JP2022518304A - Flounder disease-resistant breeding gene chip and its applications - Google Patents

Abstract

The present invention provides a gene chip used for selecting excellent disease-resistant varieties of flatfish, solves the problem of lack of gene chips in breeding excellent fish varieties, complements the shortcomings of conventional breeding techniques, and makes fish disease-resistant. The purpose is to provide a new molecular breeding method, realize a generational change in fish breeding technology, and promote the rapid development of the fish breeding industry in order to cultivate excellent excellent varieties that have both sex and high productivity. .. The gene chip of SNP locus related to flounder disease resistance provided by the present invention can be used for selection of flounder disease resistant individuals, and the actual selection accuracy is close to the theoretical value. By increasing sex and shortening the breeding period, it is possible to provide gene chip technology for the selection of excellent flounder disease resistant varieties and open up a new path for gene chip breeding for the selection of excellent fish disease resistant varieties. can.

Description

The present invention belongs to the technical field of fishery genetic breeding, and specifically relates to a method and application for producing a gene chip used for selection of excellent flounder disease-resistant varieties.

Aquaculture is an important source of Chinese food, and fish farming is also a key industry of fish farming. In 2015, fish farming produced 284.57 million tons, 57.6 of the total fish farming production. %. Farmed fish have already become an important source of Chinese protein.

However, with the rapid development of the fish farming industry, there is a shortage of good varieties and the quality of the farmed varieties deteriorates. The scale of aquaculture has expanded, the level of aquaculture has improved, and the aquaculture environment has deteriorated, causing frequent outbreaks of aquaculture diseases. do. For fish only, the immunosuppression formed by high-density farming causes a decrease in disease resistance of farmed fish. Since research on the immune disease resistance mechanism of fish and the molecular genetics of disease resistance has not progressed yet, it is difficult to submit a preventive plan for fish diseases at the molecular level. In addition, disease-resistant functional genes and disease-resistant molecular markers are deficient, making it difficult to breed excellent disease-resistant varieties, and therefore aquaculture production currently depends only on wild or artificially propagated multi-generational seedlings with reduced disease resistance and is endemic. The disease causes frequent outbreaks in fish farming. According to incomplete statistics, the direct economic loss due to disease in the fish farming industry in China reaches RMB 10 billion every year. Diseases have already become a bottleneck that constrains the sustainable development of fish farming in China.

Flounder is a world-class seawater-cultured fish and is one of the leading fish in seawater-cultured fish in China. However, there is a problem that diseases frequently occur in the flatfish farming industry and the mortality rate is high. Major diseases that threaten farmed fish such as flatfish include bacterial and viral diseases. Among them, the most harmful diseases are edwardsiellosis, vibrio disease and lymphocystis disease, respectively. Although disease preventive measures such as antibiotic drugs or vaccines have certain effects, they cannot fundamentally solve the disease problems in aquaculture. In addition, antibiotic-based drugs tend to accumulate in the body of fish, degrading the quality of farmed fish products, causing potential harm to consumer health, and causing serious drug resistance to pathogens and seriously contaminating the farming environment. Its propensity to cause is increasingly constraining its application in the aquaculture industry. At the same time, the use of antibiotics also fails to meet the ever-growing demand for non-residual, pollution-free marine products. Therefore, selection of excellent fish disease-resistant varieties is one of the urgent issues to be solved in the fishery field of China.

Until now, the selection of excellent fish varieties has been calculated mainly based on phenotypic properties, including group selection, family line selection, crossbreeding selection, BLUP selection, etc., and mainly based on phenotypic values that are easy to measure such as body length and weight. Make selections based on the breeding values that are given. After the molecular marker is created, the selection is made economically by identifying the molecular marker related to the important economic property, and the number of molecular markers used in the conventional molecular marker auxiliary selection is very limited, and a single gene is used. The selection effect of the property or quality property is excellent, but the selection effect on the quantity property in which the multiplex gene is determined is not good. Disease resistance is a quantitative property in which multiple genes are controlled, it is difficult to measure directly, and selection accuracy is very low. Therefore, selection for excellent disease resistance varieties has been sluggish for a long time, limiting the breeding of new fish disease resistance varieties. However, there is an urgent need to solve this problem with new breeding technologies.

Gene chips, also called DNA chips and DNA microarrays, use photoetching technology to synthesize a large amount of DNA sequences that have undergone selective optimization into oligonucleotides using a silicon wafer as a solid phase carrier, and attach them to slides that have undergone special treatment. After undergoing denaturation and fixation, a DNA microarray is formed. Based on the nucleic acid molecular crossing technique, the gene chip can simultaneously perform parallel crossing and analysis on tens of thousands or even hundreds of thousands of DNA fragments, and has the advantages of high throughput, concurrency, high efficiency, and a small number of samples. Currently, gene chips are already widely applied to the diagnosis of human diseases and tumors, genetic analysis of animals and plants, and genetic breeding. In animal breeding, gene chips have already been successfully applied to livestock farming, especially to select excellent breeds such as dairy cows and pigs. For example, in dairy cows, many gene chips such as Bovine3Kchip, Bovine25K, SNPchip, BovineHD700K, and BovinLD7K have already been developed one after another. Until now, North America, Europe, Australia, etc. have used the Bovine SNP50Bedchip chip (54KSNP) as a general-purpose platform for genomic SNP marker classification and inspection, and based on the SNP classification results acquired by a large-scale reference group, milk production, fertility, etc. Perform full genome-wide association studies of various economic properties such as disease resistance, and construct a selection system for dairy cow genomes. Through genome selection, early selection of newborn bulls is achieved, intergenerational intervals of dairy cow breeding are shortened, genetic progress is promoted, breeding bull selection efficiency is greatly improved, and aquaculture and breeding costs are significantly saved.

However, there have been no reports of breeding gene chips, especially disease-resistant breeding gene chips, in aquaculture animals.

The present invention provides a gene chip used for selecting excellent disease-resistant varieties of flatfish, solves the problem of lack of gene chips in breeding excellent fish varieties, complements the shortcomings of conventional breeding techniques, and provides fish disease resistance. The purpose is to provide a new molecular breeding method, realize a generational change in fish breeding technology, and promote the rapid development of the fish breeding industry in order to cultivate excellent excellent varieties that have both high productivity and high productivity.

The present invention first provides an SNP locus related to flounder disease resistance, and the SNP locus is the base at position 36 of any one of the sequences SEQNO: 1SEQIDNO: 48697.

The SNP locus of the present invention can be used for selecting excellent varieties of flounder disease resistance.

The SNP locus provided by the present invention can also be used in the production of a inspection product for selecting excellent varieties of flounder disease resistance.

The inspection product is preferably a gene chip.

A further aspect of the present invention provides a genetic chip used for selection of flounder disease resistant excellent varieties, which can detect SNP locus associated with flounder disease resistance.

Further aspects of the present invention provide a method for selecting flounder disease resistant individuals, which can be performed using the gene chips described above.

The method comprises the following steps:
1) The individual genomic DNA in the candidate group is extracted, and the above gene chip is used for inspection to obtain the result of genotyping of the SNP marker.
2) The result of genotyping of SNP locus similar to the gene chip was extracted from the SNP group in the reference group, and the result of genotyping of SNP in the reference group and the genotype obtained from the candidate group using the chip. Combine with the judgment result.
3) Using the combined genotype and the type expressed by the reference group, the estimated breeding value (GEBV) of the candidate group is estimated using the weighted GBLUP method, and the disease resistance of the test-tested individual is further estimated based on the GEBV value. Determine sexual potential.

The genotype of the reference group is used to estimate the prediction accuracy using the weighted best linear biased estimator (weighted GBLUP). Among them, the 5-fold cross-validation method is used as the prediction accuracy determination method, and the area under the feature curve (AUC) is used as the prediction accuracy determination index. The closer the AUC is to 1, the higher the prediction accuracy.

The gene chip of SNP locus related to flounder disease resistance provided by the present invention can be used for selection of flounder disease resistant individuals, and the actual selection accuracy is close to the theoretical value, so that the flounder disease resistance is excellent. The accuracy of selecting varieties can be improved and the breeding period can be shortened. This provided gene chip technology for the selection of excellent flounder disease-resistant varieties, and opened up a new path of breeding with gene chips for selecting fish disease-resistant varieties.

An object of the present invention is to establish a method for producing and applying a gene chip for breeding excellent disease-resistant varieties of flatfish, and to provide a new technical means for molecular breeding for breeding excellent disease-resistant varieties of fish such as flatfish.

Next, the explanation for the technical terms related to the present invention is as follows:
SNP: An abbreviation for Single Nucleotide Polymorphism, a single nucleotide polymorphism, a polymorphism in a DNA sequence caused by mutation of a single nucleotide at the genomic level.

Gene chip: A two-dimensional DNA probe array composed of tens of thousands to millions of specific DNA sequence fragments that are regularly placed and fixed on a support such as a silicon wafer or slide through microfabrication technology. Gene classification and molecular inspection can be performed on (DNA, etc.).

Nucleic acid notation: Based on the degenerate property of a codon, one symbol is often used to substitute two or more unspecified bases.

For example, R indicates A / G, Y indicates C / T, M indicates A / C, K indicates G / T, S indicates G / C, W indicates A / T, and so on.

Reference group: In the selection of the genome, the group having the phenotypic data obtained through the inspection such as anthropogenic infection is the phenotypic distribution representing the whole group selected from the large group having the normal phenotypic properties, and the genome rearrangement. Is a set of individuals who perform the above, acquire genotype data, and perform actual genome selection calculation.

Candidate group: In the selection of the genome, the candidate group is a group in which genotype data is acquired through genome rearrangement but no phenotypic data, and the group has breeding potential and is a subsequent excellent cultivar. It is a set of individuals that are planned to be used for breeding work.

GBLUP: An abbreviation for GenomicBestLinearUnbiasedPrescription, that is, a method for estimating a genomic breeding value by using a genetic relationship (G matrix) between individuals using a high-density molecular marker in the genome with a genomic best linear biased estimator.

GEBV: An abbreviation for Genomic Estimated Breeding Values, ie, the genomic estimated breeding value, obtained by adding up the effect estimates of all markers or haplotypes in the full genome.

Next, a detailed description will be given to the present invention by combining examples.

Example 1: Selection of "fish chip No. 1" gene chip SNP locus and chip production 1. Creation of a reference group for flounder-resistant Edwardie latalda and measurement of phenotypic properties Individuals in the reference group and candidate group from which the flounder genome is selected Are all derived from the flounder family created by this task team since 2003, and gradually derived from the excellent properties such as rapid growth, disease resistance and reversal resistance of the flounder groups in Korea, Japan and China during the many years of breeding process. Will be done.

In particular, from 2013, we will carry out research on the selection of flounder-resistant Edwardie latarda families in response to the increasingly serious situation in the flounder farming industry.

In 2013-2015, the flounder family created in the same year was continuously infused with peritoneal infection, and Edwardie eratalda was continuously examined. , 2013, 2014 and 2015, samples of 4757, 5942 and 6919 were collected and used to select and create a reference group from which the flounder Edwardia talda genome is selected.

From the infection detection sample, 96 families (32 in 2013, 10 in 2014, 48 in 2015) were selected, and each family selected equal proportions of mortality and surviving individuals 10-15 according to the mortality rate. , A reference group in which the genome is selected is formed, and the result of infection detection (death or survival) of the selected individual is used as the phenotypic property of the reference group (Table 2).

2. Flounder full genome rearrangement and SNP locus evaluation The flounder reference group has a total of 931 individuals that can be used after DNA extraction and inspection (Table 3).

After extracting the genomic DNA of the individual of the reference group of 931 and passing the DNA inspection, the next-generation sequence library is created, and the library creation type is the double-ended DNA library (insertion fragment 350 bp), which is sequenced using the IlluminaHiseqX10 sequence platform. And the data export is completed, and the average amount of data acquired by quality control is 2G / individual. Using the Hirame genome sequence (GenBankID: PRJNA73673) provided by this task team as a reference genome, sequence comparison was performed using BWA (http://bio-bwa.sourceforge.net/) software, and then Samtools (http: /). /Www.https.org/) SNP prediction and evaluation are performed using software, and an SNP set of 42.2M is obtained.

3. SNP locus evaluation and selection (1) The 42.2M flatfish SNP marker acquired in step 2 is selected according to the following standards:
Sites with miss rate> 0.1, minimum allele frequency (MAF) <0.05 were removed, sites in repeats or repeats were removed, sites that did not fit the hadwin balance were removed, and 3.4 M. Obtain a flatfish SNP marker.

モデルにおいて、ｙは表現型値で、ｕはグループ平均値で、グループｑｉはマーカー効果が正規分布ｑｉ～Ｎ（０、

）で、ｍはマーカーの総数で、Ｘはｑｉに対応する関連マトリックスで、ｅは残差である。 (2) For the 3.4M molecular markers selected in step (1), SNP locus effect value analysis and estimated breeding value calculation were performed.
The flatfish genome selection calculation uses the BayesCπ algorithm, and the analysis model equation:

In the model, y is a phenotypic value, u is a group mean value, and group qi has a normal distribution of marker effects qi to N (0,

), M is the total number of markers, X is the related matrix corresponding to qi, and e is the residual.

Combined genotype data genotype. Using the BayesCπ algorithm provided by the R language pack. csv and phenotypic data phonetype. Combining csv, genome selection calculation is performed on a total of 931 flatfish individuals in the reference group for full genome rearrangement. After that, the acquired SNP locus effect values are rearranged from the maximum to the minimum, sites with estimated breeding values <10-5 are deleted, and a total of 864229 SNP locus are acquired and used for selection of gene chip SNP locus.

(3) Further, probe design and evaluation are performed on each SNP selected in step (2) using the AffymetrixAxiom gene classification probe design biological analysis process, and sites having a probe conversion possibility evaluation score <0.6 are deleted. In addition, the SNP covers the entire genome and is evenly distributed, there are no other SNPs in the SNP flanking sequence 35bp, and the GC content of the SNP flanking sequence 35bp is 30-70%, which is the final. The 48697 SNP molecular markers are selected and guaranteed to be used in chip production, and the sequence record of the 48697 SNP molecular markers is in the sequence list.
A flatfish SNP chip (gene chip) is produced using the production technology of the AffymetrixAxiom chip manufactured by Thermo Fisher, USA, and contains a total of 48697 flatfish SNP locus, and each chip can simultaneously inspect 24 samples.

Example 2, How to use the "fish chip No. 1" gene chip 1. Production and crossing of a sample for chip inspection A small amount of fin spines (about the size of a grain of rice) was collected and a DNA extraction kit (China, China). The fin spine genomic DNA was extracted using Amane), and the DNA quality and concentration were measured using 1% agarose gel electrophoresis and a nucleic acid spectrophotometer. As is: Electrophoresis requires that a single strip be formed in the DNA, the fragment length is greater than 10 kb, the completeness is excellent, no biodegradation occurs, and the sample quality DNA test results. A260 / 280 is measured using an ultraviolet spectrophotometer: 1.8-2.0, A260 / 230> 1.5, the concentration is not less than 20 ng / μl and the total amount is not less than 4 μg. After that, a sample for chip inspection is manufactured according to a standard operation process for manufacturing an SNP chip inspection sample manufactured by Thermo Fisher, USA (https://www.thermovisher.com/). A high-quality DNA template of 1.4 ug or more is added to a 2 ml * 96 deep hole plate, denaturation is performed by adding a denaturant, and denaturation is stopped after 10 minutes of denaturation to obtain single-stranded DNA. A 48697 pair primer for chip site amplification and an isothermal amplification enzyme, dNTP, etc. are added to the deep hole plate, the deep hole plate is sealed, and isothermal amplification is performed at 37 ° C. After amplification for 24 hours, the amplified product is fragmented, equal volumes of isopropanol are added and precipitated in a refrigerator at −20 ° C. After precipitating for 24 hours, centrifuge at 4 ° C. and 3000 g to obtain a precipitate of DNA product, remove the remaining isopropanol at 37 ° C., dissolve the precipitate, obtain a hybrid solution, and the hybrid solution is 5%. Using the mass of the gel electrophoresis test result, the amplification product quality test result shows that the strip is clear and the brightness is high. The crossed liquid is crossed using a temperature control amplification device, and the conditions are 95 ° C. for 10 min and 48 ° C. for 3 min, and then the crossed liquid is continuously maintained at 48 ° C. By immersing the chip block in a crossing solution and crossing it in a crossing furnace at 48 ° C. for 24 hours, then elution, connecting the fluorescent protein, fixing the fluorescent protein, and scanning the crossing probe and fluorescent signal, each signal point becomes one. After acquiring the crossing results of each site from the results of the two probe crosses, the results of the chip scan are analyzed using the AxiomAnalysisSuit (AxAS) software (manufactured by Thermo Fisher, USA).

2. Chip inspection and gene classification data analysis (1) Collection of test group sample and DNA extraction Select some rearranged individual fluffy DNA, perform inspection using the chip, and classify the chip gene. The accuracy and reproducibility of performing genome selection calculations on the genotype data obtained using the rearrangement and gene chip classification will be checked. After that, in the selection process of flatfish breeding, an individual of a candidate adult whose family line is used is established, genomic DNA is extracted and inspection is performed with a chip, and the applied effect of the gene chip in flatfish genome selective breeding is verified. Information on the adopted individuals is shown in Table 4.

(2) Chip inspection According to the standard process of gene chip inspection, probe crossing / staining and chip scanning are completed using the Affymetrix GeneTitan gene chip processing system. The specific operation method is as follows: Add 4 μg of high-quality DNA template to a 2 ml * 96 deep hole plate, add a denaturing agent to perform denaturation (28 ° C.), and after 10 minutes of denaturation, a denaturation stop solution ( (Reaction time is not longer than 10 min) Stop denaturation and obtain single-stranded DNA. A 48697 pair primer for chip site amplification, an isothermal amplification enzyme, dNTP, a reaction solution, etc. are added to the deep hole plate, the deep hole plate is sealed, and isothermal amplification is performed at 37 ° C. for 22-36 hours. Preferably, after amplification for 24 hours, the reaction solution is inactivated at a high temperature of 65 ° C. for 20-30 min, then transferred to a culture box at 37 ° C. and cultured for 40 minutes, and the fragmentation enzyme and the reaction solution [41} are added to the fragment. The chemical amplification product is fragmented, the same volume of isopropanol as the existing reaction solution is added, the reaction solution is uniformly mixed until the reaction solution is clear, and then the product is precipitated in a refrigerator at −20 ° C. After precipitation for 24 hours, centrifuge at 4 ° C. at 3,000 g for 40-60 min to obtain a DNA product precipitate, remove the supernatant, retain the precipitate, and completely remove the remaining isopropanol at 37 ° C. , Dissolve the precipitate and obtain a hybrid. The crossed liquid is crossed using a temperature control amplification device, and the conditions are 95 ° C. for 10 min and 48 ° C. for 3 min, and then the crossed liquid is continuously maintained at 48 ° C. The chip block is immersed in a crossing solution and crossed in a crossing furnace at 48 ° C. for 24 hours. After that, by elution, connecting the fluorescent protein, fixing the fluorescent protein, scanning the crossing probe and the fluorescent signal, the crossing result of each site is obtained, and the chip scan result is the Axiom Analysis Suite (AxAS) software (manufactured by Thermo Fisher, USA). Is used for analysis.

(3) Data analysis The result of chip scan is analyzed using AxAS software (manufactured by Thermo Fisher, USA), and the gene classification result of each sample is obtained. According to the analysis results, the average classification rate of chips is 98.77%, and the classification effect is outstanding. Among them, the high-quality SNP ratio is 74.61%, and each sample can generate high-quality classification information.

Example 3 Application of "Fish Chip No. 1" Gene Chip to Disease-Resistant Breeding 1. Verification of Effect of "Fish Chip No. 1" Gene Chip Classification Select some individuals from the reference group to improve the reliability of gene chip classification. Used for validation, these selected individuals are also genotypes of rearrangement and genotypes obtained using the "Fish Chip No. 1" gene chip classification. Some individual gene chips are selected from the inventor's existing flounder reference group for classification and statistics. Consistency between genotypes obtained using chips (0/1/2 indicates AA / Aa / aa) and genotypes obtained by rearrangement, and the relationship between rearrangements and GEBV estimated using chip data Evaluate the effect of chip classification by statistic of coefficients. If the consistency of the classification results reaches 88% or higher and the correlation coefficient between GEBV reaches 0.9 or higher, the chip is considered to have excellent classification results.

According to the analysis results, 90.08% of the site information obtained using the "Fish Chip No. 1" gene chip classification is similar to the rearrangement, and the correlation coefficient between the two sets of GEBV is 0.958. Therefore, the classification result of the flounder gene chip for which the present invention is developed basically matches the rearrangement, and accurate gene classification can be performed on the flounder.

The specific operation method is as follows:
Read the chip data using the LINK software and enter the following command on the server to process the above data:
pink －－vcf op2-1. vcf －－make-bed －－out op_Val_1
pink －－vcf cs2-2. vcf －－make-bed －－out op_Val_2
pink －－vcf op2-3. vcf －－make-bed －－out op_Val_3
pink －－vcf op2-4. vcf －－make-bed －－out op_Val_4

After reading, the information in the four vcfs is according to Table 5.

a) Rename the SNP in R and extract the common marker information in the four files, the command is:
# Unload the required R package library (data.table)
# Read the site information of cs_Val_1 and cs_Val_1 val_1 <-fread (“op_Val_1.bim”, header = F)
val_1 <-fread ("op_Val_2.bim", header = F)
val_3 <-fread (“op_Val_3.bim”, header = F)
val_4 <-fread ("op_Val_4.bim", header = F)
# Outputs a file with a unified SNP naming method and renamed. sep = “:”)
val_2 $ V2 <-paste (paste (rep ("rs", now (val_2)), val_2 $ V1, sep = ""), val_2 $ V4, sep = ":")
val_3 $ V2 <-paste (paste (rep ("rs", now (val_3)), val_3 $ V1, sep = ""), val_3 $ V4, sep = ":")
val_4 $ V2 <-paste (paste (rep ("rs", now (val_4)), val_4 $ V1, sep = ""), val_4 $ V4, sep = ":")
write. table (val_11, "op_Val_1.bim", sep = "\ t", col.names = F, low.names = F, quote = F)
write. table (val_2, "op_Val_2.bim", sep = "\ t", col.names = F, low.names = F, quote = F)
write. table (val_3, "op_Val_3.bim", sep = "\ t", col.names = F, low.names = F, quote = F)
write. table (val_4, "op_Val_4.bim", sep = "¥ t", col.names = F, low.names = F, quote = F)
# Extract and output common site information com <-Reduction (intersect, list (a = val_1 $ V2, b = val_2 $ V2, c = val_3 $ V2, d = val_4 $ V2))
write. table (com, “common_sns.txt”, sep = “¥ t”, coll.names = F, row.names = F, quote = F)

b) Combine the four files using the PLLINK software and reserve the common marker, the command is:
pink －－bfile op_Val_1 －－merge-list merge_op. pxt --- extract common_snps. pxt －－recode A －－out op_chip
The following information is stored in the file "merge_op.txt":
op_Val_2. bed op_Val_2. Bim op_Val_2. fam
op_Val_3. bed op_Val_3. Bim op_Val_3. fam
op_Val_4. bed op_Val_4. Bim op_Val_4. fam

c) Extract similar individuals and sites from the reference group using the PLLINK software, and in the above four files. Organize the fam information into a file and name it "op_chip_indi.txt", where "..." represents the file catalog and the command is:
pink --- bfile ... / Val_ref --- keep op_chip_indi. pxt --- extract common_snps. pxt －－recode A －－out op_rseq
After the processing, the number of markers common to the above four files is 11,719, and the number of individuals that can be searched in the reference group is 95.

d) Utilizing the match between the R statistical chip and the rearrangement classification, the method is as follows:
# Unload the required R package library (data.table)
# Chip <-fread (“op_chip.raw”) that reads the classification information acquired by the chip and rearrangement, respectively.
rseq <-fread ("op_rseq.raw")
# Unify individual sequences in files fid <-data. frame (rseq $ FID)
colnames (fid) <-"FID"
chip <-data. table (merge (fid, chip, sort = F))
# Delete the first 6 columns in the file and output the genotype chip [, c (1: 6): = NULL]
rseq [, c (1: 6): = NULL]
file (chip, “geno_op_chip.csv”, sep = “,”, low.names = F, quote = F)
fwrite (rseq, “geno_op_rseq.csv”, sep = “,”, low.names = F, quote = F)
# Statistical consistency sum (chip == rseq) / (nlow (chip) * ncol (chip)) * 100
According to statistics, it was found that the 95 individuals had a total of 1,113,305 markers, and the number of markers that were completely similar was 1,002,829. Therefore, the results of chip and rearrangement classification are 90.08% in perfect agreement.

e) To ensure the accuracy of the GEBV estimation, genotypes of the remaining individuals in the reference group were extracted using the PLLINK software, and the commands are:
pink －－bfile… / Val_ref －－remove op_chip_indi. pxt --- extract common_snps. pxt －－recode A －－out ref

f) Use R to merge reference groups and verify genotypes of individuals, the method of which is:
# Unload the required R package library (data.table)
# Read the classification information acquired by the chip and rearrangement, respectively. Chip <-as. matrix (fread (“geno_op_chip.csv”))
rseq <-as. matrix (fread (“geno_op_rseq.csv”))
ref <-fread (“ref.raw”)
# Delete the first 6 columns in the file ref [, c (1: 6): = NULL]
ref <-as. matrix (ref)
# Combine genotype files and output the genotype file after the combination geno_chip <-rbind (chip, ref)
geno_rseq <-rbind (rseq, ref)
write. table (geno_chip, “geno_Val_Chip.csv”, sep = “,”, low.names = F, quote = F)
write. table (geno_rseq, “geno_Val_Rseq.csv”, sep = “,”, low.names = F, quote = F)

g) Two xxx obtained in g). The csv file estimates GEBV in R using the weighted GBLUP method. The specific operation method is as follows (Linux environment):
# Unload the required R packages and functions library (parallell)
library (data.table)
library (asreml)
library (prOC)
source (“ginv.R”)
The definition of the function ginv is as follows:
ginv <-function (invG) {
Ginv <-data. frame (low = rep (1: low (invG), low (invG)), logic = rep (1: low (invG), reach = narrow (invG)), value = as.numeric (invG), low. = As.logical (lower.tri (invG, diag = T)))
Ginv <-Ginv [Ginv $ lower. mat == T, c (“low”, “column”, “value”)]
Ginv <-Ginv [order (Ginv $ row, Ginv $ volume),]
return (Ginv)
}
# Read genotype and phenotype information geno_chip <-as. matrix (fread (“geno_Val_Chip.csv”, nThread = 10))
geno_rseq <-as. matrix (fread (“geno_Val_Rseq.csv”, nThread = 10))
pheno <-asreml. read. table ("pheno_op_Val.csv", header = T, sep = ",")
rnames <-as. matrix (fread (“pheno_op_Val.csv”)) [, 1]
M_1 <-geno_chip
M_2 <-geno_rseq
# Calculate the frequency of secondary coordinating genes at each site pi_1 <-round (colSums (M_1) / (2 * now (M_1)), 3)
pi_2 <-round (colSums (M_2) / (2 * now (M_2)), 3)
# Build a P matrix P_1 <-matrix (2 * pi_1, byrow = T, now = now (M_1), ncol = ncol (M_1))
P_2 <-matrix (2 * pi_2, byrow = T, now = now (M_2), ncol = ncol (M_2))
# Build a Z matrix Z_1 <-as. matrix (M_1-P_1)
Z_2 <-as. matrix (M_2-P_2)
# ZZt_1 to construct the term of equation numerator <-do. call ('rbind', mclapply (1: now (Z_1), FUN = faction (x) {tcrossprod (Z_1 [x,], Zt_1)}, mc.cores = 20))
ZZt_2 <-do. call ('rbind', mclapply (1: now (Z_2), FUN = faction (x) {tcrossprod (Z_2 [x,], Zt_2)}, mc.cores = 20))
# Construct the term of equation denominator denom_1 <-2 * (sum (pi_1 * (1-pi_1)))
denom_2 <-2 * (sum (pi_2 * (1-pi_2)))
# Build the G matrix G_chip <-ZZt_1 / denom_1
G_rseq <-ZZt_2 / denom_2
diag (G_chip) <-diag (G_chip) + 0.01
diag (G_rseq) <-diag (G_rseq) + 0.01
# G Matrix Inversion invG_chip0 <-solve (G_chip)
invG_rseq0 <-solve (G_rseq)
# G Matrix inversion is converted to a three-column format that ASReml can use and output Ginv_chip <-ginv (invG_chip)
Ginv_rseq <-ginv (invG_rseq)
write. table (Ginv_chip, "Ginv_op_Val_Chip_at_itter_0.csv", sep = ",", low.names = F, quote = F)
write. table (Ginv_rseq, "Ginv_op_Val_Rseq_at_ita_0.csv", sep = ",", low.names = F, quote = F)
#Calculate GEBV attr (Ginv_chip, “lowNames”) <-paste (rnames)
attr (Ginv_rseq, “lowNames”) <-paste (runames)
WGBLUP1 <-asreml (status ~ 1,
random = ~ giv (IID),
giverse = list (IID = Ginv_chip),
rkov = ~ units,
family = asreml. Binomial (link = “logit”)
data = pheno,
maximizer = 100,
na. method. X ='omit')
gebv01 <-coef (WGBLUP1) $ random
write. table (gebv01, "GEBVs_at_ita_0_chip.csv", sep = ",", low.names = F, quote = F)

WGBLUP2 <-asreml (status ~ 1,
random = ~ giv (IID),
giverse = list (IID = Ginv_rseq),
rkov = ~ units,
family = asreml. Binomial (link = “logit”),
data = pheno,
maximizer = 100,
na. method. X ='omit')
gebv02 <-coef (WGBLUP2) $ random
write. table (gebv02, "GEBVs_at_ita_0_rseq.csv", sep = ",", low.names = F, quote = F)

#GBLUP process ・ Zt1 <-t (Z_1) that estimates the effect value of the marker that weights the chip portion and executes the weighted iteration process 6 times using the for cycle.
p1 <-do. call ('rbind', mclappley (1: now (Zt1), FUN = faction (x) {Zt1 [x,]% *% invG01}, mc.cores = 5))
p2 <-p1% *% gebv01
u01 <-p2 / pp_1
Varu01 <-u01 * u01 * 2 * pi_1 * (1-pi_1)
write. table (u01, "markerEff_chip_at_itter_0.csv", sep = ",", low.names = F, quaote = F)
write. table (Varu01, "markerVar_chip_at_ita_0.csv", sep = ",", low.names = F, quote = F)
# Estimate the weight of each marker D <-rep (1, ncol (M_1))
D0 <-rep (1, ncol (M_1))
pre_D <-D0
u <-u01
cal <-6
inter <-1
for (t in 1: cal) {
d <-as. vector (u * u * 2 * pi_1 * (1-pi_1))
for (i in 1: (length (d) / inter)) {
di <-mean (d [(inter * (i-1) + 1): (inter * i)])
pre_D [(inter * (i-1) + 1): (inter * i)] <-di
}
D <-sum (D0) / sum (pre_D) * pre_D
write. table (D, file = paste ("weights_chip_at_ita_", t, ".csv", sep = ""), sep = ",", low.names=F, quote = F)
# Estimate the weighted G matrix p1 <-do. call ('cbind', mclapply (1: ncol (Z_1), FUN = faction (x) {Z_1 [, x] * D [x]}, mc.cores = 5))
p2 <-do. call ('rbind', mclapply (1: now (p1), FUN = faction (x) {p1 [x,]% *% Zt1}, mc.cores = 5))
G <-p2 / pp_1
write. table (G, file = paste ("G_chip_at_itter_", t, ".csv", sep = ""), sep = ",", row.names=F, quote = F)
diag (G) <-diag (G) + 0.01
invG <-solve (G)
write. table (invG, file = paste ("invG_chip_at_itter_", t, ".csv", sep = ""), sep = ",", row.names=F, quote = F)
Ginv <-ginv (invG)
write. table (Ginv, file = paste ("Ginv_chip_at_itter_", t, ".csv", sep = ""), sep = ",", row.names=F, quote = F)
attr (Ginv, "lowNames") <-paste (runames)
WGBLUP <-asreml (status ~ 1,
random = ~ giv (IID),
giverse = list (IID = Ginv),
rkov = ~ units,
family = asreml. Binomial (link = “logit”),
data = pheno,
maximizer = 100,
na. method. X ='omit')
gevv <-coef (WGBLUP) $ random
write. table (gebv, file = paste ("GEBVs_chip_at_ita_", t, ".csv", sep = ""), sep = ",", low.names = F, quote = F)
# Estimate the effect value of the post-weighted marker p1 <-do. call ('rbind', mclapply (1: now (Zt1), FUN = faction (x) {D [x] * Zt1 [x,]}, mc.cores = 5))
p2 <-do. call ('rbind', mclappley (1: now (p1), FUN = faction (x) {p1 [x,]% *% invG}, mc.cores = 5))
p3 <-do. call ('rbind', mclapply (1: now (p2), FUN = faction (x) {p2 [x,]% *% gebv01}, mc.cores = 5))
u <-p3 / pp_1
Varu <-u * u * 2 * pi_1 * (1-pi_1)
write. table (u, file = paste ("markerEff_chip_at_ita_", t, ".csv", sep = ""), sep = ",", low.names = F, quote = F)
write. table (Varu, file = paste ("markerVar_chip_at_itter_", t, ".csv", sep = ""), sep = ",", low.names = F, quote = F)
}
#GBLUP process-Zt2 <-t (Z_2) that weights the rearranged part and executes a weighted iteration process 6 times using a for cycle.
p1 <-do. call ('rbind', mclappley (1: now (Zt2), FUN = faction (x) {Zt2 [x,]% *% invG02}, mc.cores = 5))
p2 <-p1% *% gebv02
u02 <-p2 / pp_2
Varu02 <-u02 * u02 * 2 * pi_2 * (1-pi_2)
write. table (u02, "markerEff_rsq_at_itter_0.csv", sep = ",", low.names = F, quaote = F)
write. table (Varu02, "markerVar_rseq_at_ita_0.csv", sep = ",", low.names = F, quote = F)
# Estimate the weight of each marker D <-rep (1, ncol (M_2))
D0 <-rep (1, ncol (M_2))
pre_D <-D0
u <-u02
cal <-6
inter <-1
for (t in 1: cal) {
d <-as. vector (u * u * 2 * pi_2 * (1-pi_2))
for (i in 1: (length (d) / inter)) {
di <-mean (d [(inter * (i-1) + 1): (inter * i)])
pre_D [(inter * (i-1) + 1): (inter * i)] <-di
}
D <-sum (D0) / sum (pre_D) * pre_D
write. table (D, file = paste ("weights_rsq_at_itter_", t, ".csv", sep = ""), sep = ",", low.names=F, quote = F)
# Estimate the weighted G matrix p1 <-do. call ('cbind', mclapply (1: ncol (Z_2), FUN = faction (x) {Z_2 [, x] * D [x]}, mc.cores = 5))
p2 <-do. call ('rbind', mclapply (1: now (p1), FUN = faction (x) {p1 [x,]% *% Zt2}, mc.cores = 5))
G <-p2 / pp_2
write. table (G, file = paste ("G_rseq_at_itter_", t, ".csv", sep = ""), sep = ",", row.names=F, quote = F)
diag (G) <-diag (G) + 0.01
invG <-solve (G)
write. table (invG, file = paste (“invG_rseq_at_itter_”, t, “.csv”, sep = “”), sep = “,”, low.names = F, quote = F)
Ginv <-ginv (invG)
write. table (Ginv, file = paste (“Ginv_rsq_at_itter_”, t, “.csv”, sep = “”), sep = “,”, low.names = F, quote = F)
attr (Ginv, "lowNames") <-paste (runames)
WGBLUP <-asreml (status ~ 1,
random = ~ giv (IID),
giverse = list (IID = Ginv),
rkov = ~ units,
family = asreml. Binomial (link = "logit"),
data = pheno,
maximizer = 100,
na. method. X ='omit')
gevv <-coef (WGBLUP) $ random
write. table (gebv, file = paste ("GEBVs_rseq_at_ita_", t, ".csv", sep = ""), sep = ",", low.names = F, quote = F)
# Estimate the effect value of the post-weighted marker p1 <-do. call ('rbind', mclapply (1: now (Zt2), FUN = faction (x) {D [x] * Zt2 [x,]}, mc.cores = 5))
p2 <-do. call ('rbind', mclappley (1: now (p1), FUN = faction (x) {p1 [x,]% *% invG}, mc.cores = 5))
p3 <-do. call ('rbind', mclapply (1: now (p2), FUN = faction (x) {p2 [x,]% *% gebv02}, mc.cores = 5))
u <-p3 / pp_2
Varu <-u * u * 2 * pi_2 * (1-pi_2)
write. table (u, file = paste ("markerEff_rsq_at_ita_", t, ".csv", sep = ""), sep = ",", low.names = F, quote = F)
write. table (Varu, file = paste ("markerVar_rseq_at_ita_", t, ".csv", sep = ""), sep = ",", low.names = F, quote = F)
}
After calculation, the weighted GBLUP method approaches stability at the 4th iteration, so subsequent studies will be conducted on the results of the iterations at this time. The correlation coefficient among the 95 individual GEBVs used for validation was 0.958, and these individuals had GEBVs according to Table 6.

2. Verification of "Fish Chip No. 1" gene chip site in the reference group Extract the design site of "Fish Chip No. 1" gene chip from the existing reference group of the inventor, and use these site information to perform weighted GBLUP. As a method for evaluating the weighted GBLUP prediction accuracy by using random grouping of the cross-validation method of 5 times, the area under the operating characteristic curve (AUC) of the subject is evaluated by the weighted GBLUP evaluation method. It is used as an index of. A generalized linear mixed model is used as the analytical model. In order to reduce the random error of grouping, the data set is grouped 10 times, and each group is calculated 5 times. Therefore, a total of 50 times are calculated, and the average value of 50 times of AUC is used as the final evaluation result.

According to the analysis results, genome selection was performed using markers similar to SNP chips in the flatfish reference group, and the AUC (accuracy) value was 0.885, which was higher than the AUC (0.579) value by the conventional BLUP method. high. Therefore, the genome selection can be carried out smoothly and efficiently by using the chip site designed by the inventor.

The specific operation method is as follows:
Chip design site extracted from flounder reference group using fcGENE, BEAGLE and PLLINK software: Filling the defective site and outputting the genotype file, the command is:
fcgene --- ped geno_op_Rseq_chip. ped －－map geno_op_Rseq_chip. map --- offormat beagle --- out pink2beagle

Java-Xmx5120m-jar beagle. jar unphased = pink2beagle. bgl misising = 0 nitterations = 20 gprobs = true out = imputed_geno

gzipip imputed_geno. pink2beagle. bgl. phased. gz

fcgene －－bgl imputed_geno. pink2beagle. bgl. phased --- pedinfo pink2beagle_pedinfo. pxt --- snpinfo pink2beagle_snpinfo. pxt --- offormat pink --- out beagle2link

pink --file beagle2link --recode A --out genotipe_op_chip_Rseq
Then enter the following command in R:
library (data.table)
geno <-fread ("genotype_op_chip_Rseq.raw")
geno [, c (1: 6): = NULL]
fwrite (geno, “genotype_op_chip_Rseq.csv”, sep = “,”, low.names = F, quote = F)

a) In R, weighted GBLUP is performed in R using the genotype file obtained in a). The specific method of weighted GBLUP is carried out with reference to the h) part in 1).

b) Substitute the constructed weighted G matrix into ASReml-R to perform cross-validation. It is necessary to carry out grouping before the cross-validation method: Random sort is performed on all individuals using the function sample (1: 931, 931) in R, and the sorted digital is further divided into 5 columns, and each column is divided. The number of elements included in is 186, 186, 186, 186 and 187, respectively. The above process is repeated 10 times to obtain a total of 10 files. Put these 10 files in the same folder to prepare for use. The analysis uses a generalized linear mixed model, with different measurement lots and individual ages as fixed effects, and each individual performing adaptation as a random effect. The specific method of implementing the 5-fold cross-validation method is as follows:
# Unload the required R packages and functions library (parallell)
library (asreml)
library (prOC)
# Phenotype, read the three-column format of G-matrix inversion pheno <-asreml. read. table ("phenotype_931.csv", header = T, sep = ",", na.string = NA)
ped <-asreml. read. table (“pedigree_931.csv”, header = T, sep = “,”, na.string = NA)
ainv <-asreml. Ainverse (ped) $ ginv
Ginv <-fread ("... / Ginv_at_ita_4.csv")
attr (Ginv, "lowNames") <-paste (pheno [, 1])
# Set the number of external cycles N <-10
# Set the result variable res <-matrix (NA, now = 5 * N, ncol = 1)
colnames (res) <-c ("auc")
# Perform cross-validation method and output verification result for (i in 1: N) {
coor <-read. file (file = paste ("./coor/coor_", i, ".csv", sep = ""), sep = ",", header = F)
cyc <-ncol (coor)
gevv <-matrix (NA, now = now (pheno), ncol = cyc)
for (j in 1: cyc) {
y <-pheno
y $ status [coor [(1: sum (coor [, j]> 0, na.rm = T)), j]] <-NA
CV <-asreml (status-Batch + Age,
random = ~ giv (AnimalID),
ginverse = list (AnimalID = Ginv),
rkov = ~ units,
family = asreml. Binomial (link = “logit”),
data = y,
maximizer = 50)

gebv [, j] <-coef (CV) $ random
write. table (gebv, file = paste ("GEBVs_chip_ref_coor_", i, ".csv", sep = ""), sep = ",", low.names = F, quote = F)
res [(j + (i-1) * cyc),] <-loc (as.vector (pheno $ status [coor [(1: sum (coor [, j]> 0, na.rm = T))), j]]), as.vector (gebv [coor [(1: sum (coor [, j]> 0, na.rm = T)), j], j])) $ auc
write. table (res, "results_of_loc_op_chip_ref.csv", sep = ",", low.names = F, quote = F)
}
}
# Conventional BLUP estimation methods are similar to weighted GBLUPs, and ainvs can be used to replace Ginvs in the model. After executing the above code, the AUC mean by weighted GBLUP methods is 0.885, which is a conventional BLUP. The AUC averages by method are 0.579. The results of the 50 cross-validation methods are shown in Table 7.

で変換を実施する。変換後、平均値より高い家系の生存率を１と記載し、平均値より低いものを０と記載する。遺伝子チップ方法がマーカーに対する要件を満たし、すべての候補個体の分類結果に組み合わせを行い、各チップの分類結果を単独で参考グループ遺伝子型と合併する必要がある。 3. Application of "Fish Chip No. 1" gene chip in scallop disease-resistant breeding In order to estimate the genome estimated breeding value (GEBV) of candidate individuals, first obtain the candidate individual genotype ("Fish Chip No. 1" gene chip classification). Is combined with the inventor's existing reference group genotype, and a weighted G matrix is constructed using R. Finally, the prepared weighted G matrix and phenotype data are substituted into ASReml-R, and the weighted GBLUP. The GEBV of each adult family is estimated using the method, and the average value of the adult GEBV is taken as the GEBV of the corresponding family. High survival rate (survival rate is higher than 55%) and low survival rate family (survival rate is lower than 55%) according to the infection survival rate of each family, and AUC value between GEBV and infection survival rate of each family. And further compare the AUC value with the AUC value obtained in 2, and if it is close and thus higher than the AUC obtained in 2, the gene chip designed by the inventor meets the requirements of genome selection technology and is resistant to fluff. It is shown that it has an excellent application effect in sorting. The AUC value between each family GEBV and infection survival rate is calculated and the AUC value is compared with the AUC value obtained in 2, and the gene chip and genome selection technology designed by the inventor are used for flounder disease resistance selection. Used to verify the actual application effect in. Before estimating the AUC value, determine the infection survival rate of the offspring.

Perform the conversion with. After the conversion, the survival rate of the family whose family value is higher than the average value is described as 1, and the survival rate of the family whose value is lower than the average value is described as 0. It is necessary that the gene chip method meets the requirements for the marker, the classification results of all candidate individuals are combined, and the classification results of each chip are independently combined with the reference group genotype.

According to the analysis results, the average survival rate of 6 high survival rate families in the offspring of 16 candidate groups is 62.4% and the average survival rate of 10 low survival rate families is 33.47% ( It can be seen that Table 8). Among them, the average GEBV of adults with high survival rate is 2.10, and the average GEBV of adults with low survival rate is 1.34. According to the calculation, the accuracy of predicting the infection survival rate can reach 0.794 by using the GEBV value of these flounder families, which is close to the theoretical value. Therefore, the gene chip designed by the inventor can be effectively applied to the selection of flounder disease resistance properties.

The specific operation method is as follows:
A candidate individual classification file is used using PLLNK and R, and individuals to be used for subsequent verification are selected from the candidate individuals, and these individual information is stored in one text file, and the family number, individual number, and father's book are stored. A text file is prepared according to an array of numbers, mother numbers, genders, and phenotypic values, and each item is separated by one individual on each line, and each item information of each individual is separated by using a table delimiter. A form for carrying out the invention:
# Read the classification result of each SNP chip, and the result is xxx. bed, xxx. Bim and xxx. Stored in fam.
pink --- vcf op1. vcf －－make－bed －－out op1
pink －－vcf op2. vcf －－make－bed －－out op2
pink －－vcf op3. vcf －－make－bed －－out op3

The SNP nomenclature in each file is converted in R, and the specific operation method is as follows:
#R Unloading the package library (data.table)
# Read data op1 <-fread (“op1.bim”, header = F)
op2 <-fread (“op2.bim”, header = F)
op3 <-fread (“op3.bim”, header = F)
# Convert SNP name op1 $ V2 <-paste (paste (rep ("rs", now (op1)), op1 $ V1, sep = ""), op1 $ V4, sep = ":")
op2 $ V2 <-paste (paste (rep ("rs", now (op2)), op2 $ V1, sep = ""), op2 $ V4, sep = ":")
op3 $ V2 <-paste (paste (rep ("rs", now (op3)), op3 $ V1, sep = ""), op3 $ V4, sep = ":")
# Write to output the classification information acquired by each chip. table (op1 $ V2, "snaps_op1.txt", sep = "\ t", col.names = F, row.names = F, quote = F)
write. table (op2 $ V2, “snaps_op2.txt”, sep = “¥ t”, col.names = F, row.names = F, quote = F)
write. table (op3 $ V2, “snaps_op3.txt”, sep = “¥ t”, col.names = F, row.names = F, quote = F)
# Output the organized Bim file fwrite (op1, "op1.bim", sep = "\ t", col.names = F, row.names = F, quote = F)
file (op2, “op2.bim”, sep = “¥ t”, col.names = F, low.names = F, quote = F)
file (op3, “op3.bim”, sep = “¥ t”, col.names = F, low.names = F, quote = F)
End # R

Prepare the verification individual information and put it in a file with the title "selected_indi.txt", and organize the files according to the following method:
1701. CEL 1701. CEL 0 0 0-9
1702. CEL 1702. CEL 0 0 0-9
The individual contained in each SNP chip is extracted using #LINK software, and the method is as follows:
pink －－bfile op1 －－ keep selected_indi. pxt －－make-bed －－out op_can_1
pink －－bfile op2 －－ keep selected_indi. pxt －－make-bed －－out op_can_2
pink －－bfile op3 －－ keep selected_indi. pxt －－make-bed －－out op_can_3

# Output the genotype file of the individual used for verification pink －－bfile op_can_1 －－recode A －－out op1
pink －－bfile op_can_2 －－recode A －－out op2
pink －－bfile op_can_3 －－recode A －－out op3
Furthermore, in R, the genotype file is output using the following command:
library (data.table)
op1 <-fread (“op1.raw”)
op2 <-fread (“op2.raw”)
op3 <-fread (“op3.raw”)
op1 [, c (1: 6): = NULL]
op2 [, c (1: 6): = NULL]
op3 [, c (1: 6): = NULL]
fwrite (op1, “geno_op1_can.csv”, sep = “,”, low.names = F, quote = F)
fwrite (op2, “geno_op2_can.csv”, sep = “,”, low.names = F, quote = F)
fwrite (op3, “geno_op3_can.csv”, sep = “,”, low.names = F, quote = F)
a) Using PLLNK and R, the reference group genotypes are combined with each chip candidate individual genotype, and the specific operation method is as follows:
pink －－bfile… / op_Ref －－extract snaps_op1. pxt --recode A --out op_Ref_op1
pink －－bfile… / op_Ref －－extract snaps_op2. pxt －－recode A －－out op_Ref_op2
pink －－bfile… / op_Ref －－extract snaps_op3. pxt －－recode A －－out op_Ref_op3
In addition, use the following command in R to process and output the genotype file:
library (data.table)
# Reference group Read the genotype and perform processing on it op1_ref <-fread (“op_Ref_op1.raw”)
op2_ref <-fread (“op_Ref_op2.raw”)
op3_ref <-fread (“op_Ref_op3.raw”)
op1_ref [, c (1: 6): = NULL]
op2_ref [, c (1: 6): = NULL]
op3_ref [, c (1: 6): = NULL]
op1_ref <-as. matrix (op1_ref)
op2_ref <-as. matrix (op2_ref)
op3_ref <-as. matrix (op3_ref)
# Process candidate individual genotypes: read, combine op1_can <-as. matrix (fread (“geno_op1_can.csv”))
op2_can <-as. matrix (fread (“geno_op2_can.csv”))
op3_can <-as. matrix (fread (“geno_op3_can.csv”))
geno_op1 <-rbind (op1_can, op1_ref)
geno_op2 <-rbind (op2_can, op2_ref)
geno_op3 <-rbind (op3_can, op3_ref)
write. table (geno_op1, “geno_op1.csv”, sep = “,”, low.names = F, quote = F)
write. table (geno_op2, "geno_op2.csv", sep = ",", low.names = F, quote = F)
write. table (geno_op3, "geno_op3.csv", sep = ",", low.names = F, quote = F)

b) Using the four processed genotype files in b), a weighted G matrix is constructed in R, respectively, and the method for constructing the weighted G matrix is the same as the description in 1).

c) Estimate the GEBV of a candidate individual using ASReml-R and the code is:
# Unload the required R packages and functions library (data.table)
library (asreml)
#### op1 ####
pheno <-asreml. read. table ("phenotype_op1.csv", sep = ",", header = T)
Ginv <-fread ("... / Ginv_op1.csv")
attr (Ginv, “rowNames”) <-paste (pheno [, 1])
op1 <-asreml (status ~ Batch + Age, random = ~ give (AnimalID), giverse = list (AnimalID = Ginv), logov = ~ units, random = inl. X = “omit”, data = pheno, maximizer = 50)
write. table (coef (op1) $ random, "gebv_op1.csv", sep = ",", col.names = F, quote = F)

#### op2 ####
pheno <-asreml. read. table ("phenotype_op2.csv", sep = ",", header = T)
Ginv <-fread (“… / Ginv_op2.csv”)
attr (Ginv, "lowNames") <-paste (pheno [, 1])
op2 <-asreml (status ~ Batch + Age, random = ~ give (AnimalID), giverse = list (AnimalID = Ginv), logov = ~ units, random = inl. X = “omit”, data = pheno, maximizer = 50)
write. table (coef (op2) $ random, "gebv_op2.csv", sep = ",", col.names = F, quote = F)
#### op3 ####
pheno <-asreml. read. table ("phenotype_op3.csv", sep = ",", header = T)
Ginv <-fread (“… / Ginv_op3.csv”)
attr (Ginv, “rowNames”) <-paste (pheno [, 1])
op3 <-asreml (status ~ Batch + Age, random = ~ give (AnimalID), giverse = list (AnimalID = Ginv), logov = ~ units, random = inl. X = “omit”, data = pheno, maximizer = 50)
write. table (coef (op3) $ random, "gebv_op3.csv", sep = ",", col.names = F, quote = F)

を用いて各家系の感染生存率に変換を行い、変換後の平均値より高い家系の生存率を１に設定し、平均値より低いものを０に設定する。最後に、各家系ＧＥＢＶと変換後の生存率の間のＡＵＣ値を計算し、ＡＵＣ値の計算方法は以下のとおりである：
＃必要なＲパッケージをアンロードする
ｌｉｂｒａｒｙ（ｄａｔａ．ｔａｂｌｅ）
ｌｉｂｒａｒｙ（ｐＲＯＣ）
＃各家系ＧＥＢＶおよび相応の感染生存率を一つのファイルに整理し、家系番号・ＧＥＢＶと変換後の感染生存率の配列に従って配列し、各行は一つの家系情報のみを含み、整理後のファイルを読み取る
ｒｅｓ ＜－ ｆｒｅａｄ（“…／ｇｅｂｖ＿ａｎｄ＿ｓｒ＿ｏｐ＿ｃａｎ．ｃｓｖ”）
＃子の代の家系生存率を変換する
ｒｅｓ＄ＳＲ＿ｔｒａｎｓ ＜－ ｅｘｐ（ｒｅｓ＄ＳＲ） ／ （１ ＋ ｅｘｐ（ｒｅｓ＄ＳＲ））
＃変換後の生存率を平均値に従ってさらに０と１に変換する
ＳＲ＿ｂｉｎａｒｙ ＜－ ｍａｔｒｉｘ（ＮＡ， ｎｒｏｗ ＝ ｎｒｏｗ（ｒｅｓ）， ｎｃｏｌ ＝ １）
ＳＲ＿ｂｉｎａｒｙ［ｗｈｉｃｈ（ｒｅｓ＄ＳＲ＿ｔｒａｎｓ ＞ ｍｅａｎ（ｒｅｓ＄ＳＲ＿ｔｒａｎｓ））， ］ ＜－ １
ＳＲ＿ｂｉｎａｒｙ［ｗｈｉｃｈ（ｒｅｓ＄ＳＲ＿ｔｒａｎｓ ＜ ｍｅａｎ（ｒｅｓ＄ＳＲ＿ｔｒａｎｓ））， ］ ＜－ ０
＃ＡＵＣ値を計算する
ｒｏｃ（ＳＲ＿ｂｉｎａｒｙ［， １］， ｒｅｓ＄ＧＥＢＶ）
計算を経て、１６のヒラメ家系のＧＥＢＶを取得し、計算は各家系ＧＥＢＶと相応の感染生存率の間のＡＵＣ（正確性）が０．７９４であることを示す。各家系ＧＥＢＶおよび感染生存率は表８による。 d) Calculate and formulate the appropriate pedigree GEBV according to the estimated GEBV of all candidate individuals

Is used to convert the infection survival rate of each family, the survival rate of the family higher than the average value after conversion is set to 1, and the survival rate of the family lower than the average value is set to 0. Finally, the AUC value between each family GEBV and the post-conversion survival rate is calculated, and the calculation method of the AUC value is as follows:
# Unload the required R package library (data.table)
library (prOC)
# Organize each family GEBV and corresponding infection survival rate into one file, arrange according to the sequence of family number / GEBV and converted infection survival rate, each line contains only one family information, and the file after organization Read res <-fread ("... / gebv_and_sr_op_can.csv")
# Convert the family survival rate of the child's generation res $ SR_trans <-exp (res $ SR) / (1 + exp (res $ SR))
# Convert the survival rate after conversion to 0 and 1 according to the average value SR_binary <-matrix (NA, now = now (res), ncol = 1)
SR_binary [which (res $ SR_trans> mean (res $ SR_trans)),] <-1
SR_binary [which (res $ SR_trans <mean (res $ SR_trans)),] <-0
# Loc to calculate AUC value (SR_binary [, 1], res $ GEBV)
Through the calculations, GEBV of 16 flounder families is obtained, and the calculation shows that the AUC (accuracy) between each family GEBV and the corresponding infection survival rate is 0.794. Table 8 shows the GEBV and infection survival rate of each family.

候補群における１６の子の代家系を感染生存率に従って６の高生存率家系（平均的な生存率が６２．４％である）と１０の低生存率家系（平均的な生存率が３３．４７％である）の二大類（表８）に分け、高生存率と低生存率家系の成体のＧＥＢＶを比較し、高生存率家系成体の平均的なＧＥＢＶが２．１０で、低生存率家系成体の平均的なＧＥＢＶが１．３４であることがわかる。計算はよると、これらのヒラメ家系のＧＥＢＶ値を使用してはその感染生存率が予測される正確性が０．７９４に達することができ、理論値に近い。そのため、発明者が設計される遺伝子チップはヒラメ耐病性性状の選別に効果的に応用することができる。

16 offspring in the candidate group were infected according to survival rate, 6 high survival rate families (average survival rate is 62.4%) and 10 low survival rate families (average survival rate 33. It is divided into two major categories (Table 8) (47%), and the GEBV of adults with high survival rate and low survival rate is compared. The average GEBV of adult with high survival rate is 2.10, and the low survival rate. It can be seen that the average GEBV of an adult family is 1.34. According to the calculation, the accuracy of predicting the survival rate of infection can reach 0.794 using the GEBV values of these flounder families, which is close to the theoretical value. Therefore, the gene chip designed by the inventor can be effectively applied to the selection of flounder disease resistance properties.

According to the above results, the individual of the flounder candidate group was genetically classified using the "Fish Chip No. 1" gene chip, the genome breeding value (GEBV) was calculated using the weighted GBLUP, and the flounder was calculated according to the magnitude of the GEBV value. Disease-resistant parent fish are selected, and the survival rate of progeny seedlings bred using these parent fish is significantly increased. As a result, the "Fish Chip No. 1" gene chip is widely applied in the breeding of excellent flounder disease-resistant varieties. It turns out that you can do.

Industrial feasibility The gene chip of SNP locus related to flounder disease resistance provided by the present invention can be used for selection of flounder disease resistant individuals, and the actual selection accuracy is close to the theoretical value. Increased selection accuracy of excellent disease-resistant varieties and shortened breeding period, thereby providing gene chip technology for selection of excellent flounder disease-resistant varieties, and gene chip breeding for selection of excellent fish disease-resistant varieties. Can pave the way for new roads.

Claims (7)

An SNP locus related to flounder disease resistance, wherein the SNP locus is a base at position 36 of any one of the sequences in which the sequence is SEQ NO: 1-SEQ ID NO: 48697. SNP locus related to flounder disease resistance. Application of the SNP locus related to flounder disease resistance according to claim 1 to the selection and breeding of flounder disease resistant varieties. Application of the SNP locus related to the flounder disease resistance according to claim 1 to the manufacture of a inspection product for selecting and cultivating a variety having excellent flounder disease resistance. The application according to claim 3, wherein the inspection product is a gene chip. A gene chip used for selecting an excellent flounder disease-resistant variety, wherein the gene chip can detect SNP locus related to the flounder disease resistance according to claim 1, and is used for selecting an excellent flounder disease-resistant variety. The gene chip used. A method for selecting a flounder disease-resistant individual, wherein the method is a method for selecting a flounder disease-resistant individual, which comprises performing a measurement using the gene chip according to claim 3. The method for selecting a flounder disease-resistant individual according to claim 6.
The method is
1) A step of extracting individual genomic DNA in a candidate group and performing a test using the above gene chip to obtain the result of genotyping of an SNP marker.
2) The result of genotyping of SNP locus, which is similar to the gene chip, is extracted from the SNP set of the reference group, and the result of genotyping of SNP of the reference group and the genotype of the candidate group obtained using the chip. Steps to merge with the judgment result,
3) Using the combined genotype and the type expressed by the reference group, the estimated breeding value GEBV of the candidate group is estimated using the weighted GBLUP method, and the disease resistance potential of the test-tested individual is further estimated based on the GEBV value. Steps to determine ability and
Including
In addition, the genotype of the reference group is used to estimate the prediction accuracy using the weighted best linear bias estimation method.
Among them, the 5-fold cross-validation method is used to estimate the prediction accuracy, and the area AUC under the feature curve is used as an index to judge the prediction accuracy. The closer the AUC is to 1, the higher the prediction accuracy is. How to select flounder disease resistant individuals.