; (b) Estimate the genomic breeding value of individual spotted sea bream; (c) Screening for spotted sea bream parents or families resistant to Edwardsiella tarda; (d) Prepare disease-resistant breeding products of spotted sea bream.

[0009] A kit comprising the liquid phase chip and reagents for genotyping, the kit being used for genomic selection breeding of spotted sea bream resistant to Edwardsiella tarda.

[0010] To achieve efficient and low-cost breeding of disease-resistant varieties, this invention also provides a method for constructing a low-density liquid-phase chip for genomic selection breeding of disease-resistant species in spotted sea bream, comprising the following steps: (1) This invention obtains SNP markers from the whole genome of spotted sea bream and constructs Geno1 containing 2,415,773 SNPs; it uses the whole genome uniform distribution sampling method to construct Geno2 containing 22,757 SNPs; and it uses maximum likelihood estimation ranking (MLE-rank), genome-wide association analysis (GWAS) and phenotypic variance explained ratio (PVE) analysis to screen the whole set of Geno1 and obtain a candidate core set containing 500 SNP sites that is highly associated with disease resistance traits, namely Geno3.

[0011] (2) 500 SNP loci were screened using a combination of MLE-rank, GWAS and PVE analysis. The GBLUP method was used on the reference population for genomic selection using 10×10 cross-validation. The prediction accuracy was calculated by the area under the ROC curve (AUC) and compared with the prediction accuracy of Geno1 for all SNPs and Geno2 for uniform distribution.

[0012] (3) To further verify that the 500 core SNP loci provided by this invention can be efficiently applied to genomic selection breeding, the prediction accuracy was compared with that of Geno1 with all SNPs and Geno2 with uniform distribution in an independent validation population. Furthermore, a GBLUP model was constructed using the phenotypic and genotypic data of the offspring to obtain the parental GEBV of Geno1, Geno2, and Geno3. The Pearson correlation between the family's GEBV and the mortality and survival rate of the offspring was calculated using the parents to further verify that the 500 core SNP loci provided by this invention are effective and can be efficiently applied to genomic selection breeding.

[0013] (4) Based on the 500 SNP sites of Geno3 identified by MLE-rank, GWAS analysis and PVE analysis, a corresponding capture probe was designed and synthesized, and a low-density liquid phase chip was prepared for the detection of disease resistance traits of spotted sea bream.

[0014] Compared with the prior art, the present invention has the following beneficial effects: To address the drawbacks of traditional breeding methods, such as long breeding cycles, high costs, and difficulty in improving disease resistance traits, this invention provides a liquid-phase chip, "Dream No. 1," for genomic selection breeding of spotted rock seabream resistant to Edwardsiella tarda, containing 500 core SNP loci. In a reference population, Geno3, with its 500 SNP loci selected using a combination of MLE-rank, GWAS, and PVE analyses, demonstrated higher prediction accuracy than Geno1 (containing all SNPs) and the uniformly distributed Geno2, indicating that Geno3 is effective in improving disease resistance in spotted rock seabream. In an independent validation population, the prediction accuracy of the 500 core SNP loci provided by Geno3 was 0.87, significantly better than Geno1 (containing all SNPs, 0.7) and the conventionally uniformly distributed Geno2 (0.68). By calculating the pedigree average GEBV of the parents and the mortality and survival rates of the offspring, the Pearson correlation between the breeding values ​​predicted by the 500 core SNP loci and the actual survival rates of the offspring was 0.481. Using the Pearson correlation coefficient significance test, the p-value was 0.0046 < 0.01, indicating that the correlation was highly significant at the 0.01 level. Therefore, the 500 core SNP loci provided by this invention are effective. This invention achieves extremely high predictive accuracy with extremely low marker density, successfully solving the industry challenge of balancing high accuracy and low cost, and providing a key technological breakthrough for the rapid and low-cost breeding of disease-resistant varieties of spotted sea bream. Attached Figure Description

[0015] Figure 1 GWAS diagram of the 739-tail reference population; Figure 2 : PVE chart of the 739-tail reference group; Figure 3 : Prediction accuracy plot of all SNP markers in the 739-tailed reference population, 22,757 uniformly distributed SNP markers, and 500 SNP markers selected by MLE-rank and GWAS under the 10×10 cross-validation method; Figure 4 : 121 tails validation population uniform distribution, 500 core SNP loci prediction accuracy map. Detailed Implementation

[0016] To make the objectives and technical solutions of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings. Unless otherwise specified, the experimental methods described in the following tests are conventional methods; for tests where specific techniques or conditions are not specified, they shall be performed in accordance with the techniques or conditions described in the literature in this field or according to the product instructions; unless otherwise specified, the reagents and materials mentioned are all commercially available.

[0017] Example 1: Construction of phenotypes, genotypes, and different SNP marker sets 1. Construct phenotypic data for individual spotted seabream. The spotted sea bream samples used in this embodiment were collected in 2023 and 2024 at Huanghai Aquatic Products Co., Ltd., Haiyang City, Yantai City, Shandong Province, China. Infection experiments were conducted by intraperitoneal injection of the same concentration of Edwardsiella tarda into individual spotted sea bream. Fins of dead individuals were collected every 6 hours after the infection experiment. After one week without any deaths, the caudal fins of the remaining spotted sea bream were collected as surviving individuals, and the recorded mortality and survival data were used as phenotypic data. A reference population of 739 individuals was established, and an additional 121 individuals were added as an independent validation population, for a total of 860 spotted sea bream individuals for the study.

[0018] 2. Screening for high-quality SNPs DNA was extracted from 860 samples. After rigorous quality control, whole-genome sequencing was performed using the BGI T7 sequencing platform. Sequencing data underwent quality control, and clean reads were aligned with the *Sinocyclocheilus maculatus* reference genome (CNGBdb:CNA0019300). After removing repetitive sequences, basic data statistics and mapping analysis were performed. Based on the alignment results, SNP calling was performed using the GATK genome analysis toolkit, retaining only high-quality SNPs (QUAL>30, QD>2, MQ>40) to generate the original VCF file. Quality control was performed using VCFtool, removing low-quality SNPs according to the following criteria: SNPs with a sequencing depth below 15×, deletion rates above 2%, minor allele frequencies (MAF) below 5%, and SNPs that did not conform to Hardy-Weinberg equilibrium (p-value set to 1×10⁻⁶). -6 Only biallelic SNP loci were retained. After quality control, Beagle (v5.2) was used for genotyping, resulting in 2,415,773 high-quality SNP loci for further research.

[0019] 3. Construct the SNP tag set Of the 2,415,773 high-quality SNP loci obtained from quality control, all SNP markers were designated as Geno1. Using the -bp-space command in Plink v1.9 software, uniform sampling was performed based on the physical location of the reference genome, and 22,757 SNP markers were finally selected and designated as Geno2. 500 SNP loci with strong phenotypic correlations were selected through MLE-rank model, GWAS analysis, and PVE analysis and designated as Geno3. These 500 loci were determined as the core marker set for constructing the breeding chip of this invention.

[0020] The MLE-rank model is as follows:

[0021] The representative phenotype is the transpose of the phenotype matrix of surviving (denoted as 1) individuals. The representative phenotype is the phenotype matrix of deceased individuals (denoted as 0). Genotype matrix representing the phenotype of a single individual. Genotype matrix representing individuals with phenotype 0, where AA / Aa / aa are labeled as 0 / 1 / 2, p represents the proportion of surviving individuals in the population, and value represents the vector representing the phenotypic influence of each SNP locus.

[0022] MLE-rank screening of SNP sites involves calculating the value of each SNP site in the value column, sorting them from largest to smallest, and then filtering for SNP sites.

[0023] GWAS is implemented using the mlma command in the gcta software. The GWAS model is as follows: y=P +Zu+

[0024] y represents the phenotypic value, and P represents the covariance matrix of the first three principal components. Z represents the effect size of the principal components, Z represents the SNP genotype matrix, and u represents the effect size of each SNP. Represents the residual.

[0025] The results of GWAS analysis were sorted by p-value from smallest to largest to screen for SNP sites.

[0026] PVE calculation is implemented using the R language, and the PVE formula is as follows: PVE=

[0027] beta is the effect size of the SNP allele, MAF is the minor allele frequency, se is the standard error of the effect size, and N is the total number of individuals involved in the analysis.

[0028] SNP sites were selected by sorting PVE values ​​from largest to smallest.

[0029] The GWAS and PVE plots for the 739-tail reference population are as follows: Figure 1 and Figure 2 .

[0030] Example 2: Evaluation of the heritability of resistance to Edwardsiella tarda in spotted sea bream 1. Purpose of the Implementation Example This embodiment aims to compare the performance of a 500-SNP marker set (Geno3), a genome-wide marker set (Geno1), and a uniformly distributed SNP marker set (Geno2) based on MLE-rank, GWAS analysis, and PVE screening in assessing the heritability of the resistance to Edwardsiella tarda in spotted sea bream, and to verify the effectiveness of the SNP screening strategy of this invention.

[0031] 2. Evaluation Methods The reference population (739 tails) constructed in Example 1 was used. Using the GBLUP model and the probit connection function, the Geno1, Geno2, and Geno3 datasets were analyzed using the Asreml-R software package to estimate their additive genetic variance and heritability.

[0032] 3. Results and Analysis The results of the genetic parameter evaluation for each dataset are summarized in Table 2.

[0033] Table 2: Evaluation of heritability and variance components for three datasets

[0034] Table 2 shows the results of assessing heritability and variance components for the three datasets. The estimated heritability for all SNPs (Geno1) is 0.182 ± 0.062, and the additive genetic variance is 0.223 ± 0.092; the estimated heritability for the uniform distribution (Geno2) is 0.163 ± 0.055, and the additive genetic variance is 0.195 ± 0.079; and the estimated heritability for the 500 core SNP loci (Geno3) is 0.278 ± 0.061, and the additive genetic variance is 0.384 ± 0.117.

[0035] The results in Table 2 show that the genetic parameters estimated by the uniform distribution (Geno2) did not reach the level of all SNP markers (Geno1), indicating that a simple uniform distribution leads to a loss of genetic information. The heritability and variance components estimated by the SNP dataset (Geno3) based on the MLE-rank model, GWAS analysis, and PVE analysis are significantly higher than those estimated by all SNPs (Geno1). This indicates that Geno3 achieves 1.5 times the heritability of all SNP markers at 500 SNP loci, meaning it can capture most of the genetic variation at low density and reduces the influence of "noise" unrelated to the target trait from all SNPs. Geno3 maintains high accuracy of genetic parameters while reducing genotyping costs, providing a key technological foundation for establishing an economical and efficient genomic selection breeding system for disease-resistant spotted sea bream. Example 3: Accuracy Assessment of Genomic Breeding Value (GEBV) Prediction Based on Reference Population Cross-Validation 1. Purpose of the Implementation Example This embodiment aims to compare the accuracy of the core SNP marker set (Geno3), whole-genome SNP marker set (Geno1), and uniformly distributed SNP marker set (Geno2) selected by the present invention in predicting GEBV through 10-fold cross-validation, and to evaluate the performance of the screening strategy of the present invention in the application of genomic selection breeding.

[0036] 2. Evaluation Methods The reference population (739 tails) constructed in Example 1 was used. The GBLUP model, combined with the probit connection function, was employed using the Asreml-R package with 10-fold cross-validation, repeated 10 times (for a total of 100 iterations). In each iteration, 90% of the individuals were used as the training set to train the model, and 10% were used as the validation set to test the model. The area under the ROC curve (AUC) was calculated using the phenotypic data of the validation set and GEBVs to determine the prediction accuracy.

[0037] 3. Results and Analysis The average of the cross-validation results from each iteration was used to obtain 10 prediction accuracy results to evaluate the model's performance. The results are shown in Table 3 and... Figure 3 .

[0038] Table 3: Average Prediction Accuracy of 10-Fold Cross-Validation

[0039] As shown in Table 3, the average prediction accuracy of Geno3 (0.686) after 100 cross-validations is significantly higher than that of Geno1 (0.607) and Geno2 (0.647). This result indicates that the 500 SNPs strongly associated with the target trait selected by Geno3 not only effectively capture most of the genetic information but also efficiently estimate GEBVs with high prediction accuracy when performing genomic selection in a reference population. Therefore, Geno3 can be used for liquid-phase chip fabrication, effectively reducing the cost of genotyping and benefiting the breeding of disease-resistant varieties of spotted sea bream.

[0040] Example 4: Validation of the application effect of the core SNP marker set (Geno3) in an independent validation population. 1. Purpose of the Implementation Example This embodiment aims to evaluate the actual predictive performance of the 500 core SNP marker set (Geno3) finally selected by the present invention in an independent validation population (121 individuals), and compare it with Geno1 and Geno2, thereby confirming the practical application value of the breeding chip developed based on Geno3 in disease-resistant breeding of spotted sea bream.

[0041] 2. Evaluation Methods The independent validation population (121 tails) defined in Example 1 was used. Using the GBLUP model and the probit connection function, and through the Asreml-R package, the independent validation population was trained using the reference population (739 tails) from Example 1 and the 121 tails as the validation population, based on the SNP marker sets of Geno1, Geno2, and Geno3, respectively. The prediction accuracy of each marker set was compared by calculating the area under the ROC curve (AUC) between the true phenotype (survival / death) of individuals in the independent validation population and the GEBV predicted by each marker set.

[0042] 3. Results and Analysis Figure 4 The results show that the prediction accuracy AUC of the 500 core SNP sites (Geno3) provided by this invention is 0.87, which is significantly higher than the AUC of 0.68 for the uniform distribution (Geno2) and the AUC of 0.7 for all SNPs (Geno1). Therefore, the 500 core SNP sites finally screened based on the MLE-rank model, GWAS model and PVE analysis are effective for breeding of spotted sea bream resistant to Edwardsiella tarda. The prepared liquid phase chip can be used for breeding disease-resistant spotted sea bream.

[0043] Example 5: Validation of the application of the core marker set (Geno3) in pedigree selection and parentage assessment 1. Purpose of the Implementation Example This embodiment aims to verify the actual effect and accuracy of the 500 core SNP marker set (Geno3) screened in this invention in breeding by evaluating the GEBV of parents and families, and to compare it with whole-genome SNPs (Geno1) and uniformly distributed SNPs (Geno2).

[0044] 2. Evaluation Methods Parental data from 32 maternal parents and 33 paternal parents, forming 33 families, were selected. Using the GBLUP model, the GEBV of each parent (paternal and maternal) was calculated based on the Geno1, Geno2, and Geno3 datasets, respectively. The average GEBV of each family corresponding to the parents was taken as the average GEBV of that family.

[0045] 3. Results and Analysis 3.1 Screening of disease-resistant and susceptible families By comparing family GEBV rankings calculated from three different datasets, the accuracy of 500 core SNP loci in screening disease-resistant and susceptible families was examined.

[0046] Table 4: Family average GEBV

[0047] As shown in Table 4, the average GEBV of the top 6 families in the 500 core SNP dataset, the all 2,415,773 SNP dataset, and the uniformly distributed 22,757 SNP dataset is the same, namely F2320, F2306, F2349, F2309, F2340, and F2324. Among the bottom 6 families, five families are the same, namely F2326, F2336, F2321, F2325, and F2317. This result shows that although the Geno3 marker set of this invention only has 500 SNPs, its decision-making ability in distinguishing between disease-resistant and susceptible families is almost identical to that of using whole genome data (Geno1) and uniform distribution (Geno2). Therefore, the 500 core SNP loci provided by this invention can be applied to practical breeding.

[0048] 3.2 Correlation Analysis of Predictive Accuracy The accuracy of predictions is determined by calculating the Pearson correlation coefficient between the estimated pedigree mean GEBV for each dataset and the actual disease-resistant survival rate of the offspring in that pedigree.

[0049] Table 5: Prediction accuracy of core SNP sites

[0050] Table 5 shows that the correlation between the family mean GEBV and family mortality / survival rate was analyzed using Pearson correlation coefficient analysis on 500 core SNP loci. The results showed a correlation coefficient of 0.481. Using the Pearson correlation coefficient significance test, the P-value was 0.0046 < 0.01, indicating that the correlation was highly significant at the 0.01 level. Therefore, the 500 core SNP loci provided by this invention are effective and significantly reduce the cost of genotyping. Although the uniform distribution has a slightly higher correlation (0.536), the number of loci is 45.5 times that of the core SNP loci in this invention. Considering the cost of genotyping, a uniform distribution is unreasonable for cost input in actual breeding. Therefore, the core SNP loci provided by this invention can reduce the cost of genotyping while ensuring accuracy. In summary, this invention provides 500 core SNP loci that can accurately screen for disease-resistant families, and the results are consistent with those obtained using whole-genome SNP data, which can be directly used to guide breeding practices. The core SNP loci provided by this invention achieve 91.8% predictive accuracy with whole-genome SNPs with the lowest number of markers, requiring only 0.02% of the total number of SNPs, thus reducing the cost of genotyping. The low-cost chip developed based on this can promote the industrialization and large-scale production of disease-resistant breeding for spotted sea bream.

Claims

1. A liquid-phase chip for genomic selection breeding of spotted sea bream resistant to Edwardsiella tarda, characterized in that, The chip contains a probe array capable of specifically capturing or detecting 500 SNP sites in the reference genome of the spotted sea bream, as shown in Table 1; the sequences of the 500 SNP sites are SEQ ID NO.1-SEQ ID NO.250, with the 36th and 107th bases indicated by degenerate base y / r. Table 1: Information on 500 SNP loci 。 2. The liquid phase chip as described in claim 1, characterized in that, The screening method for the 500 SNP sites includes the following steps: (a) Obtain whole-genome resequencing data of the reference population of spotted sea bream, and obtain a high-quality SNP marker set Geno1 after quality control and genotyping; (b) The SNP sites in Geno1 were analyzed and ranked using the maximum likelihood estimation ordination model, genome-wide association analysis, and phenotypic variance explained rate analysis, respectively. (c) Combining the sorting results of the three methods in step (b), the 500 SNP sites with the highest correlation to the trait of resistance to Edwardsiella tarda were selected to form the core SNP marker set.

3. The application of the liquid phase chip according to claim 1 in any of the following: (a) Assessing the resistance of individual spotted sea bream to Edwardsiella tarda; (b) Estimate the genomic breeding value of individual spotted sea bream; (c) Screening for spotted sea bream parents or families resistant to Edwardsiella tarda; (d) Prepare disease-resistant breeding products of spotted sea bream.

4. A reagent kit, characterized in that, It includes the liquid phase chip of claim 1, and reagents for genotyping.

5. The application of the kit according to claim 4 in the genomic selection breeding of spotted sea bream resistant to Edwardsiella tarda.

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