WO2021119980A1 - 一种牙鲆抗病育种基因芯片及其应用 - Google Patents

一种牙鲆抗病育种基因芯片及其应用 Download PDF

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WO2021119980A1
WO2021119980A1 PCT/CN2019/125857 CN2019125857W WO2021119980A1 WO 2021119980 A1 WO2021119980 A1 WO 2021119980A1 CN 2019125857 W CN2019125857 W CN 2019125857W WO 2021119980 A1 WO2021119980 A1 WO 2021119980A1
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breeding
disease
gene chip
snp
resistant
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PCT/CN2019/125857
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English (en)
French (fr)
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陈松林
周茜
卢昇
陈亚东
刘洋
徐文腾
李仰真
王磊
杨英明
王娜
李希红
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中国水产科学研究院黄海水产研究所
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Priority to JP2020556756A priority Critical patent/JP7158496B2/ja
Priority to PCT/CN2019/125857 priority patent/WO2021119980A1/zh
Priority to CN201980003897.4A priority patent/CN111278994B/zh
Publication of WO2021119980A1 publication Critical patent/WO2021119980A1/zh

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    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q1/00—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
    • C12Q1/68—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions involving nucleic acids
    • C12Q1/6876—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes
    • C12Q1/6888—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for detection or identification of organisms
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q1/00—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
    • C12Q1/68—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions involving nucleic acids
    • C12Q1/6813—Hybridisation assays
    • C12Q1/6834—Enzymatic or biochemical coupling of nucleic acids to a solid phase
    • C12Q1/6837—Enzymatic or biochemical coupling of nucleic acids to a solid phase using probe arrays or probe chips
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q2600/00—Oligonucleotides characterized by their use
    • C12Q2600/124—Animal traits, i.e. production traits, including athletic performance or the like
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q2600/00—Oligonucleotides characterized by their use
    • C12Q2600/156—Polymorphic or mutational markers
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
    • Y02A40/00—Adaptation technologies in agriculture, forestry, livestock or agroalimentary production
    • Y02A40/80—Adaptation technologies in agriculture, forestry, livestock or agroalimentary production in fisheries management
    • Y02A40/81—Aquaculture, e.g. of fish

Definitions

  • the invention belongs to the technical field of aquatic genetic breeding, and specifically relates to a preparation method and application of a gene chip used for the selection and breeding of disease-resistant fine breeds of Japanese flounder.
  • Aquaculture is an important source of food in my country, and fish farming is the pillar industry of aquaculture. In 2015, fish farming output was 284.57 million tons, accounting for 57.6% of the entire aquaculture output; farmed fish has become my country’s food An important source of protein.
  • Paralichthys olivaceus is a mariculture fish worldwide and one of the leading fish in mariculture in my country.
  • the main diseases that harm Japanese flounder and other farmed fish include bacterial and viral diseases.
  • the most harmful diseases are Edwards' disease, vibriosis and lymphocytic virus disease.
  • antimicrobial drugs or vaccines and other disease prevention measures have certain effects, they cannot fundamentally solve the problem of disease in aquaculture; and antibiotics are easy to accumulate in fish, which reduces the quality of farmed fish products and is beneficial to consumers’ health.
  • the use of antibiotics cannot meet people's growing demand for green aquatic products without drug residues. Therefore, the selection and breeding of disease-resistant fish species is one of the major issues that urgently need to be overcome in the field of aquatic products in my country.
  • the breeding of fine fish species is mainly based on the selection of phenotypic traits, including population selection, family selection, cross breeding and BLUP selection, etc., mainly based on easy-to-measure phenotypic values such as body length and weight.
  • the calculated breeding value is selected.
  • molecular markers economic traits are selected by locating molecular markers associated with important economic traits.
  • the number of molecular markers used in traditional molecular marker-assisted breeding is very limited, and the selection of single-gene traits or quality traits The effect is good, but the selection effect for quantitative traits determined by multiple genes is not ideal.
  • disease resistance traits are quantitative traits controlled by multiple genes, it is difficult to directly measure and the selection accuracy is quite low. Therefore, the selection of disease-resistant elite species has been progressing slowly, which limits the cultivation of new disease-resistant fish species. Plant new breeding techniques to overcome this problem.
  • Gene chips also known as DNA chips and DNA microarrays, use photoetching technology to use silicon wafers as solid supports to synthesize oligonucleotides from a large number of selected and optimized DNA sequences and place them on specially treated glass slides. After denaturation and fixation, a DNA microarray is formed. Based on nucleic acid molecular hybridization technology, gene chips can simultaneously perform parallel hybridization and analysis of tens of thousands or even hundreds of thousands of DNA fragments, and have the advantages of high throughput, parallelism, high efficiency, and small sample volume. At present, gene chips have been widely used in the diagnosis of human diseases and tumors, the analysis of animal and plant genetics, and genetic breeding.
  • Bovine SNP50 Beadchip 54K SNP
  • Bovine SNP50 Beadchip 54K SNP
  • the purpose of the present invention is to provide a gene chip for the breeding of excellent disease-resistant species of Japanese flounder, solve the problem of lack of gene chip in the breeding of improved fish species, make up for the shortcomings of traditional breeding technology, and provide the breeding
  • a new molecular breeding method provides the fish breeding industry with technical means for the selection of disease-resistant excellent varieties, realizes the upgrading of fish breeding technology, and promotes the rapid development of the fish seed industry.
  • the present invention first provides a SNP site related to the disease resistance of Japanese flounder, where the SNP site is the 36th base of any one of SEQ NO: 1-SEQ ID NO: 48697;
  • the SNP locus of the present invention can be used in the breeding of disease-resistant fine breeds of Japanese flounder;
  • the SNP site provided by the present invention is used to prepare detection products for the selection of disease-resistant fine breeds of Japanese flounder;
  • the detection product is preferably a gene chip
  • Another aspect of the present invention is to provide a gene chip used for breeding of advanced disease-resistant Paralichthys olivaceus, which can detect SNP sites related to the disease resistance of Paralichthys olivaceus;
  • Another aspect of the present invention is to provide a method for screening disease-resistant individuals of Japanese flounder, which is carried out by using the above-mentioned gene chip;
  • the method includes the following steps:
  • GEBV estimated breeding value
  • weighted GBLUP weighted best linear unbiased estimation
  • the gene chip based on the SNP locus related to the disease resistance of Japanese flounder provided by the present invention can be used for the selection of disease-resistant individuals of Japanese flounder, and the actual selection accuracy is close to the theoretical value, so it can improve the selection of good disease-resistant Japanese flounder. Accuracy, shorten the breeding cycle, provide gene chip technology for the selection of disease-resistant fine species of Japanese flounder, and open up a new way of gene chip breeding for the selection and breeding of disease-resistant fine species of fish.
  • the present invention establishes a method for producing and applying a gene chip for breeding disease-resistant fine species of Japanese flounder, and aims to provide a new molecular breeding technique for breeding of fine disease-resistant species of Japanese flounder and other fish.
  • SNP The abbreviation of Single Nucleotide Polymorphism, that is, single nucleotide polymorphism, a DNA sequence polymorphism caused by a single nucleotide variation at the genome level.
  • Gene chip Through micro-processing technology, tens of thousands or even millions of specific DNA sequence fragments are regularly arranged and fixed on silicon wafers, glass slides and other supports to form a two-dimensional DNA probe array , It can perform genotyping and molecular testing of genetic material (DNA, etc.).
  • Merged bases According to the codon mergeability, a symbol is often used to replace two or more bases. For example, R stands for A/G, Y stands for C/T, M stands for A/C, K stands for G/T, S stands for G/C, W stands for A/T, etc.
  • Reference population In genome selection, a population with phenotypic data obtained through experiments such as artificial infection, usually selected from a large population with phenotypic traits can represent the phenotypic distribution of the entire population, and the genome has been re-sequenced , The collection of individuals whose genotype data is obtained, and the actual genome selection calculation is performed.
  • Candidate population In genome selection, a candidate population refers to a population that has obtained genotype data through genome resequencing, but does not have phenotype data. This population has breeding potential and is intended to be used for the next actual breeding of fine seeds. .
  • GBLUP The abbreviation of Genomic Best Linear Unbiased Prediction, which is the best linear unbiased prediction of the genome. It is a method of estimating the genetic relationship between individuals (G matrix) by using high-density molecular markers in the genome to estimate the breeding value of the genome.
  • GEBV The abbreviation of Genomic Estimated Breeding Values, that is, the estimated breeding value of the genome, which is obtained by adding up the effect estimates of all markers or haplotypes on the whole genome.
  • Example 1 Screening of SNP sites of "Fishxin No. 1" gene chip and chip preparation
  • the reference population and candidate population individuals used in the selection of the Paralichthys olivaceus genome are all derived from the Paralichthys domesticus family established by this research group since 2003. In the course of years of cultivation, they have gradually combined with the rapid growth of Paralichthys olivaceus populations from Korea, Japan and my country. , Good traits such as disease resistance and stress resistance. Especially since 2013, in response to the increasingly serious situation of Edwards sluggish disease in the Paralichthys olivaceus aquaculture industry, research has been carried out on the selection and breeding of families of Paralichthys olivaceus resistant to Edwardsiella sluggish disease.
  • the reference population of Paralichthys olivaceus was extracted by DNA, and after detection, there were 931 available individuals (Table 3).
  • Table 3 Paralichthys olivaceus genome selection reference population and candidate population statistics table
  • the library type is a paired-end DNA library (insert 350bp).
  • the sequencing and data output are completed using the Illumina Hiseq X10 sequencing platform. Quality The average amount of data obtained from the control is 2G/unit.
  • BWA http://bio-bwa.sourceforge.net/
  • Samtools http:// www.htslib.org/
  • the 42.2M Paralichthys olivaceus SNP marker obtained in step 2 is screened according to the following criteria: remove the sites with the deletion rate> 0.1 and the minimum allele frequency (MAF) ⁇ 0.05, and remove the repetitive sequence or transposon Remove the sites that do not meet Hardwin’s equilibrium, and obtain 3.4M Paralichthys olivaceus SNP markers.
  • step (1) For the 3.4M molecular markers screened in step (1), perform SNP locus effect value analysis and estimate breeding value calculation,
  • the Bayes C ⁇ algorithm is used for the calculation of the genome selection of Japanese flounder, and the analysis model equation is:
  • y is the phenotypic value
  • u is the population average
  • qi is the marker effect obeys a normal distribution
  • m is the total number of markers
  • X is the incidence matrix corresponding to qi
  • e is the residual.
  • the Affymetrix Axiom genotyping probe is further used to design a biological analysis process to design and evaluate the SNPs screened in step (2), and remove the sites with a probe conversion possibility evaluation score of ⁇ 0.6. In addition, it is ensured that SNPs cover the entire genome and are evenly distributed. There are no other SNPs within 35bp of the SNP flanking sequence. The 35bp GC content of the SNP flanking sequence is 30-70%. Finally, 48697 Japanese flounder SNP markers were screened for chip production. The sequences of 48697 SNP molecular markers are recorded in the sequence table.
  • the Paralichthys olivaceus SNP chip (gene chip) is made with the American Thermo Fisher Affymetrix Axiom chip manufacturing technology, which contains a total of 48,697 Paralichthys olivaceus SNP sites, and each chip can detect 24 samples at the same time.
  • electrophoresis requires a single band of DNA, fragment length greater than 10kb, good integrity, no degradation, sample quality DNA detection results; UV spectrophotometer detection A260/280: 1.8-2.0, A260/230>1.5 , The concentration is not less than 20ng/ ⁇ l, and the total amount is not less than 4 ⁇ g.
  • the chip detection sample preparation was carried out according to the standard procedure operation of SNP chip detection sample preparation by Thermo Fisher Company (https://www.thermofisher.com/). 1. Add a high-quality DNA template of no less than 4ug to a 2ml*96 deep-well plate, add a denaturant to denaturate at room temperature, and terminate the denaturation 10 minutes after denaturation to obtain single-stranded DNA; it will be used to amplify the chip site Add 48697 pairs of primers, isothermal amplification enzymes, dNTPs, etc.
  • the hybridization solution used 5% gel Gel electrophoresis test result quality, amplification product quality test result, the band is clear, the brightness is high; the hybridization solution is hybridized with a temperature-controlled amplification instrument, the conditions are 95 °C 10 min, 48 °C 3 min, and then the hybridization solution has been maintained at 48°C; Immerse the chip block in the hybridization solution, hybridize in the hybridization oven at 48°C for 24 hours, then elution, connect the fluorescent protein, fix the fluorescent protein, hybridize the probe, scan the fluorescent signal, each signal point is a probe Hybridization results. After obtaining the hybridization results for each site, the chip scanning results are analyzed with Axiom Analysis Suite (AxAS) software (Thermo Fisher Company, USA).
  • AxAS Axiom Analysis Suite
  • Table 4 Individual information of Paralichthys olivaceus candidate population used for gene chip typing
  • the Affymetrix GeneTitan gene chip processing system is used to complete probe hybridization, staining and chip scanning.
  • the specific operation is as follows: add 4 ⁇ g of high-quality DNA template to a 2ml*96 deep well plate, add denaturing agent to denaturate (28°C), quickly add denaturation stop solution after denaturation 10min (reaction time not longer than 10min) to stop denaturation , To obtain single-stranded DNA; add 48697 pairs of primers, isothermal amplification enzymes, dNTPs and reaction solution used to amplify the chip site into the deep-well plate, seal the deep-well plate, and perform isothermal amplification at 37°C 22 -36h; preferably after 24h amplification, the reaction solution should be inactivated by high temperature 65°C for 20-30min, then transferred to 37°C incubator and incubated for 40min.
  • AxAS software (American Thermo Fisher Company) was used to analyze the microarray scanning results to obtain the genotyping results of each sample.
  • the analysis results show that the average typing rate of the chip is 98.77%, and the typing effect is good; among them, the proportion of high-quality SNP is 74.61%, and each sample can produce high-quality typing information.
  • Some individuals are selected from the reference population to verify the reliability of gene chip typing. These selected individuals have both re-sequenced genotypes and genotypes obtained using "Fishcore No. 1" gene chip typing. Part of the individuals selected from the inventors' existing reference populations of Japanese flounder were typed and counted using gene chips. Evaluate the chip typing by counting the consistency between the genotype obtained by using the chip (0/1/2 means AA/Aa/aa) and the genotype obtained by re-sequencing, and the correlation coefficient of GEBV estimated by re-sequencing and chip data Effect. If the consistency of the typing results is more than 88% and the correlation coefficient between GEBV is more than 0.9, the chip is considered to have a good typing effect.
  • Table 5 The number of individuals and markers used to verify the typing effect of the gene chip
  • the number of tags shared by the above 4 files is 11,719, and the number of individuals that can be found in the reference population is 95.
  • Table 6 Comparison of GEBV values estimated by verified individuals using "Yixin No. 1" gene chip and resequencing typing
  • the design sites of the "Fishcore No. 1" gene chip were extracted from the inventor's existing reference population, and weighted GBLUP was implemented by using the information of these sites, and 5-fold cross-validation and random grouping were used to evaluate the accuracy of the weighted GBLUP prediction.
  • Methods the area under the receiver operating characteristic curve (AUC) was used as an index to evaluate the prediction accuracy of the weighted GBLUP method.
  • the analysis model uses a generalized linear mixed model. In order to reduce the random error of grouping, the entire data set is grouped 10 times, and each group is calculated 5 times. Therefore, a total of 50 calculations are performed, and the average value of the 50 AUC is used as the final evaluation result.
  • the genotype of the candidate individual (obtained from the "Fishxin 1" gene chip typing) is merged with the inventor's existing reference population genotype, and then R is used to construct a weighting G matrix, finally bring the prepared weighted G matrix and phenotypic data into ASReml-R, use the weighted GBLUP method to estimate the GEBV of the parents of each family, and then use the average of the parental GEBV as the GEBV of the corresponding family.
  • GEBV Genomic Estimated Breeding Value
  • the infection survival rate of the offspring needs to be converted by exp(x)/(1+exp(x)). After conversion, the survival rate of families higher than the average value is recorded as 1, and the survival rate of families lower than the average value is recorded as 0.
  • the typing results of all candidate individuals are not merged, so the typing results of each chip need to be merged with the genotype of the reference population separately.
  • Use PLINK and R to process candidate individual typing files, and then select individuals from the candidate individuals for subsequent verification, and store these individual information in a text file, according to family number, individual number, paternal number, and maternal number
  • the specific implementation method is:
  • the GEBV of 16 families of Japanese flounder was obtained, and the calculation showed that the AUC (accuracy) between the GEBV of each family and the survival rate of the corresponding infection was 0.794. See Table 8 for GEBV and the survival rate of infection in each family.
  • the 16 progeny families of the candidate group were divided into 6 families with high survival rate (average survival rate 62.4%) and 10 families with low survival rate (average survival rate 33.47%) (Table 8).
  • Comparison For the GEBV of the parents of the high and low survival families the average GEBV of the parents of the high survival families was 2.10, and the average GEBV of the parents of the low survival families was 1.34 (Table 8). Calculations show that the accuracy of using the GEBV value of these flounder families to predict their infection survival rate can reach 0.794, which is close to the theoretical value. Therefore, the gene chip designed by the inventors can be well applied to the breeding of disease-resistant fine breeds of Japanese flounder.
  • the "Fishcore No. 1" gene chip is used to genotype the individual of the Paralichthys olivaceus candidate population, the weighted GBLUP is used to calculate the Genomic Breeding Value (GEBV), and the disease-resistant broodstock of Paralichthys olivaceus is screened according to the value of GEBV.
  • the anti-infection survival rate of the offspring grown from the broodstock is significantly improved, which indicates that the "Fishcore No. 1" gene chip can be promoted and applied in the breeding of fine, disease-resistant Japanese flounder.
  • the gene chip based on the SNP locus related to the disease resistance of Japanese flounder provided by the present invention can be used for the selection of disease-resistant individuals of Japanese flounder, and the actual selection accuracy is close to the theoretical value, so it can improve the selection of good disease resistance of Japanese flounder.
  • the accuracy and shortening of the breeding cycle provide gene chip technology for the selection of disease-resistant fine species of Japanese flounder, and open up a new way of gene chip breeding for the selection and breeding of disease-resistant fine species of fish.

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Abstract

提供一种用于牙鲆抗病良种选育的基因芯片，解决鱼类良种培育中缺乏基因芯片的问题，弥补传统育种技术的不足，为鱼类抗病高产优质良种培育提供一种新的分子育种方法，为鱼类养殖业提供抗病优良品种选育的技术手段，实现鱼类育种技术的更新换代，推动鱼类种业快速发展。提供的基于与牙鲆抗病性相关的SNP位点的基因芯片可用于牙鲆抗病个体选育，并且实际的选择准确性与理论值接近，因此可以提高牙鲆抗病良种选择的准确性、缩短育种周期，为牙鲆抗病良种选育提供了基因芯片技术，为鱼类抗病良种选育开辟基因芯片育种新途径。

Description

一种牙鲆抗病育种基因芯片及其应用 技术领域
本发明属于水产遗传育种技术领域，具体涉及一种用于牙鲆抗病良种选育的基因芯片的制备方法及应用。
背景技术
水产养殖业是我国食品的重要来源，而鱼类养殖业又是水产养殖业的支柱产业，2015年鱼类养殖产量28457万吨，占整个水产养殖产量的57.6％；养殖鱼类已成为我国食物蛋白的重要来源。
然而，随着鱼类养殖业的迅速发展，缺乏优良品种、养殖种类种质退化；养殖规模扩大、集约化程度的提高以及养殖环境的恶化导致水产养殖病害发生频繁，养殖产品药残突出等问题也严重制约了鱼类养殖业的可持续发展。仅就鱼类而言，由于高密度养殖形成的免疫压抑，导致养殖鱼类的抗病力下降。由于对鱼类的免疫抗病机理及抗病力的分子遗传研究还不够深入，难以从分子水平上提出预防鱼类病害的方案；而且，由于缺乏抗病功能基因和抗病分子标记，也难以进行抗病优良品种的培育，因此目前养殖生产只能依靠抗病力差的野生或人工繁殖多代的苗种，导致流行性病害在鱼类养殖中频繁发生。据不完全统计，我国鱼类养殖业每年因病害造成的直接经济损失达100亿元之巨。病害已成为制约我国鱼类养殖业可持续发展的瓶颈。
牙鲆是世界性海水养殖鱼类，也是我国海水养殖的主导鱼类之一。但牙鲆养殖业中也存在病害频发、死亡率高等问题。危害牙鲆等养殖鱼类的主要病害包括细菌性和病毒性疾病。其中危害较大的疾病分别是爱德华氏菌病、弧菌病和淋巴囊肿病毒病。抗菌素类药物或者疫苗等防病措施虽然有一定效果，但无法从根本上解决水产养殖中的病害问题；且抗菌素类药物存在容易在鱼体内积累，降低养殖鱼类商品质量，对消费 者的健康具有潜在危害，容易使病原菌产生抗药性以及严重污染养殖环境等问题，因而在水产养殖业中的应用越来越受到限制。同时，抗菌素的使用也不能满足人们日益增长的对无药物残留的绿色水产品的需求。因此，鱼类抗病良种选育是我国水产领域急需攻克的重大课题之一。
迄今为止，鱼类良种选育主要是基于表型性状的选育，包括群体选育、家系选育、杂交选育和BLUP选育等，主要是根据体长、体重等容易测量的表型值计算出来的育种值进行选择。分子标记出现以后，则是通过定位与重要经济性状相关联的分子标记而对经济性状进行选择，但传统的分子标记辅助选育所用的分子标记数量非常有限，对单基因性状或质量性状的选择效果不错，但对于多基因决定的数量性状的选择效果不理想。由于抗病性状是由多个基因控制的数量性状，难以直接测量，选择准确性相当低，所以对抗病良种的选育一直进展缓慢，限制了鱼类抗病新品种的培育，迫切需要一种新的育种技术来攻克这一难题。
基因芯片，又称DNA芯片、DNA微阵列，是采用光蚀刻技术，以硅片做固相支持物，将大量经过选择优化的DNA序列合成寡核苷酸，点在经特殊处理的玻片上，经变性、固定后形成DNA微阵列。基于核酸分子杂交技术，基因芯片可以同时对几万甚至几十万DNA片段进行并行化杂交和分析，具有高通量、并行性、高效率、样品量小等优点。目前，基因芯片已广泛应用于人类疾病、肿瘤的诊断，动植物遗传学分析及遗传育种中。在动物育种中，基因芯片已成功应用于畜牧，特别是奶牛、猪等物种的良种选育中。例如，在奶牛中，已先后开发出Bovine3K chip，Bovine25K SNP chip、BovineHD 700K，BovinLD 7K等多款基因芯片。目前，北美、欧洲、澳洲等采用Bovine SNP50 Beadchip芯片(54K SNP)作为基因组SNP标记分型检测的通用平台，并基于大规模参考群体获得的SNP分型结果，开展了产奶量、繁殖力、抗病力等多种经济性 状的全基因组关联分析，建立了奶牛基因组选择体系。通过基因组选择可以实现初生公牛的早期选择，缩短了奶牛育种的世代间隔，增加遗传进展，极大提高种公牛的选择效率，显著节约了养殖和育种成本。但在水产养殖动物上，目前还未见育种用基因芯片，特别是抗病育种基因芯片的报道。
发明内容
本发明的目的是提供一种用于牙鲆抗病良种选育的基因芯片，解决鱼类良种培育中缺乏基因芯片的问题，弥补传统育种技术的不足，为鱼类抗病高产优质良种培育提供一种新的分子育种方法，为鱼类养殖业提供抗病优良品种选育的技术手段，实现鱼类育种技术的更新换代，推动鱼类种业快速发展。
本发明首先提供与牙鲆抗病性相关的SNP位点，所述的SNP位点是序列为SEQ NO:1—SEQ ID NO:48697中任一个序列的第36位碱基；
本发明的SNP位点可用于牙鲆抗病良种选育中；
本发明所提供的SNP位点用于制备牙鲆抗病良种选育用的检测制品；
所述的检测制品，优选为基因芯片；
本发明再一个方面是提供一种用于牙鲆抗病良种选育的基因芯片，其能够检测与牙鲆抗病力相关的SNP位点；
本发明再一个方面是提供一种牙鲆抗病个体的筛选方法，是使用上述的基因芯片来进行的；
所述的方法，包括如下的步骤：
1)提取候选群体中的个体基因组DNA，并利用上述基因芯片检测并获得SNP标记的分型结果；
2)从参考群体的SNP集合中提取出与基因芯片相同的SNP位点的 分型结果，再将参考群体的SNP分型结果和候选群体利用芯片获得的分型结果合并；
3)利用合并的基因型和参考群体表型，使用加权GBLUP方法估算候选群体的估计育种值(GEBV)，再根据GEBV值确定待检测个体的抗病潜力；
利用参考群体基因型，使用加权最佳线性无偏估计(加权GBLUP)方法估算预测准确性；其中，5倍交叉验证为预测准确性评判方法，特征曲线下面积(AUC)为评判预测准确性指标；AUC越接近1，预测准确性越高。
[根据细则91更正 22.05.2020]　
本发明提供的基于与牙鲆抗病性相关的SNP位点的基因芯片可用于牙鲆抗病个体选育，并且实际的选择准确性与理论值接近，因此可以提高牙鲆抗病良种选择的准确性、缩短育种周期，为牙鲆抗病良种选育提供了基因芯片技术，为鱼类抗病良种选育开辟基因芯片育种新途径。
具体实施方式
本发明建立了牙鲆抗病良种培育的基因芯片的制作与应用方法，旨在为牙鲆等鱼类抗病良种培育提供一种新的分子育种技术手段。
下面对本发明所涉及的术语解释如下：
SNP：Single Nucleotide Polymorphism的缩写，即单核苷酸多态性，在基因组水平上由单个核苷酸的变异所引起的DNA序列多态性。
基因芯片：通过微加工技术，将数以万计，乃至百万计的特定的DNA序列片段，有规律地排列固定于硅片、玻片等支持物上，构成的一个二维DNA探针阵列，可对遗传物质(DNA等)进行基因分型及分子检测。
兼并碱基：根据密码子的兼并性,常用一个符号代替某两个或者更多碱基。如R代表A/G,Y代表C/T，M代表A/C，K代表G/T，S代表G/C，W代表A/T等。
参考群体：在基因组选择中，通过人工感染等实验获得的具有表型数据的群体，通常从有表型性状的大的群体中筛选出的可以代表整个群体表型分布，并进行了基因组重测序，获得了基因型数据，进行实际基因组选择计算的个体的集合。
候选群体：在基因组选择中，候选群体是指通过基因组重测序，获得了基因型数据，但没有表型数据的群体，该群体具有育种潜力，拟用于接下来实际良种培育工作的个体的集合。
GBLUP：Genomic Best Linear Unbiased Prediction的缩写，即基因 组最佳线性无偏预测，是利用基因组内高密度分子标记估算个体间亲缘关系(G矩阵)，进行基因组育种值估计的方法。
GEBV：Genomic Estimated Breeding Values的缩写，即基因组估计育种值，通过将全基因组上所有标记或单倍型的效应估计加和得到。
下面结合实施例对本发明进行详细的描述。
实施例1：“鱼芯1号”基因芯片SNP位点的筛选及芯片制备
1、牙鲆抗迟缓爱德华氏菌病参考群体的建立及表型性状测定
牙鲆基因组选择所用的参考群体和候选群体个体均来源于本课题组自2003年以来建立的牙鲆家系，在多年培育过程中，逐渐结合来源于韩国、日本和我国的牙鲆群体的快速生长、抗病抗逆等优良性状。尤其是从2013开始，针对牙鲆养殖业中迟缓爱德华氏菌病日益严重的形势，开展了牙鲆抗迟缓爱德华氏菌病家系选育的研究。
2013-2015年，连续对当年建立的牙鲆家系进行人工腹腔接种感染迟缓爱德华氏菌实验，对感染实验鱼苗收集鳍条，测量生长和抗病表型，2013，2014和2015各年分别采集样品4577尾、5942尾和6919尾，用于选择构建牙鲆抗迟缓爱德华氏菌病基因组选择的参考群体。
从感染实验的样品中，挑选出96个家系(2013年32个，2014年10个，2015年48个)，每个家系按照死亡率选取等比例死亡和存活个体10-15个，组成基因组选择的参考群体，将选取个体的感染实验结果(死亡或存活)作为参考群体的表型性状(表2)。
表2：用于基因组重测序的牙鲆个体统计表
Figure PCTCN2019125857-appb-000001
Figure PCTCN2019125857-appb-000002
2、牙鲆全基因组重测序及SNP位点鉴定
牙鲆参考群体经DNA提取，检测后，共有可用个体931个(表3)。
表3：牙鲆基因组选择参考群体与候选群体统计表
Figure PCTCN2019125857-appb-000003
提取931个参考群体个体的基因组DNA，待DNA检测合格后，构建二代测序文库，建库类型为双端DNA文库(插入片段350bp)，使用Illumina Hiseq X10测序平台完成测序和数据产出，质量控制所得平均数据量为2G/个体。以本课题组提供的牙鲆基因组序列(GenBank ID：PRJNA73673)为参考基因组，使用BWA(http://bio-bwa.sourceforge.net/)软件进行序列比对，然后用Samtools(http://www.htslib.org/)软件进行SNP预测和鉴定，获得42.2M个SNP集合。
3、SNP位点评价与筛选
(1)对步骤2所获得的42.2M牙鲆SNP标记,根据如下标准进行筛选：去除缺失率>0.1，最小等位基因频率(MAF)<0.05的位点，去除处于重复序列或转座子中的位点、去除不符合哈德温平衡的位点，获得3.4M个牙鲆SNP标记。
(2)对步骤(1)筛选的3.4M分子标记，进行SNP位点效应值分析和估计育种值计算，
牙鲆基因组选择计算选用Bayes Cπ算法，分析模型等式为：
Figure PCTCN2019125857-appb-000004
模型中，y为表型值，u为群体平均值，qi是标记效应服从正态分布
Figure PCTCN2019125857-appb-000005
m是标记的总数，X是与qi对应的关联矩阵，e是残差。
使用R语言包BGLR所提供的BayesCπ算法，结合整理好的基因型数据genotype.csv与表型数据phonetype.csv，对全基因组重测序的参考群体共931个牙鲆个体进行基因组选择计算。然后将所得SNP位点效应值由大到小排序，去除估计育种值<10 -5的位点，共得到864229个SNP位点用于基因芯片SNP位点的选取。
(3)进一步采用Affymetrix Axiom基因分型探针设计生物分析流程对步骤(2)筛选到的SNP分别进行探针设计和评估，去除探针转换可能性评估分值<0.6的位点。此外，保证SNP覆盖整个基因组且均匀分布、SNP的侧翼序列35bp内不存在其他SNP，SNP侧翼序列35bp的GC含量30-70％，最终筛选到48697个牙鲆SNP标记用于芯片制作，所述48697个SNP分子标记的序列记录在序列表中。
采用美国Thermo Fisher公司Affymetrix Axiom芯片制造技术制作牙鲆SNP芯片(基因芯片)，总共包含48697个牙鲆SNP位点，每张芯片可同时检测24个样品。
实施例2、“鱼芯1号”基因芯片的使用方法
1、芯片检测样品的制备与杂交
采集牙鲆鱼鳍条少量(米粒大小)，利用DNA提取试剂盒(中国，天根)提取鳍条基因组DNA，利用1％琼脂糖凝胶电泳和核酸分光光度计检测DNA质量和浓度，最终合格样品标准为：电泳要求DNA出现单一条带，片段长度大于10kb，完整度好，未出现降解，样品质量DNA检测结果；采用紫外分光光度计检测A260/280：1.8-2.0，A260/230>1.5，浓度不低于20ng/μl，总量不小于4μg。然后根据美国Thermo Fisher 公司的SNP芯片检测样品制备标准流程操作(https://www.thermofisher.com/)进行芯片检测样品制备。1.将不低于4ug的高质量DNA模板加入2ml*96的深孔板中，加入变性剂进行常温变性，在变性10min后时终止变性，获得单链DNA；将用于扩增芯片位点的48697对引物和等温扩增酶，dNTP等加入深孔板中，将深孔板封膜，于37℃进行等温扩增；扩增24h后，将扩增产物片段化，加入等体积异丙醇，于-20℃冰箱中沉淀；沉淀24h后，采用4℃，3000g离心获得DNA产物沉淀，37℃下除去残余的异丙醇，将沉淀溶解，获得杂交液，杂交液采用5％的凝胶电泳检测结果质量，扩增产物质量检测结果，条带清晰，亮度较高；将杂交液采用控温扩增仪进行杂交，条件为95℃10min，48℃3min，之后将杂交液一直维持在48℃；将芯片块浸没至杂交液中，于48℃杂交炉中杂交24h，而后通过洗脱，连接荧光蛋白，固定荧光蛋白，杂交探针，扫描荧光信号，每个信号点为一个探针杂交结果。获得每个位点的杂交结果后，芯片扫描结果用Axiom Analysis Suite(AxAS)软件(美国Thermo Fisher公司)进行分析。
2、芯片检测和基因分型数据分析
(1)待测群体样品采集和DNA提取
选择部分进行过重测序的牙鲆个体DNA，使用所述芯片进行检测，对芯片基因分型的准确性以及使用重测序和基因芯片分型所得基因型数据进行基因组选择计算的可重复性进行检测。随后选择牙鲆育种过程中，建立家系所使用的候选亲本个体，进行基因组DNA的提取并用芯片进行检测，验证基因芯片在牙鲆基因组选择育种中的应用效果。所用个体信息见表4。
表4：用于基因芯片分型的牙鲆候选群体个体信息
Figure PCTCN2019125857-appb-000006
Figure PCTCN2019125857-appb-000007
(2)芯片检测
按照基因芯片检测标准流程，使用Affymetrix GeneTitan基因芯片处理系统完成探针杂交、染色和芯片扫描。具体操作如下：将4μg的高质量DNA模板加入2ml*96的深孔板中，加入变性剂进行变性(28℃)，在变性10min时快速加入变性终止液(反应时间不长于10min)，终止变性，获得单链DNA；将用于扩增芯片位点的48697对引物，等温扩增酶，dNTP和反应液等加入深孔板中，将深孔板封膜，于37℃进行等温扩增22-36h；优选扩增24h后，采用高温65℃处理20-30min使反应液失活，而后转移至37℃培养箱中孵育40min，加入片段化酶和反应液，将扩增产物片段化，加入与已有反应液等体积的异丙醇，将反应液混匀直至反应液澄清，而后于-20℃冰箱中沉淀产物；沉淀24h后，采用4℃，3,000g离心40-60min获得DNA产物沉淀，弃掉上清，保留沉淀，于37℃下完全除去残余的异丙醇后，将沉淀溶解，获得杂交液；将杂交液采用控温扩增仪进行杂交，条件为95℃10min，48℃3min，之后将杂交液一直维持在48℃；将芯片块浸没至杂交液中，于48℃杂交炉中杂交24h；而后通过洗脱，连接荧光蛋白，固定荧光蛋白，杂交探针，扫描荧光信号等，获得每个位点的杂交结果，芯片扫描结果用Axiom Analysis Suite(AxAS)软件(美国Thermo Fisher公司)进行分析。
(3)数据分析
利用AxAS软件(美国Thermo Fisher公司)分析芯片扫描结果，得到每个样品的基因分型结果。分析结果表明，芯片平均分型率为98.77 ％，分型效果良好；其中，高质量SNP比例为74.61％，各样品均能产生高质量的分型信息。
实施例3“鱼芯1号”基因芯片在牙鲆抗病育种中的应用
1、“鱼芯1号”基因芯片分型效果验证
从参考群体中挑选部分个体用于验证基因芯片分型的可靠性，这些选中的个体既有重测序的基因型，也有使用“鱼芯1号”基因芯片分型得到的基因型。从发明人已有的牙鲆参考群体中挑选部分个体应用基因芯片进行分型，统计。通过统计使用芯片得到的基因型(0/1/2表示AA/Aa/aa)和重测序得到的基因型的一致性以及利用重测序和芯片数据估算出的GEBV的相关系数来评估芯片分型的效果。若分型结果一致性达88％以上且GEBV之间的相关系数达0.9以上，则认为芯片具有好的分型效果。
分析结果表明，利用鱼芯1号”基因芯片分型得到的位点信息有90.08％与重测序相同，2组GEBV之间的相关系数为0.958。因此，本发明所研制的牙鲆基因芯片分型效果与重测序基本一致，能够对牙鲆进行准确的基因分型。
具体操作方法如下：
利用PLINK软件读取芯片数据，在服务器中输入以下指令对上述数据进行处理：
plink--vcf op2-1.vcf--make-bed--out op_Val_1
plink--vcf cs2-2.vcf--make-bed--out op_Val_2
plink--vcf op2-3.vcf--make-bed--out op_Val_3
plink--vcf op2-4.vcf--make-bed--out op_Val_4
经读取，4份vcf文件中信息如表5：
表5：用于验证基因芯片分型效果的个体数和标记数
Figure PCTCN2019125857-appb-000008
a)在R中重新命名SNP并提取4份文件中共有的标记信息，命令如下：
Figure PCTCN2019125857-appb-000009
Figure PCTCN2019125857-appb-000010
b)利用PLINK软件合并4份文件，并保留共有的标记，命令如下：
Figure PCTCN2019125857-appb-000011
c)利用PLINK软件从参考群体中提取出相同的个体和位点，将上述4份文件中.fam的信息整理至一个文件中并命名为“op_chip_indi.txt”，“…”表示文件目录，命令如下：
plink--bfile…/Val_ref--keep op_chip_indi.txt--extract common_snps.txt--recode A--out  op_rseq
经处理，上述4个文件共有的标记数为11,719，可在参考群体中找到的个体数为95。
d)利用R统计芯片和重测序分型的一致性，方法如下：
Figure PCTCN2019125857-appb-000012
经统计，上述95个个体共有1,113,305个标记，完全相同的标记数为1,002,829。因此，芯片和重测序分型结果有90.08％完全一致。
e)为保证GEBV估算的准确性，利用PLINK软件提取参考群体中剩余个体的基因型，命令如下：
plink--bfile…/Val_ref--remove op_chip_indi.txt--extract common_snps.txt--recode A--out ref
f)利用R合并参考群体和验证个体的基因型，方法如下：
Figure PCTCN2019125857-appb-000013
g)利用g)中得到的2个xxx.csv文件在R中使用加权GBLUP方法估算GEBV。具体操作方法如下(Linux环境)：
Figure PCTCN2019125857-appb-000014
Figure PCTCN2019125857-appb-000015
Figure PCTCN2019125857-appb-000016
Figure PCTCN2019125857-appb-000017
Figure PCTCN2019125857-appb-000018
Figure PCTCN2019125857-appb-000019
Figure PCTCN2019125857-appb-000020
Figure PCTCN2019125857-appb-000021
Figure PCTCN2019125857-appb-000022
Figure PCTCN2019125857-appb-000023
经计算，迭代至第4次时加权GBLUP方法趋于稳定，因此此时的迭代结果进行后续的研究。用于验证的95个个体GEBV之间的相关系数为0.958，这些个体GEBV如表6所示：
表6：验证个体使用“鱼芯1号”基因芯片和重测序分型估算的GEBV值比较
Figure PCTCN2019125857-appb-000024
Figure PCTCN2019125857-appb-000025
Figure PCTCN2019125857-appb-000026
Figure PCTCN2019125857-appb-000027
2、“鱼芯1号”基因芯片位点在参考群体中的验证
从发明人已有的参考群体中提取出“鱼芯1号”基因芯片的设计位点，利用这些位点信息实施加权GBLUP，并采用5倍交叉验证和随机分组作为评价加权GBLUP预测准确性的方法，将受试者操作特征曲线下面积(AUC)作为评估加权GBLUP方法预测准确性的指标。分析模型使用广义线性混合模型。为了减小分组的随机误差，对整个数据集进行10次分组，每组计算5次。因此，一共计算50次，将50次AUC的均值作为最终的评价结果。
分析结果表明，在牙鲆参考群体中使用与SNP芯片相同的标记实施基因组选择，AUC(准确性)值为0.885，高于传统BLUP方法的AUC(0.579)值。因此，使用发明人所设计的芯片位点能够顺利并高效地实施基因组选择。
具体操作方法如下：
利用fcGENE、BEAGLE和PLINK软件处理从牙鲆参考群体中提取 的芯片设计位点：填充缺失位点并输出基因型文件，命令如下：
Figure PCTCN2019125857-appb-000028
a)在R中利用a)中得到的基因型文件在R中实施加权GBLUP。加权GBLUP具体方法参照1)中h)部分进行。
b)将构建好的加权G矩阵带入ASReml-R中进行交叉验证。交叉验证前需要进行分组：在R中使用函数sample(1:931,931)对所有个体进行随机排序，再将排序后的数字分为5列，每列包含的元素个数分别为186、186、186、186和187；重复上述过程10次，共得到10个文件；将这10个文件放入同一文件夹备用。分析采 用广义线性混合模型，将不同实验批次和个体日龄作为固定效应，每个个体作为随机效应进行拟合。5倍交叉验证具体实施方法如下：
Figure PCTCN2019125857-appb-000029
Figure PCTCN2019125857-appb-000030
执行完上述代码后，加权GBLUP方法的AUC均值为0.885，传统BLUP方法的AUC均值为0.579。50次交叉验证结果见表7：
表7：加权GBLUP和传统BLUP方法交叉验证结果
Figure PCTCN2019125857-appb-000031
Figure PCTCN2019125857-appb-000032
3、“鱼芯1号”基因芯片在牙鲆抗病育种中的应用
为估算候选个体的基因组估计育种值(GEBV)，首先将候选个体基因型(由“鱼芯1号”基因芯片分型得到)与发明人已有的参考群体基因型合并，再使用R构建加权G矩阵，最后将准备好的加权G矩阵和表型数据带入ASReml-R、使用加权GBLUP方法估算各家系亲本的GEBV，再将亲本GEBV的均值作为相应家系的GEBV。将各家系按照感染存活率分为高存活率家系(存活率高于55％)和低存活率家系(存活率低于55％)，并计算各家系GEBV和感染存活率之间的AUC值，再将该AUC值与2、中得到的AUC进行比较，若接近甚至高于2、中得到的AUC，则说明发明人所设计的基因芯片满足基因组选择技术的要求并且能够在牙鲆抗病选育中具有良好的应用效果。计算各家系GEBV和感染存活率之间的AUC值并将该AUC值与2、中得到的AUC进行比较，以验证发明人所设计的基因芯片和基因组选择技术在牙鲆抗病选 育中的实际应用效果。估算AUC值之前，需将子代的感染存活率用exp(x)/(1+exp(x))进行转换。转换后，将高于均值的家系的存活率记为1，低于均值的记为0。为满足基因芯片方法对标记的要求，不对所有候选个体的分型结果进行合并，故需要将每张芯片的分型结果单独与参考群体基因型合并。
分析结果表明16个候选群的子代家系中的6个高存活率家系的平均存活率为62.4％和10个低存活率家系的平均存活率为33.47％(表8)；其中，高存活率家系亲本平均GEBV为2.10，低存活率家系亲本平均GEBV为1.34（表8）。计算表明，利用这些牙鲆家系的GEBV值对其感染存活率预测的准确性可达0.794，接近理论值。因此，发明人所设计的基因芯片能够很好的应用于牙鲆抗病性状的选育。
具体操作方法如下：
利用PLINK和R处理候选个体分型文件，再从候选个体中挑出用于后续验证的个体，将这些个体信息存于一个文本文件中，按照家系编号、个体编号、父本编号、母本编号、性别和表型值的顺序准备文本文件，每行一个个体，每个个体的各项信息用table分隔符进行分隔。具体实施方法为：
Figure PCTCN2019125857-appb-000033
Figure PCTCN2019125857-appb-000034
Figure PCTCN2019125857-appb-000035
a)利用PLINK和R将参考群体基因型分别与每张芯片候选个体基因型进行合并，具体方法为：
Figure PCTCN2019125857-appb-000036
Figure PCTCN2019125857-appb-000037
b)利用b)中处理好的4个基因型文件，在R中分别构建加权G矩阵，加权G矩阵构建方法同1)中所述
c)使用ASReml-R估算候选个体的GEBV，代码如下：
Figure PCTCN2019125857-appb-000038
Figure PCTCN2019125857-appb-000039
d)根据估算的所有候选个体的GEBV计算相应家系GEBV，并使用公式exp(x)/(1+exp(x))对各家系的感染存活率进行转化，将转换后高于均值的家系的存活率设为1，低于均值的设为0。最后，计算各家系GEBV和转换后存活率之间的AUC值，AUC值计算方法如下：
Figure PCTCN2019125857-appb-000040
经计算，获得16个牙鲆家系的GEBV，计算表明各家系GEBV与相应感染存活率之间的AUC(准确性)为0.794。各家系GEBV及感染存活率见表8。
表8：牙鲆家系GEBV及感染存活率
Figure PCTCN2019125857-appb-000041
将候选群的16个子代家系按照感染存活率分为高存活率家系6个(平均存活率62.4％)和低存活率家系10个(平均存活率33.47％)两大类(表8)，比较高存活率和低存活率家系亲本的GEBV，发现高存活率家系亲本平均GEBV为2.10，低存活率家系亲本平均GEBV为1.34(表8)。计算表明，使用这些牙鲆家系的GEBV值对其感染存活率预测的准确性可达0.794，接近理论值。因此，发明人所设计的基因芯片能够很好的应用于牙鲆抗病良种选育中。
上述结果表明，采用“鱼芯1号”基因芯片对牙鲆候选群体个体进行基因分型，采用加权GBLUP计算基因组育种值(GEBV)，根据GEBV数值大小进行牙鲆抗病亲鱼的筛选，采用这些亲鱼培育出的后代苗种的抗感染存活率明显提高，从而表明“鱼芯1号”基因芯片可以在牙鲆抗病良种培育中进行推广应用。
工业实用性
本发明所提供的基于与牙鲆抗病性相关的SNP位点的基因芯片可用于牙鲆抗病个体选育，并且实际的选择准确性与理论值接近，因此可以提高牙鲆抗病良种选择的准确性、缩短育种周期，为牙鲆抗病良种选育提供了基因芯片技术，为鱼类抗病良种选育开辟了基因芯片育种新途径。

Claims (7)

  1. 一种与牙鲆抗病性相关的SNP位点，其特征在于，所述的SNP位点是序列为SEQ NO:1—SEQ ID NO:48697中任一个序列的第36位碱基。
  2. 权利要求1所述的与牙鲆抗病性相关的SNP位点在制备牙鲆抗病品种选育中的应用。
  3. 权利要求1所述的与牙鲆抗病性相关的SNP位点在制备牙鲆抗病良种选育用的检测制品中的应用。
  4. 如权利要求3所述的应用，其特征在于，所述的检测制品为基因芯片。
  5. 一种用于牙鲆抗病良种选育的基因芯片，其特征在于，所述的基因芯片能够检测权利要求1所述的与牙鲆抗病性相关的SNP位点。
  6. 一种牙鲆抗病个体的筛选方法，其特征在于，所述的方法是使用权利要求3所述的基因芯片来进行检测的。
  7. 如权利要求6所述的方法，其特征在于，所述的方法包括如下的步骤：
    1)提取候选群体中的个体基因组DNA，并利用上述基因芯片检测并获得SNP标记的分型结果；
    2)从参考群体的SNP集合中提取出与基因芯片相同的SNP位点的分型结果，再将参考群体的SNP分型结果和候选群体利用芯片获得的分型结果合并；
    3)利用合并的基因型和参考群体表型，使用加权GBLUP方法估算候选群体的估计育种值GEBV，再根据GEBV值确定待检测个体的抗病潜力；
    利用参考群体基因型，使用加权最佳线性无偏估计方法估算预测准确性；其中，5倍交叉验证为预测准确性评判方法，特征曲线下面积AUC为评判预测准确性指标；AUC越接近1，预测准确性越高。
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