Method for identifying fish genome structure variation based on graph genome and application

Abstract

The invention discloses a method for identifying fish genome structure variation based on a graph genome and application, and the method comprises the following steps: 1) respectively comparing fish individual genome data of different germplasm with a reference genome to determine variation information between each individual genome sequence and the reference genome; 2) constructing a graphical generic genome by using variation information; and 3) comparing the genome data of the fish individuals to be identified with the graphical generic genome to generate a genome structure variation data set of the fish individuals to be identified. The method can comprehensively and accurately identify fish genome structure variation, can identify specific structure variation, provides a scientific basis for fish genetic improvement, breeding and resource protection, and is suitable for popularization and application.

Description

Technical Field

[0001] The present invention belongs to the technical field of pan-genome identification of structural variation, and in particular relates to a method and application of identifying fish genome structural variation based on a pan-genome. Background Art

[0002] With the development of genomics, especially the rise of pan-genomics, more and more species are revealing their genetic diversity and structural variation by constructing pan-genome maps. In genomic research, structural variation (such as insertion, deletion, inversion, translocation, etc.) is a key factor affecting species phenotypic diversity and adaptive changes. Fish, as a large group of vertebrates, has rich genetic diversity and complex genetic structure, and its important traits (such as growth rate, disease resistance, reproductive capacity, etc.) are often closely related to genome structural variation. However, the existing technology mainly identifies structural variation through linear genome sequence alignment. This method has limitations when dealing with complex genome regions (such as repetitive sequences, highly variable regions, etc.), and it is difficult to fully and accurately capture structural variation information. Identifying structural variation in fish genomes not only helps to understand their adaptive mechanisms, but also provides a scientific basis for fish breeding and resource protection. Traditional methods mainly rely on single linear reference genome sequence alignment, which is difficult to fully capture structural variation information. Therefore, a method based on graph pan-genome came into being. This method can store and display all the genetic information and sequence structural variation of species, and has become a new hotspot in fish genetics research.

[0003] The graph-pan genome represents individual genomes and their variations as a graph structure based on the de novo assembly of the genome. This graph structure can comprehensively display the genetic information and structural variation of species, including single nucleotide polymorphisms (SNPs), insertion / deletion mutations (InDels), copy number variations (CNVs), and presence / absence variations (PAVs). Compared with traditional linear genomes, the graph-pan genome can more accurately reflect the genetic diversity and structural variation of species, providing a powerful tool for the analysis of important traits. Summary of the invention

[0004] An object of the present invention is to solve at least the above problems and / or disadvantages and to provide at least the advantages which will be described hereinafter.

[0005] In order to solve the problems existing in the prior art, the present invention aims to provide a method for identifying fish genome structural variation based on a pan-genome. For fish genomes, structural variation can be quickly identified through a constructed pan-genome containing all germplasm structural variation information; the identification method is simple, the analysis takes up little memory and is not limited by the number of genomes.

[0006] To this end, the technical solution provided by the present invention is:

[0007] In a first aspect, a method for identifying fish genome structural variation based on a pan-genome graph comprises the following steps:

[0008] 1) Compare the individual genomic data of fish of different germplasms with the reference genome to determine the variation information between each individual genomic sequence and the reference genome; that is, compare the individual genomic data of fish of each germplasm with the reference genome one by one;

[0009] 2) constructing a graphical pan-genome using the variation information;

[0010] 3) Comparing the genome data of the individual fish to be identified with the graphical pan-genome to generate a data set of genome structural variations of the individual fish to be identified.

[0011] Preferably, in the method for identifying fish genome structural variation based on map-pan-genome, in step 1), representative fish germplasms are selected for de novo genome assembly to obtain individual fish genome data of different germplasms.

[0012] Preferably, in the method for identifying fish genome structural variation based on graph pan-genome, in step 1), the genome assembly data is compared with the reference genome by minimap2 software to obtain variation information in vcf format, and then the graph pan-genome is constructed.

[0013] Preferably, in the method for identifying fish genome structural variation based on graph-pan genome, in step 3), 10× high-depth resequencing is performed on the sample of the individual fish to be identified, and each gene sequencing sample is aligned to the graph-pan genome using vg giraffe software to obtain a result file in GAM format;

[0014] Then use "vg stats" to count the alignment results; use "vg gamsort" to sort the alignment results on the constructed pan-genome, use "vg call" to detect structural variations in samples of different germplasms, and generate a variation file in vcf format for each sample;

[0015] Use bgzip to compress the variant files in vcf format and use tabix to create an index; use bcftoolsmerge software to merge the variant files of each sample, and finally generate a total variant data file in vcf format containing all samples.

[0016] Preferably, the method for identifying fish genome structural variation based on map pan-genome also includes: using plink to convert the total variation data file in vcf format into map and ped formats, combining the phenotypic information of different germplasm samples, and using gcta64 to perform PAV-GWAS to identify structural variations related to economic traits of fish.

[0017] Preferably, in the method for identifying fish genome structural variation based on graph-pan genome, the mirror carp "Longke No. 11" assembled and sequenced by T2T is used as the reference genome. More preferably, the fish individuals of different germplasms include carp "Longke No. 12", German mirror carp, Yuxuan Yellow River carp No. 2, Furui carp No. 2, Jianli No. 2, Yuanjiang carp, red carp, Songpu mirror carp and Heilongjiang carp.

[0018] Preferably, in the method for identifying fish genome structural variation based on graph pan-genome, the genes in the graph pan-genome are divided into core gene families, variable gene families and unique gene families according to the number of germplasm covered.

[0019] Preferably, in the method for identifying fish genome structural variation based on map pan-genome, the fish individual to be identified belongs to one of the fish individuals of different germplasm in step 1).

[0020] Preferably, the method for identifying fish genome structural variation based on pan-genome comprises the following steps:

[0021] Step 1: Select representative fish germplasms for de novo genome assembly, use PacBio HiFi sequencing, use Hifiasm software for de novo assembly, perform Hi-C sequencing and mount to the chromosome level, and use BUSCO to analyze its integrity;

[0022] Step 2: Integrate the chromosome-level genome in step 1, use the T2T genome as the backbone of the pan-genome, and combine it with the previously published or sequenced third-generation genomes. Use minimap2 software to compare the genome assembly data with the reference genome to obtain variation information in vcf format, and use vg software to construct the pan-genome. The genes in the pan-genome are divided into core gene families, variable gene families, and unique gene families according to the number of germplasms covered;

[0023] Step 3: Perform 10× high-depth resequencing on samples of different germplasms for the pan-genome obtained in step 2; use vg giraffe software to align the clean reads of each sample to the pan-genome and obtain a result file in GAM format;

[0024] Step 4: Then use "vg stats" to count the alignment results; use "vggamsort" to sort the alignment results on the constructed pan-genome, use "vg call" to detect SVs of samples from different germplasms, and generate a variation file in vcf format for each sample;

[0025] Step 5: Use bgzip to compress the variant file in vcf format and use tabix to create an index. Use bcftools merge to merge the variant files of each sample and finally generate a total variant file in vcf format containing all samples;

[0026] Step 6. Use plink to convert the vcf file into map and ped formats. After quality control, combine the phenotypic information of samples from different germplasms, and use gcta64 to perform PAV-GWAS to identify structural variations associated with important economic traits of fish.

[0027] Secondly, the genes related to the high-quality traits of carp are:

[0028] Chr1:11972915, the variant type is deletion, the nucleotide sequence before deletion (wild type) is shown in SEQ ID NO:1, and the nucleotide sequence after deletion (mutant type) is shown in SEQ ID NO:2;

[0029] Chr1:21527167, the variant type is deletion, the nucleotide sequence before deletion (wild type) is shown in SEQ ID NO:3, and the nucleotide sequence after deletion (mutant type) is shown in SEQ ID NO:4;

[0030] Chr1:23399634, the variant type is insertion, the nucleotide sequence before insertion (wild type) is shown in SEQ ID NO:5, and the nucleotide sequence after insertion (mutant type) is shown in SEQ ID NO:6;

[0031] Chr7:2062567, the variant type is deletion, the nucleotide sequence before deletion (wild type) is shown in SEQ ID NO:7, and the nucleotide sequence after deletion (mutant type) is shown in SEQ ID NO:8;

[0032] Chr31:9671978, the variant type is insertion, the nucleotide sequence before insertion (wild type) is shown in SEQ ID NO:9, and the nucleotide sequence after insertion (mutant type) is shown in SEQ ID NO:10;

[0033] Chr40:9140647, the variant type is insertion, the nucleotide sequence before insertion (wild type) is shown in SEQ ID NO:11, and the nucleotide sequence after insertion (mutant type) is shown in SEQ ID NO:12;

[0034] Chr48:8724756, the variant type is deletion, the nucleotide sequence before deletion (wild type) is shown in SEQ ID NO:13, and the nucleotide sequence after deletion (mutant type) is shown in SEQ ID NO:14;

[0035] Chr50:4154584, the variant type is insertion, the nucleotide sequence before insertion (wild type) is shown in SEQ ID NO:15, and the nucleotide sequence after insertion (mutant type) is shown in SEQ ID NO:16;

[0036] Chr20:2258997, the variant type is deletion, the nucleotide sequence before deletion (wild type) is shown in SEQ ID NO:17, and the nucleotide sequence after deletion (mutant type) is shown in SEQ ID NO:18;

[0037] Chr25:3825897, the variant type of which is insertion, the nucleotide sequence before insertion (wild type) is shown in SEQ ID NO:19, and the nucleotide sequence after insertion (mutant type) is shown in SEQ ID NO:20; and / or,

[0038] Chr44:16600200, the variation type is deletion, the nucleotide sequence before deletion (wild type) is shown in SEQ ID NO:21, and the nucleotide sequence after deletion (mutant type) is shown in SEQ ID NO:22;

[0039] Among them, Chr1:11972915, Chr1:21527167, Chr1:23399634, Chr7:2062567, Chr31:9671978, Chr40:9140647, Chr48:8724756 and Chr50:4154584 were related to the intramuscular fat content of carp, and Chr20:2258997, Chr25:3825897 and Chr44:16600200 were related to the linoleic acid content of carp muscle.

[0040] In the third aspect, the gene is used in breeding carp with high-quality traits.

[0041] The present invention has at least the following beneficial effects:

[0042] (1) The present invention can comprehensively reflect genetic variation by identifying fish genome structural variation based on the map pan-genome. By constructing the map pan-genome, it is possible to comprehensively capture the genetic variation information between fish individuals, including various types of structural variation.

[0043] (2) The present invention can improve the accuracy of structural variation identification, and use graphical structures to represent genomic information, which can more accurately identify the location, size and type of structural variation.

[0044] (3) The present invention can eliminate mapping bias. The pan-genomic technology can eliminate mapping bias in traditional linear genome sequence alignment and improve the efficiency of identifying structural variations.

[0045] (4) The present invention helps to reveal the characteristics of complex sites. By deeply analyzing the characteristics of complex sites in the map pan-genome, it can reveal the structural variation sites closely related to the fish genome. The method of identifying fish genome structural variation based on the map pan-genome of the present invention has the advantages of being comprehensive, accurate, and efficient, and provides a new technical means for fish genetic improvement, breeding, and resource protection.

[0046] Other advantages, objectives and features of the present invention will be embodied in part through the following description, and in part will be understood by those skilled in the art through study and practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 The carp pan-genome map constructed for the embodiment of the present invention, wherein A: display of 10 carp germplasm resources; B: gene family saturation analysis; C: core, variable and unique gene family analysis; D: petal map display of core genes and variable genes of 10 carp germplasms; E: gene family frequency analysis of 10 carp germplasms; F: statistical analysis of core, unique and variable gene families of each carp germplasm.

[0048] Figure 2 It is a graph of PAV detection and annotation results of mirror carp "Longke No. 11" (KB) and carp "Longke No. 12" (YZ) in the embodiments of the present invention, wherein A: GO enrichment analysis of PAV-associated genes of mirror carp "Longke No. 11" (KB); B: KEGG pathway enrichment analysis of PAV-associated genes of mirror carp "Longke No. 11" (KB); C: GO enrichment analysis of PAV-associated genes of carp "Longke No. 12" (YZ); D: KEGG pathway enrichment analysis of PAV-associated genes of carp "Longke No. 12" (YZ).

[0049] Figure 3It is a structural variation diagram related to carp high-quality traits identified based on PAV-GWAS in an embodiment of the present invention, wherein A: Manhattan diagram and QQ diagram in PAV-GWAS analysis of carp intramuscular fat content; B: Manhattan diagram and QQ diagram in PAV-GWAS analysis of carp muscle linoleic acid content.

[0050] Figure 4 This is an analysis chart of intramuscular fat content in carp carrying different PAV genotypes related to intramuscular fat content in an embodiment of the present invention.

[0051] Figure 5 This is a graph analyzing the linoleic acid content of carp carrying different genotypes of PAVs related to linoleic acid content in an embodiment of the present invention. DETAILED DESCRIPTION

[0052] The present invention is further described in detail below in conjunction with the accompanying drawings so that those skilled in the art can implement the invention with reference to the description.

[0053] It should be noted that the experimental methods described in the following embodiments are conventional methods unless otherwise specified, and the reagents and materials can be obtained from commercial channels unless otherwise specified.

[0054] The present invention aims to provide a method for identifying fish genome structural variation based on graph-pan genome. The method utilizes graph-pan genome technology, constructs a graph structure containing genome information of multiple fish individuals, aligns the second-generation resequencing data to the graph-pan genome, and uses PAV-GWAS to identify structural variation related to the fish genome. The present invention can comprehensively and accurately identify fish genome structural variation, and can identify unique structural variation, providing a scientific basis for fish genetic improvement, breeding and resource protection, and is suitable for promotion and application.

[0055] The present invention provides a method for identifying fish genome structural variation based on a pan-genome, comprising the following steps:

[0056] (1) Genome data collection and preprocessing: Collect genome sequencing data of multiple fish individuals and perform quality control and preprocessing, including removing low-quality sequences and correcting sequencing errors, to ensure that the data quality meets the requirements of subsequent analysis.

[0057] (2) De novo genome assembly: Assemble the genomes of multiple fish individuals with high quality to obtain sequence information of individual genomes.

[0058] (3) Obtaining variation information: Use alignment software (such as minimap2, MUMmer, etc.) to align the individual genome with the reference genome to obtain variation information (vcf file).

[0059] (4) Graph pan-genome construction: Using specialized graph pan-genome construction software (such as vg, pggb, etc.), the genomic variation information of multiple fish individuals and the reference genome sequence are integrated into a graph structure to construct a graph pan-genome. This graph pan-genome can comprehensively display the genetic variation between individuals, including single nucleotide polymorphisms (SNPs), insertion / deletion mutations (InDels), copy number variations (CNVs), presence / absence variations (PAVs) and other types of structural variation.

[0060] (5) Structural variation identification: Based on the graph pan-genome, structural variation is identified using specialized variation analysis software (such as SyRI, SVMU, Assemblylytics, etc.). These software can efficiently identify a variety of variation types including large-scale structural variation (SV), copy number variation (CNV), and presence / absence variation (PAV). By comparing the graph structural differences between different individuals, structural variation sites related to the fish genome are identified.

[0061] (6) Analysis of important traits: Based on the mined structural variation information, combined with the biological characteristics and phenotypic data of fish, the identified structural variations are analyzed for association with important traits. By comparing and analyzing the functions and characteristics of core / non-core genes, the mechanisms of the generation of common and unique phenotypes of fish are explored. At the same time, through statistical methods (such as genome-wide association analysis (GWAS)) and machine learning algorithms, structural variation sites on the genome that are significantly associated with important traits are screened out, providing key information for subsequent breeding and resource protection. At the same time, this method is applied to fish breeding practice to improve breeding efficiency by screening individuals with excellent traits.

[0062] In order to enable those skilled in the art to better understand the technical solution of the present invention, the following embodiments are provided for illustration:

[0063] Example 1: A method for identifying structural variation of high-quality traits of carp based on a pan-genome

[0064] 1. Construction of carp resource population

[0065] A total of 7 carp resource groups were collected nationwide, including the mirror carp "Longke No. 11", carp "Longke No. 12", German mirror carp, Yuxuan Yellow River Carp No. 2, Furui Carp No. 2, Jianli No. 2, Yuanjiang carp, and the red carp, Songpu mirror carp, and Heilongjiang carp that already had third-generation sequencing data, a total of 10 carp germplasm pan-genome maps were constructed.

[0066] 2. Genome sequencing

[0067] The above 7 carp germplasms, including mirror carp "Longke 11", carp "Longke 12", German mirror carp, Yuxuan Yellow River carp 2, Furui carp 2, Jianli 2, and Yuanjiang carp, were subjected to second-generation small fragment, HiFi, Hi-C, transcriptome and full-length transcriptome library construction, and the ultra-long ONT library construction and sequencing of the mirror carp "Longke 11" (KB) assembled and sequenced by T2T was performed.

[0068] 3. De novo genome assembly

[0069] The genomes of mirror carp "Longke 11", carp "Longke 12", German mirror carp, Yuxuan Yellow River carp 2, Furui carp 2, Jianli 2, and Yuanjiang carp were constructed and assembled with high quality to obtain the sequence information of various plasm genomes. Finally, the genome Contig N50 length of the seven carp germplasms was 28.34Mb, and the average genome size was 1.57Gb.

[0070] 4. Genome annotation

[0071] The genome assembly and annotation were assisted by constructing and sequencing eukaryotic transcriptome libraries of 6 different tissues of 7 carp germplasms. Hi-C technology assisted genome assembly. Genome annotation mainly includes three aspects: repetitive sequence annotation, gene annotation (including gene structure prediction and gene function prediction), and non-coding RNA (ncRNA) annotation. By annotating 7 carp genomes, the results showed that in the 7 carp genomes, the average repetitive sequence accounted for 41.46%, and an average of 45,217 coding genes were predicted, of which 99.5% of the genes could be predicted to have functions. According to the HiC data obtained by sequencing, the assembled contigs / scaffolds sequences were mounted to the near-chromosome level, and then manually corrected according to the chromosome interaction strength, and finally the chromosome-level genome was obtained. The average genome size is 1.57Gb, and the average mounting rate is 96.83%.

[0072] 5. Construction of pan-genome

[0073] The first step in building a graph pan-genome is to select a suitable reference genome. In this embodiment, the mirror carp "Longke No. 11" (KB) assembled and sequenced by T2T is used as a reference genome. The genome sequencing data of 9 individuals are compared with the reference genome respectively using minimap2 comparison software, aiming to determine the difference between the individual genome sequence and the reference genome, and obtaining variation information (vcf files) including single nucleotide polymorphisms, insertions and deletions. After completing the comparison, it is necessary to analyze the variation information in detail. This includes key information such as the position, type, and frequency of the identification variation, and annotating it to reveal the influence of variation on gene structure and function. After obtaining the variation information, a special graph pan-genome construction tool vg can be used to build a graph pan-genome. This step integrates the reference genome sequence and variation information into a complex graph structure, in which each node represents a position in the genome, and the edge represents the connection relationship between different positions. The genes in the pan-genome are divided into core gene families, variable gene families, and unique gene families according to the number of covered germplasms.

[0074] 6. Identification of structural variations

[0075] The samples of different germplasms were resequenced at 10× high depth; the quality-controlled sequencing sequences (clean reads) of each sample were aligned to the pan-genome using vg giraffe software to obtain the structural variation result file in GAM format; then the alignment results were statistically analyzed using "vg stats"; on the constructed pan-genome, the alignment results were sorted using "vggamsort", and SV detection was performed on samples of different germplasms using "vg call" to generate a variation file in vcf format for each sample; the variation file in vcf format was compressed using bgzip and indexed using tabix. The variation files of each sample were merged using bcftools merge, and a total variation file in vcf format containing all samples was finally generated.

[0076] 7. PAV-GWAS analysis of carp quality traits

[0077] The vcf file was converted into map and ped formats using plink. After quality control, the phenotypic information of high-quality traits (including intramuscular fat content and linoleic acid content) of different carp germplasms was combined, and the mixed linear model (MLM) of GCTA software was used to conduct PAV-GWAS of carp high-quality traits. The population structure was used as a fixed effect, and the individual kinship matrix was used as a random effect. The threshold of the P value was corrected using the Bonferroni correction method to determine the significance threshold. When the P value of the SNP exceeded the threshold, it was considered to be a PAV significantly associated with the high-quality traits of carp, and the structural variation associated with the high-quality traits of carp was identified. The Manhattan plot and QQ plot were drawn using the R language CMplot package to visualize the results of the whole genome association analysis.

[0078] 8. Results Analysis

[0079] The Carp Pan-Genome was constructed using the genomes of seven carp germplasms (Mirror Carp "Longke 11", Carp "Longke 12", German Mirror Carp, Yuxuan Yellow River Carp 2, Furui Carp 2, Jianli 2, Yuanjiang Carp) and three existing carp reference genomes (Purple Red Carp, Heilongjiang Carp, Songpu Mirror Carp), a total of 10 carp germplasms Figure 1 (A) Gene family saturation analysis Figure 1 As shown in Figure B, as the number of genomes increases, the number of core gene families decreases, while the number of pan-gene families increases. Figure 1 As shown in Figure C, there are 17745 core gene families (accounting for 43.44% of all gene families), 21714 variable gene families (accounting for 53.15% of all gene families), and 1393 unique gene families (accounting for 3.41% of all gene families). Figure 1 As shown in D, there are 20 unique gene families in mirror carp "Longke 11" (KB), and 34 unique gene families in carp "Longke 12" (YZ). The frequency analysis of different gene families in 10 carp germplasms is shown in E. Among them, the gene family that only appears in one carp germplasm is the unique gene, the gene family that appears in 10 carp germplasms is the core gene, and the gene family that appears in 2 to 9 carp germplasms is the variable gene. The statistics of core, unique, and variable gene families of each carp germplasm are shown in Figure 1. Figure 1 As shown in F, among them, the number of variable genes in red carp and Songpu mirror carp is relatively large among the 10 carp germplasms, and the number of gene families identified in red carp is the largest, which is 31,656 gene families.

[0080] PAV detection and annotation of mirror carp “Longke 11” (KB) and carp “Longke 12” (YZ): The PAV detection and annotation results of mirror carp “Longke 11” (KB) are shown in Figure 2. Figure 2As shown in AB: KB-specific PAV-associated genes GO enriched in biological processes such as platelet-derived growth factor receptor signaling pathway, threonine metabolism process, and feeding behavior; KB-specific PAV-associated genes KEGG enriched in pathways such as dorsal-ventral axis formation, Apelin signaling pathway, cell apoptosis, phenylalanine metabolism, PPAR signaling pathway, and autophagy.

[0081] The PAV detection and annotation results of carp “Longke 12” (YZ) are shown in the figure. Figure 2 As shown in the figure CD: YZ-specific PAV-associated genes GO enriched in biological processes such as glutamine-tRNA aminoacylation, protein deubiquitination, lipoprotein metabolic process, TOR signaling, MyD88-dependent toll-like receptor signaling pathway; YZ-specific PAV-associated genes KEGG enriched in metabolic pathways such as steroid biosynthesis and inositol phosphate metabolism; biological system pathways such as the intestinal immune network produced by IgA.

[0082] The high-quality traits (referring to the quality of fish meat) indicators (including intramuscular fat content and linoleic acid content) of 266 fish of Yuxuan Yellow River Carp No. 2, Jianli No. 2, Mirror Carp "Longke No. 11", Carp "Longke No. 12" and Songpu Mirror Carp were tested. PAV-GWAS was performed to identify PAVs related to carp quality traits in combination with PAVs identified by pan-genome. The results showed that a total of 8 PAVs significantly associated with carp intramuscular fat content were identified using the Bonferroni corrected P value (0.05 / 162727) as the significance threshold. Figure 3 Among them, DEL Chr1:11972915 and DEL Chr1:21527167 on chromosome 1, DELChr7:2062567 on chromosome 7, and DEL Chr48:8724756 on chromosome 48 are deletion mutations, and carp carrying the deletion mutation genotype (MM) have higher intramuscular fat content and better quality fish meat ( Figure 4 ). INS Chr1:23399634 on chromosome 1, INS Chr31:9671978 on chromosome 31, INS Chr40:9140647 on chromosome 40, and INSChr50:4154584 on chromosome 50 are insertion mutations. Among them, carp carrying the insertion mutation genotype (MM) of INS Chr1:23399634 on chromosome 1 and INSChr50:4154584 on chromosome 50 have higher intramuscular fat content and better quality fish meat ( Figure 4 ). Eight PAVs significantly correlated with carp intramuscular fat content were located in the intronic and intergenic regions of genes (Table 1). At the same time, using the Bonferroni-corrected P value (0.05 / 162727) as the significance threshold, a total of three PAVs significantly correlated with carp muscle linoleic acid content were identified ( Figure 3 (B). Among them, DEL Chr20:2258997 located on chromosome 20 and DEL Chr44:16600200 located on chromosome 44 are deletion variants. For DEL Chr20:2258997, carp carrying the deletion mutation genotype (MM) have a higher linoleic acid content and better fish meat; while for DEL Chr44:16600200, carp carrying the wild type (WW, i.e. not deleted) have a higher linoleic acid content and better fish meat. INS Chr25:3825897 located on chromosome 25 is an insertion variant. Carp carrying the wild type (WW, i.e. not inserted) have a higher linoleic acid content and better fish meat. (Table 2 and Figure 5 ). The three PAVs significantly correlated with linoleic acid content in carp were located in the intronic and intergenic regions of genes (Table 2).

[0083] Table 1 PAVs associated with intramuscular fat content in carp identified by PAV-GWAS

[0084]

[0085] Table 2 PAVs associated with linoleic acid content in carp identified by PAV-GWAS

[0086]

[0087] The PAV sequence related to the intramuscular fat content of carp is shown in SEQ ID NO: 1-16, wherein the genome sequence of DEL Chr1: 11972915 is shown below, and the variation type is deletion.

[0088] (SEQ ID NO:1)

[0089] GACAAAACATATTTAATGCTATTTCATAAGTAATGCAGTAAACCAGTGGTGACTTCATACACCCACTAAAATTCAAATTGGGACCAAG

[0090] GGGTTAAACGTAATTGCAAAATTGACTTAATTCTCTATAATAATATATCTCCAGATGCTGCT

[0091] (SEQ ID NO:2)AATGAAATTAAGACAAAACATATTTAA

[0092] (SEQ ID NO: 1 represents the sequence before deletion, and SEQ ID NO: 2 represents the sequence after deletion).

[0093] The genomic sequence of DEL Chr1:21527167 is shown below, and the mutation type is deletion.

[0094] (SEQ ID NO:3)

[0095] TAAGATCACTGGAAACCCCTGGATGTGCAATCAAACCTGCTGGACACTGAACAAAAGTTACACATAAAAACCTTGTTAAAAACAAACT

[0096] ATAAACTCCTCATACCTACCAAGAAATCAGTACAATACTGCAATTCAGTATTACAGCTACTTGCAGGCCCATATCTAAAAACAAACAA

[0097] GGCAATAAGCCTCTATTTAACAGGCAAAAAGGCATTTTTAAAACAGGGACCAACCTGAAAACTCACTGGCATACAGCCAGCACACGAC

[0098] AGACTGGAGCAGGGCCGTGCACAGACATTTTGAGGAGCAGTGGCCCAAACCAAAAAAAAGGGGGCAAAAGGAGGGTGGGTGCTTAATA

[0099] CAAATTATCCAGTTGTCACAAATTTTCAATTAAAAGAGAAGATCGCACACTCTGTAATAACTGACTTTATTGAAGATGTTGGCCAACA

[0100] AACTCCGTTTAGTCTGTTTTTAGGAAGGCTACAACTTTGGTGTTAATATATATATATATATATATATATATATATTGTTTCAGAAATG

[0101] CAAAAAATTAAATATTAAAATTTAAACTTCATTTGGTTGGTTTTATGTTGTTAAAAGTTAACTTTAAGAAAAGAAATATGACTGTCTG

[0102] TAAAAGGTATTAGTTTTTAAATTTTATTTCACATGGGTTTGAATCAGATTCGGTCAGATGGTCATTTTACATTCAGATGGTGCATCTT

[0103] ACCCACTGACTCGACTCGGGTCAACTTACCCCAAACCCGGGGCAAACTGAGCCATGGGAACTTTATAAGAAGCCATATTTTTAGAACC

[0104] CTTTGTCTTAGATACATTCTGATCATTGAAAATGATAGGAATACTTTCATTTGGATAGGATAGATAATTTCATTGTACTTATGCGTTT

[0105] TTTTCGACTCATTACCGACAAAAACAAACAAACTAATTACTAATTCTACGGATCAACCACTGACTAATGTGGATCGTAATGCATCTAC

[0106] CTGGTAATGCAACGACCCGCGGGGACTGTGGTCTCCACTCTCCAGAGAGCGGAACACAGATTGTGAGTGTTGCTGCGGAGCGAGCGGG

[0107] AAACATGGAGCGAAATCAGCGGAGCGGCAACCGACAGCTTTGAGGTGCGGTGATGGGGGATTGGTGAGAGGGGCACTTCAGCCGATGA

[0108] GGACAAAGAGGCAGTGGCCCCAGCCACCGGGCCACCCCCCTGTGCACGACCCTGGGCAGGAGGATAAACCATCTGCTAGTTCTCATTC

[0109] AAAGCGCCACATGTCACAAAGGGGTAGTATCAAGAATGATGCATGCCAAGCAACCCAACACTTCCCCCTTTTATATGGACAACACAAG

[0110] CACTAATTGACTCATGAAACTTACCCAACTATATATATATATATATATATATATATATATAT

[0111] (SEQ ID NO:4)

[0112] CTTCAAGAAATCAGTACAATACTGCAATTCAGTATTACAGCTACTTGCAGGCCCATATCTAAAAACAAACAAGGCAATAAGCCTCTAT

[0113] TTAACAGGCAAAAAGGCATTTTTAAAACAGGGACCAACCTGAAAACTCACTGGCATACAGCCAGCACACGACAGACTGGAGCAGGGCC

[0114] GTGCACAAACATTTTGAGGAGCAGTGGCCCAAACCAAAAAAAAGGGGGCAAAAGGAGGGTGGGTGCTTAATACAAATTATCCAGTTGT

[0115] CACAAATTTTCAATTAAAAGAGAAGATCGCACACTCTGTAATAACTGACTTTATTGAAGATGTTGGCCAACAAACTCCGTTTAGTCTG

[0116] TTTTTAGGAAGGCTACAACTTTGGTGTTAATATATATATATATATATATATTGTTTCAGAAATGCAAAAAATTAAATATTAAAATTTA

[0117] AACTTCATTTGGTTGGTTTTATGTTGTTAAAAGTTAACTTTAAGAAAAGAAATATGACTGTCTGTAAAAGGTATTAGTTTTTAAATTT

[0118] TATTTCACATGGGTTTGAATCAGATTCGGTCAGATGGTCATTTTACATTCCGATGGTGCATCTTACCCACTGACTCGACTCGGGTCAA

[0119] CTTATCCCAAACCCGGGGCAAACTGAGCCATGGGAACTTTATAAGAAGCCATATTTTTAGAACCCTTTGTCTTAGATACATTCTGATC

[0120] ATTGAAAATGATAGGAATACTTTCATTTGGATAGGATAGG

[0121] (SEQ ID NO: 3 represents the sequence before deletion, and SEQ ID NO: 4 represents the sequence after deletion).

[0122] The genome sequence of INS Chr1:23399634 is shown below, and the mutation type is insertion.

[0123] (SEQ ID NO:5)

[0124] ATGTTGATCAAAAACATACATTTACTCACAAACTGACCACTGACCGCAACTTTCAGACGCCATCGTTATTTTTTGACTTAACTGTCAC

[0125] AGAATGGAACGCACAGGATAGTGGGATATCAATGGCAGCGAAGGATACATCTATGCTGCCTTCAAAAATCGGCCAGATGAAGGCATCT

[0126] CCGGAGACAGGAACTGAAACTAACACTGAATTCAGGCGTGCCTTGATGCCTCCTACCTTGGAATGCATCCTCCGAAGGCAGCATTTT

[0127] TCAGTTTTTTGGATGCAGCCTGAGACTGGATGAAGGATAATTTGTACATTTCTGTACTAAATAAAGAGTATAAACTATATCTTAAGAT

[0128] AAGTTAACATTTGAACCACTTTGATTAAATTATTTTTTTAAATTTATTATTACTTTATATAATTTCCTTCCTGATGGATACAAGTTAT

[0129] TTCTGAGTTCAGCTTTCAATTGTGACCAGTGGATTGGAAATATAAACTTGATAGCTTGATACAAATTAAATTGATAGTCTAATAAATC

[0130] ATGCATTCGCTACTGTTGTCTGCTCTGCAATGCTCTTTACCTTATCTCCAGTGAAGGGATTCTGCACACAAAAAAGAAAAGTCATGCC

[0131] CTGCATGAACATGAACATGAACATGCACCGTGATTAAGCGAGTGCTTCATTAAGGTTCCTGATGGATCTCATATCTATAAGATGAATT

[0132] TCTTTGTGCGAGGTTACAAATGTATATGTTCCTTTTCTAAGTGTGTCTCTCAAAAATGCACTTAACACCAAGACGCAAGTATTTTAAA

[0133] GGGATCGGGTCTTATCGGGTCCAATTGGGGAAAGCACATTTATTTTAAATTATCTGACACCTGAGGCAACGAATACTAAACCCATGTC

[0134] TGAGGCACTTGTCAATGTTTTGGACCCAAACCCGCTCGGGTCTCAGGTCAGACTTCAGGTTTTCGGATCCAAGGGGACCCGTGAAGAC

[0135] CTCTAGTGTTCACGTGAGAGTAGACCTTGTATTGTGAACAAGCTTTGGATAATATAATTTTGTAAATAAAGTTTTTTTTTTTTTTTCA

[0136] TAAAACTGAGGTTAAACCACAGGAGTCACATGGATTAATTTCACAGTGTCTTTCTTATCTTTCTGGACCTTGAACTTGTCAGTTGCGT

[0137] TGCTGTCAATGGAGGGACAGAAAGCTCACGGATTTCACTAAAAATATCTTCATTTGTGTTCTGAAGATGAACGAAAGTGTTATGGGTT

[0138] TGAAATTATATGACGTTGAATAATTGATGACATACAGTAAGTTTAATTTTTGGGTGAACTAACCCTTTAATAACATTTTACGTGTATT

[0139] TACACACAGTTTTTGTTTTGTCTGTGGGACAGACTGTTCCAGCAGGCTAAACTGGATTCTGGAAAGAGCACGTCTTGAGTTTGAGATGA

[0140] AGCATCAGGAAGCTGAGGACAGGCTTGCTCGAGGTACCCACCAATTCATGGTACAGATTCTGCAAATGCTTAGACCAACAGTGCCAC

[0141] (SEQ ID NO:6)

[0142] ATGTTGATCAAAAACATACATTTACTCACAAACTGACCACTGACCGCAACTTTCAGAGACGCCATCGTTATTTTTTGACTTAACTGTCAC

[0143] AGAATGGAACGCACAGGATAGTGGGATATCAATGGCAGCGAAGGATACATCTATGCTGCCTTCAAAAATCGGCCAGATGAAGGCATCT

[0144] CCGGAGACAGGAACTGAAACTAACACTGAATTCAGGCGTGCCTTGATGCCTTCCTACCTTGGAATGCATCCTCCGAAGGCAGCATTTT

[0145] TCAGTTTTTTGGATGCAGCCTGAGACTGGATGAAGGATAATTTGTACATTTCTGTACTAAATAAAGAGTATAAACTATATCTTAAGAT

[0146] AAGTTAACATTTGAACCACTTTGATTAAATTATTTTTTTAAATTTATTATTACTTTATATAATTTCCTTCCTGATGGATACAAGTTAT

[0147] TTCTGAGTTCAGCTTTCAATTGTGACCAGTGGATTGGAAATATAAACTTGATAGCTTGATACAAATTAAATTGATAGTCTAATAAATC

[0148] ATGCATTCGCTACTGTTGTCTGCTCTGCAATGCTCTTTACCTTATCTCCAGTGAAGGGATTCTGCACACAAAAAAGAAAAGTCATGCC

[0149] CTGCATGAACATGAACATGAACATGCACCGTGATTAAGCGAGTGCTTCATTAAGGTTCCTGATGGATCTCATATCTATATGTAACCAG

[0150] GTGGAAAAGATGTTGTCAAACATAAAACTGTTTTCTTTACACCCTGGTTTTGAGTGTACTGCGCATAGATTGGTTTTCATGTCTTGCTC

[0151] CCTGTTGCCATAAAGTTACACTGGCCCTTTAAGAAACACGGCTGCATGAGTCTCGTGAAGTGCCGCGAGTGCTGCAGATGAACAGCGC

[0152] GAGTTATCATTGCAGGGTGAATGTTCCACGTGAAGTGGAGGATCGATGACATTAAAATCCATTAAATCACAGAATATAGAGAAATATG

[0153] CACATATAGTGAGTTATAAACTCGTTCTCTGTGGTATTGTGACTCGGATGGCTGTGTAACTTTGCATATCGACGGTGATTGCTGACGA

[0154] ACATGTCAGGGTGGGGAAGCTAACCGTTTTTCTCCGAAGCGTAAGTGTTGTTAATTTAATCTGTATCAATGTACCGCTGAAAAGTTCA

[0155] CTTGTGCATAATTTAATGCAAAAACAGTGTTATTAATGCATAGCTATTTTATTTAGAGGGGAGAAAAGTGCTCCATATAGACCCTTTG

[0156] TGTTTTCCGGGGCGAGTGTAAAGCATTCAAGTGAGTGTTCACTTTTATTGATTGGAAAATTGTTTAATAGTGTTTCACTGGAATGAGTG

[0157] TAGTGGTATTATTGTTCTAACGTCATATGTTTCAAATGTTAAGGAAAAGTTTTGTATTTTTTCCCTTTATCATCCGCAGAGGGAATC

[0158] AAGTCAGCATAGGCTGTTGTTAACATGTGCTAGAGTGGTGATGAGCTGTAAGTTTGAATCATCAAAATTCACTTTTGAAGTCCTATGAG

[0159] TACTGATTTACTAAAAATGCAACTTCTAATTTCCTTTGCATGTACTTTTCCTATAATTTGAGTAGCCTTATTTGGTTTGACAATTTTG

[0160] ATTTATTGATGGCTGTTACACCTGGTGTTTTTATTTGTTTTCCAGCATAAAAGGCTACTACCGTTGTCTATGTTAGCTGGTCATTGTGA

[0161] ATTGTGGAAAAAGGCAAACTCATTTCATTGTGAAGATCAAACTTTATTGTTCGCCGTGGCTGATTTAAGTTTTAATTCTCTGTCCG

[0162] AATTCCCAAAAATTTGCATACTTAATGCTGTGCTGTTTCAAGTGCAGTCTGGAATAAAAGGCCCCATTTAAATTTTCATCCTGTGTCC

[0163] GGTGTTACCAATTCATATTCCGGGCCACATAGATGAATTTCTTTGTGCGAGGTTACAAATGTATATGTTCCTTTTCTAAGTGTGTCTC

[0164] TCAAAATGCACTTAACACCAAGACGCAAGTATTTTAAAGGGATCGGGTCTTATCGGGTCCAATTGGGAAAAGCACATTTATTTTAAA

[0165] TTATCTGACACCTGAGGCAACGAATACTAAACCCATGTCTGAGGCACTTGTCAATGTTTTGGACCCAAACCCGCTCGGGTCTCAGGTC

[0166] AGACTTCAGGTTTTCGGATCCAAGGGGACCCGTGAAGACCTCTAGTGTTCACGTGAGAGTAGACCTTGTATTGTGAACAAGCTTTGGA

[0167] TAATATAATTTTGTAAATAAAGTTTTTTTTTTTTTCATAAAACTGAGGTTAAACCACAGGAGTCACATGGATTAATTTCACAGTGT

[0168] CTTTCTTATCTTTCTGGACCTTGAACTTGTCAGTTGCGTTGCTGTCAATGGAGGGACAGAAAGCTCACGGATTTCACTAAAAATATCT

[0169] TCATTTGTGTTCTGAAGATGAACGAAAGTGTTATGGGTTTGAAATTATATGACGTTGAATAATTGATGACATACAGTAAGTTTAATTT

[0170] TTGGGTGAACTAACCCTTTAATAACATTTTACGTGTATTTACACACAGTTTTTGTTTTGTCTGTGGGACAGACTGTTCCAGCAGGCTA

[0171] AACTGGATCTGGAAAGAGCACGTCTTGAGTTTGAGATGAAGCATCAGGAAGCTGAGGACAGGCTTGCTCGAGGTACCCACCAATTCAT

[0172] GGTACAGATTCTGCAAATGCTTAGACCAACAGTGCCAC

[0173] (SEQ ID NO: 5 represents the sequence before insertion, and SEQ ID NO: 6 represents the sequence after insertion).

[0174] The genome sequence of DEL Chr7:2062567 is shown below, and the mutation type is deletion.

[0175] (SEQ ID NO:7)

[0176] CTATGTTTTTGGGGAAAAAAAAGAAGTCCAAAAAAGTATATGGACACTTTTGTTATTCTAATAAATGTGTGAATGTGATTGCATCAGA

[0177] TAAAAACATAAAACCACAATGCGGTATCCTCTCTTCACTAACTTCACTTTTTTCAATATTTTTGCTCAGTGTCTTTTAATCAACTTTT

[0178] GTTTTGTTAATTTTTAGTGAATTTA

[0179] (SEQ ID NO:8)TGGACCTCTTTTTTTTTTCCCAAAAACACATTCTCTTAAGATG

[0180] (SEQ ID NO: 7 represents the sequence before deletion, and SEQ ID NO: 8 represents the sequence after deletion).

[0181] The genome sequence of INS Chr31:9671978 is shown below, and the mutation type is insertion.

[0182] (SEQ ID NO:9)T

[0183] (SEQ ID NO: 10)

[0184] TTCAATTCAATTCAATTCAAGTTTATTTGTATAGCGCTTTTTACGATACAAATCATTACAAAGCAACTTTACAGAAAATTAAGTTTCT

[0185] ACAATATTTAGTAGT

[0186] (SEQ ID NO: 9 represents the sequence before insertion, and SEQ ID NO: 10 represents the sequence after insertion).

[0187] The genome sequence of INS Chr40:9140647 is shown below, and the mutation type is insertion.

[0188] (SEQ ID NO:11)T

[0189] (SEQ ID NO:12)

[0190] TGTAGTGTCGTGTCTGCTGCCATGGATCTGAGGGATCACACTCAGTCTTGGGGTTTTGTTTCCAGAAGATAGACTTTATTTCAGAGAG

[0191] AAACGAAGCCGAGATGTTCTCTGCGAGAAGATCTCCAGAATGTTATGTGGTATCTCCTTATATACCATATACAGGGCGTTCCCAATAT

[0192] TCCATAATTTTCCTTAGGCTTACAATTCGATCCCTAGAGAGAAGGGGCCCCCTATTTGGTCTGGTAAGGAGAAAGGCCCTTTTGTTGG

[0193] TCTCCCCAGATGTTTCTCATCTGGGTCTGGACATTCTGGTCACGTAGGCATCTTTCTTCATATACTACAACCATAGGATTATAATGGA

[0194] ATAATAACTATAGACATATATGGCTAAATTCACATTGATTATAGAAAATCTTATTAATAAAAATCTTCTACATTTCCCCCCTCTGAGA

[0195] CTTAGTAAGTCTCATATTTGATTATTTTTATTCAGAGTGCTACTCCATAATCCAGTCTTATTAGTTAACACTACCTAGTGACTAAATA

[0196] AGTCACACTGTATTATTACCAATGACTAGATTATGTAATTTCACTCTGGGGAGCAACAGGACCAAACATCTCCTTCCACAGTTGGCTA

[0197] GGAGGGAGTAAAAGATCCAGCCTCCCTAATGTTATAAGCGTGAGCGTGTAATTGGTTCAGCACTTGCCTGTGATTGTCACAATTGTGT

[0198] ATAAAAGACATAAAAAATACATACAAAACATAACCCATTGAATATCACAAGATTATAATAATTGATCTTCAGCCCAGATACTCTATGT

[0199] ATCGTAGAGGGCCACCCTGGGCAGAAGTTAGGCTAGTACTTACACCCAGGGTATGGTTCATCTTTAGACCTCTTCATCGTCCTCGTTA

[0200] GAGTCACTTGAACTATCATCAAAAGTTTGTTCATTTAAATTTAGTTTGTTTAGCCATCCTTCATAATATTCCTTTAGGCTCCTCTCTG

[0201] TGATAATTTGTTGATTATTTGGGACTTTTATCACTTCCATTTGCTTAGCCGTAGCGTTGACGATTAATGTTCTCAATATAGGTAATAC

[0202] ACAACAAAATAACAATCCTCCTACTAATATGGCAACTCCCAAAAAACATTCCCAGTTAAGCAAACCATGCTCCCCATGCTCTAAATTT

[0203] CAAATCAATCCAATCCCAAATTTGTTTGTCTCTTCCGGCATTTGCTGTAACTTCAGCTCTTAATTTTTTTAATTTTAGTCATAACTTC

[0204] ACTAAAAGCTCCCCCAGGGGCAGCATTATTTGGTATAAAAGTACAACATTGATCTCCGAACATTGTCTGCCAAAAGCCAGTTAAGAGC

[0205] CTGTCTGTTTTGCCATGTCATTCTACTTGTTGCGTGTACTTGGTCTCCTAACAAGGTTAACGCATCATCAGTATAGTTAATGAATCTT

[0206] TCTTGATTATAATAAATGTAATTAATCCACTCTGCATTTTTATTTGGTGTGATCCAAACAAATATTGACTCAATAACCTTTTTATTTC

[0207] ATCTCTTGCCTTGAATTCATTAGGAATTCCTCTCGGTTGCCCAATTGAGTCTAACTATACATTTGGGTCTGTCTCGTAACCTCTTTTA

[0208] ATTCTGTTGTCTTCCCTACTTGTGCTTGATTCTGTTCCCCATTGTGCCATGTTAACTTCTTGAAGTAATCGTACTCTTACACATATAC

[0209] CTTTCCATCCTACTGTTAAAGAAGGCAGTAATATCTCACCTCCTCAGATCCAAAAGAAATCTGCTATTATTAATGATTGGTCTTCATA

[0210] AATTTCTATTGAAGGTAATGCAATAGTTTGATTTTCACATTCTTCGGGCGTTATTTTACTTCCGTTAGTTGTTCTGAGTGCTAACAGT

[0211] TCTGGAGCTCTAGAAGGAATTCCTGATTTCCAAGAATTTTTCTTATCTATGTACCAAATAATAGCACATTTTCTTTTAAATTTGCCCA

[0212] CATCTTGTGTTCCTTTTGGTTTATGGAAACACTGATATTCCTGTTTATAATCTATGCTATACAATTTTGGAATCTCTACTTTCGTTTT

[0213] TCAATTTTTATATTTTGGCTAACATCTGAATACTGACACCAAGATCCCCAGAGTGGTCTAAAAAATAATCTGTTAATTTAGGATGGTA

[0214] TGGCTGTTACTTTGTTTAACGGAGATTTGGAGCACAGCAAGCAATTTTTCTTTCCTGTTTCTCTAGCAGTAAAGGCTGCCCATTTATA

[0215] CCATTCATTATTTTGGGCTGTATCCCATAATGCATCAATATGATTAATGTCTACATCACCTTGAACATCAAAATCAGTGTTTCTACGA

[0216] TTTTTGATTATCATTGGTTCTGTTGGGTTGATATCATTGTCACTTTTGTTCAAAGGTTGAAAAGTCAATTGAAGGGAAGTCAGATTGC

[0217] TTTCCATTGGTTGAGTTTGGGTTTGCATTATTAAATGCATTAGGCATAGGATGGTCTTCAGACATGTTGTCAAATTTGTTCTCTGTCC

[0218] TCTCCAGTTCGTCTGACAGGAGGAAAGGTGCTCCTTGTGGATATTTCTTTGTGCTCTCGTCAGTTTGCACTTCATCATTTGTCTCTGA

[0219] TCTTTCCTCGTTACTCTCTCCTGCATCGTCTGGTTGTCTGTTTTCTTTTCTTTCTGCCTTTCCCATGGTAACTCTAGTACAGTGGTTT

[0220] AGATGATACCAGGTTGTGCTTCCCTCCACTTGGACTGCTGTTGGAGTAGCTCTCACCACTTTGTAGGTTCCTTCTCTCCTGGGCTCAT

[0221] TCCACTTTCTCCGGAATACCCTCAGATATACTTGGTCACCTGGAACAACTGGACAGGCAGTCTCCAATTCCTCTCTGGGCTCTTTCCT

[0222] TTTCTCCTGTAAATAGATAACTTTATGCATAGCAGTTAATTTCTTCATATTATATCGAAGTTTCATTTATAATTGCTCTAGAGGCAGT

[0223] CCTTTGCAAGGACCTTTGCAATATGGCACATGCATGGGTCGACCCGTAAGCATTTCATGCGGTGTTAAGTGCATGTTCCTGTTAGTTT

[0224] GCATGCGATAGCTTATTAGTGCAAGAGGTAAAGCATTAATCCAATTAAATTTTAGTACTTTGACATATTTTGTTTACTTTGGCCTTGAT

[0225] AGTACTGTTTAATTTTTCTTATCACTTCTTTCTGTTTCATTTGTTTAATTTTCCTTATAATTTTCCTTATCACTTCCCCCCTTGCGCA

[0226] ATGGTCAAAACCATGCGCATCTGCAATAAGAATATTCAAAAGTAGGTGCAATAATGTTTCCTTCATGTGTAGCCAGATCTCTTGTTA

[0227] GTCCTTTTTTAGCTTCCCTTCTGTTACACATTTATTGTTCATAGGGGCCCGCTTGTTGTTTAATTCGAATTATATAATCCAATTGAGG

[0228] TTGAGGTTCAATATGTACTACAGGTGCTAATATGGCTACCTGACACCCTAAAGCTTTTTTATCTTCAACATCCAGTGTAATCTTTCCC

[0229] TTACTGACACTGTTTACCTGTATATGTAAACAAATACCTTCTCTCTTCTGCAAGCAGCACACTGTAGTAAACTGAATAAAGATCAGTT

[0230] ACGATAAAATATTTGGCATCAGTAGGGACATTCATCAGTAATGTGTGCGGGTTTGGAACTTCAGCTGGCAAATTATTTCATTTATTAT

[0231] TTATAAGTCTTTTTCATCGTTCATCCTATATTTTTTGTTTCCCATTTTTTTTCTACATATTCTGCAATATGTCGGACAGAATTAGTTA

[0232] TTAAAAATATTAATTTTAACAAATTCACCATCTGGTATTTATAGCTGCTCCATTTACCTGTTATGATGTACTTGGAGACACAATTTGC

[0233] TGTCCTTTGGTTTCTTTCTGCCATTTTTGTTCTATTTTCTCATCTCTCTGTTAATCATTTATCATTGTGCATTGAAATTCTGATTTTA

[0234] GTAGCTGTGCTTCAGGAATCTGTGCTTCAATAAACTTTCCCCATTTATTGACTCACATCTTGTTCTATCTGTCCTATCCAATAGACAT

[0235] TTGATTTTGGCTCTGCAATTACCATTTGTGTTTCTTTTTCCTATTGCAGCAATATATATTCCTTCTGTAGAACACCTAATTTGTAGCCC

[0236] TAATTTGCATAATGCATCTTCTTCCTAACAAATTGATAGGAGTCTGATTTGATACCAGAATGGGCATTATTATACTTTGATCTTTTGTT

[0237] TGTAGACACACTAGAGCTGTTATTGGAATTAATTGCATTTTTCCCTGAGAATCCTATTGTCTTTGCAAATTTTTCCAGACAAGGGGAG

[0238] ATGTGAGACATATTTTAAATTTAAACAGGTATAAAAACACCTCTGGTGTGTCTAAAATCATGGGTGTGTTTTTCCTTCTATATTTTAATTT

[0239] GCATAATAATCTCTTGATCAGCTTTAGTTATTAAAATTGGAAGCTGACTCTCCCCTGTAAGATTCTCTGGGCACCCCTAATAACCTGA

[0240] ATTGGGACCTCCCCAAGGGTTTACTGGGTGCTGTACTGGACCTTGGTTAGGCTGTCGCCAGCTCTCTCCCTTCCACACTGAGGGTACT

[0241] TTTAAGGTGGATAGGTGGGATATTCATTTTTATTATGTCCGGGCTGTTCCATCTGCCCAGCACACACCTGGAGGACCAGTATAAACCT

[0242] CTTCCTCTCTGGCCTCGCTGTATGGGTTTCTTTCTCTCTTCCATTGTCCTGGCTGTTGAGTATAAACATTAATTATGGGCACAGGAAT

[0243] CGGGCGTGCGTTTGGAGGGATTTCTGTAAGTGGGCTAAACAATGTTGCCATTAATGGGTCTCTTTTTGGATCCCCTTCAGTTTCTTGC

[0244] TTCCACCTTTTCAGCTGTTTCTGAACATATGTGGTTGGATTTTTCGTGTTTCCCAGCTCTTGTCTCTCAAGGATTTAGGATTCAGTCT

[0245] GATCGGGTACTCCACACACAGTGCCTGCCACACTGCTGGTCCGAATGCATCAAAAACAATCCCATCCATATTACGAGAGTTCACTGCT

[0246] CTGTTTAGAGTAGTTGTTTGAAGAATTTCATCCATCTTTGTCCCTCCTACAATTTTAGCCAGCAAAGCTTTATTGTCTCTCACAGCCA

[0247] AAAGTTTGTCCATAGTTCCTTTCTCAAACACCCTGATCCACTTTCCAGCCCCCTCATGAATATCTGGGAGGCGATTGTCCAGCCCCTC

[0248] GAGGTCCTGTGGAGCCCAGGGGACTTACTGTCCCTGCCCACCCTTGATTAAAATTGGGAGGTGTGCAGCCAGTGGTCTGAATGTCCCC

[0249] TTTTTCTCTGCCCTGTTCCCATTTTCTGTTATCAAATAACTCATCTGCTTTATGTATATCCTCATTACTATAGAACAACTTACTTTTT

[0250] CCTCTCTCTGACCTATTTACCTCCTGTATCTTTTTTCCTGATTCTTTGATTTTCTCTTCCATTTCTTCTTCCATAGCTTGTACTTGTG

[0251] CTTCTATTTCTATTTATTATCTTTACTCCTTATTTTTTCTAATCCTTCCTCAAACCTGGGTTTATCTTCCTGGTTTGCTTTCATTTTT

[0252] TCTAACGCTGTACGTGCCTGTCTGTAACAGTCTGTAAATAGCCTTTACTTTTCTTTCTGTCTTGCCTCTGTGTTTTCACAGCTGGTT

[0253] CTCTCTACCATCCATCTCTATATGCCCCTCCATCTCCAGTTCTCCTGTTACTTCCACTGTTCCCTTGAGAATTGGGAACTCTACCTCT

[0254] GAGGGGCAAATAGGGAGGCTCAAAAAATACTTTTCCCTTCTTCTCTGTCTCTTTGTCTGCCTGTTTAGTATTTTCCTGGCCTTTTT

[0255] TAAACTTTTCAACATATTCTCTCCTTCCTTCTTAAACATGGCTATTACTTATTTTTCTCTTTTTCCCTCTTTGCTTCTCTTTTTCTC

[0256] TTTTTTTTACTGATGCTTTCAGTTTTGTAGTTTTTTATTTTACCAGAGTTTCCATCTGCTCACACAATGCTACATCGAATGTCCCTTC

[0257] CTTTTGCCATTTTGTCATATTTTTGTTCTCTTCTGCCATTTGTTTGAGATCTGTTTATTTGCTCTTTACACAGTGGATATTTTAATC

[0258] CTATTATTTCCTGCCATATCTGAGTGGCTGTCATATCTTTTCTTATAATATTACAATCCAATATATACTTTTTTAATTCTGTATATAT

[0259] ATTTCTTTGTTTAATTATCTATTTTCATATTTATATATTTATTCATCTATCAACTTATCTTATCTATTTTTAACCAGTTTTTCCATTT

[0260] TTTTTTTCTTTATTCCTTAGTCTTTATTCATGCCTCTTTTGTATTTAGTTATTAATTCTGTTTGATGCATGTACACTTTTTGTATAAAT

[0261] TGATAATCTCAACTCTATTTTACTATCACTCAGTATGTATTACATTTCTAAAGATAATTCTTTATTTATCTTTACTTCTCTTTATTGT

[0262] TTTGTCGGCACTTATATTATTTTTTAATATACTTATTTAACTTAATGCCTTGCTCTCACTTCTACTGCTCTGGTCTCACAAAATTTCT

[0263] CCTTCTCTTTCACACGGACATCCAAAATGCAAAAGCCTATAAAAATCTTGATTTAAATTTATTGATTCTATTTTCCTAAACAAATTCA

[0264] CTCCTTCAAACCATTTCATCCTTTGTCCCTTCATCCTATAGGGCTCAATCTCTCATACTTTTGTCCCTTAATTTAATAGGGCTCATC

[0265] ATTCAATCATCCCTCTCGAGTGCGGTAGTTGCATCCAATCCTGCACGGCCACACCTAAGTGACGGTCCAGGAACGGTGCACAGCTTTT

[0266] ACCCACTCAATACGGAATTTATTTGTTCAAACATTCTCATCACTCACACAGACATTCGTCCGGACACAGTTTCACACACACAACAACA

[0267] CATAACACTCTTCCCTAGAGTGACCTGTCAGTTCTACTCGCTCTTTCACTGATGCACTTCCTCAACACAGTAAAAAAGCAGTACCCTG

[0268] CAGGAATTTATGCCTTTCCGTGCTGCCCTCTGTATCTCCCTAACAACCTCTACAGAGCGTTATCCAAAGGACTTTAACGCGCGATTTT

[0269] ACATCTGTTTACCGTTTATTGCTGTCCTTCTCTAGACCAATCTTCAATAAACACAGACCCTTACATGCAATGCATTTATGCCTACACC

[0270] ATTTAGTCTTAATTTAGGTCACCTATCAAGGCCTTTCTAACATTATATTAAATATGTGCATGCAATTTTTCTTCTGGAAACAAAACCC

[0271] CCCATTTTCATTTACTTATTTAATTCTAAATTTGGAAGAGCAGATTTTAAGTGACCTTACCACTCAGAACTGACTGTTCAACAACACA

[0272] CACTAATTATATAAGCAAAAATGCTTACCTTATTTTATGGTGGCCACCACTGTGTGCGTGTTGCTCATCAGTCAGTTCAAAAGCGGTC

[0273] GTCCACTCTGCTGCTCCTTTCCAGCCCCACGTTGGGCGCCAAATGTAGTGTCGTGTCTGCTGCCATGGATCTGAGGGATCACACTCAG

[0274] TCTTGGGGTTTTGTTTCCAGAAGATAGACTTTATTTCAGAGAGAAACGAAGCCGAGATGTTCTCTGCGAGAAGATCTCCAGAATGTTA

[0275] TGTGGTATCTCCTTATATACCATATACAGGGCGTTCCCAATATTCCATAATTTTCCTTAGGCTTACAATTCGATCCCTAGAGAGAAGG

[0276] GGCCCCCTATTTGGTCTGGTAAGGAGAAAGGCCCTTTTGTTGGTCTCCCCAGATGTTTCTCATCTGGGTCTGGACATTCTGGTCACGT

[0277] AGGCATCTTTCTCATATAACTACAACCATAGGATTATAATGGAATAATAACTATAGACATATATGGCCTAAATTCACATTGATTATAGA

[0278] AAATCTTATTAATAAAAATCTTCTAC

[0279] (SEQ ID NO: 11 represents the sequence before insertion, and SEQ ID NO: 12 represents the sequence after insertion).

[0280] The genome sequence of DEL Chr48:8724756 is shown below, and the mutation type is deletion.

[0281] (SEQ ID NO:13)

[0282] ATGCTAAAAAAAATGATGAGAAGTATGTGGTTATGTCAGTGAATGGTGAGAGTCAAAATTTCTGAAACTCTTATTACAAAAAAAAACA

[0283] AAAAAAAAAAACATCCTTGAATGCCCAGAGAAGTTCTGTTGCTTAGGATCTTCCCTTTCATTGCTCAGGAGACTTAACAGAGCATAGG

[0284] GTTTGTGCTTCTGAAGCTTTAAAAAATGTAAAAGG

[0285] (SEQ ID NO:14)CAGTAGTGT

[0286] (SEQ ID NO: 13 represents the sequence before deletion, and SEQ ID NO: 14 represents the sequence after deletion).

[0287] The genomic sequence of INS Chr50:4154584 is as follows, and the mutation type is insertion.

[0288] (SEQ ID NO:15)

[0289] TTTCCTATCAAAATGCTTGCTACAGTTGCTAAAAATGCAGAAAATCGTACCTGATCTGAATTTTTTATGACGGACAAAAGCTTCGGAG

[0290] GCAGTGTGTAAACGTGATTAACAATGTGAGGTCGTATTTATTTTTTAATGTGCAAAGATTTCGGACTTAGTGTGCAATGACCTTTACT

[0291] GTTTACTAATGTGAGTAACAATAATTACTATAATTTGATATTTACGTTTTTATTAGTCATAATACATATATCTTAACATGTAAAATAT

[0292] CTATATATCTAATATAATATTTATATTAGAAATATATGAAAATAGATTAAAATAATAAAGTTAGAATTTTTTTTTACTTAAAGGCACA

[0293] AATTTTCGTAATAAAATATCCAAAACATAATATCCGCATATCCACATAGAAGAGATTGACTCATAAAACCGTATATAGACAGCTTTAC

[0294] TGATTATAAGGGGAGTTTTTCGAGAGTGCTTAAACTCGCGCTAGACCAAAGTCAGTCATAATGTTCTTTGACAATGGAAATGTTCTTT

[0295] GCTTGCTTTCTCTGTATAATTTATTGCCGTTTAAATAACTGTGGAAGGTGCAGGTAATAATAAAACAAATTACCCCATACTTGAACAT

[0296] CCTCCTTCACAGCATCATCAAGCTTTGCTTTTGTTATTGTTTTGAAAATGCGACTTCTAGCAGCGAAAACTTACATATTGTGCCTTTA

[0297] ATTTTTAATTATTATAATTAATATAGAAACAAAAATTCCACACACTATGCCAGCTTGCAAAAAATATCTCCCTGACATTTTATTAA

[0298] AAGCAAAATTTGATCAGAAATTGTATGAAACTTTTAATTAATTAATTTTTTTTTCATTAAATTAAACTTAAAATTTGTTTTATTAT

[0299] TTCGTAATATGTATGTAGTATATATAGTCTGAAATATAGTTTTTAAAAAAGTAAAATTTTATATATATATATATATATATTATATATA

[0300] TTGTATGCTGTTAAGAAAGGTAATTTTGCTTTGGAATAGGGGACAGATATTATTAATATAATATAATAATATTATTATTAAGATTT

[0301] TATCTTCTTTCTGATCCCTTTTGCACATGGGAATTTAAATTATCTAAAGAAGAAAATGAAATGTTTTGTGTTTTACCATGGAGCAATA

[0302] AAATAAAAAATGAAATGAAAAAATTA

[0303] (SEQ ID NO:16)

[0304] GTTTGACGGATGAAAAAGCTTTGCTTTTGTTATTGTTTTGAAAATGCGACTTCTAGCAGCAAAAACTTACATATTGTGCCTTTAATTT

[0305] TTAATTATTAAAATTAATATAGAAACAAAAATTCCACACACTATGCCAGCTTGCAAAAAATATCTCCCTGACATTTTATTAAAAGC

[0306] AAAATATAATCAGAAATTGTATGAAACTTTTAATTATTTTTTTTTTAATTAAATTAAACTTAAATTTTGTTTTTTATTATTTTGTAAT

[0307] ATGTATGTAGTATATATATAGAAGCAGATTAGTCTCAAATATAATTCACGCAAAAAACACGATTCACACATGCGTAGACAATTTCACA

[0308] TGCATGAAACCTAATTCACGTACACACAAAAAAAATTCACGTGCGTGAAAAAAAAAATATATTCACACAAAGCAATTCACATGCGCAA

[0309] AATAAAATTATATATTCATAAAATACATTTCACAAATGCAAAACACAATTCGTAGATATACAACTGTGCACAAACCTTTGAATGTT

[0310] TAAATGTACGAGTGTCTGAATGTACGAATCGTCATTTACTACGAATCCACTCGGATTTGTGTGTGTGTGTTTTTGAGACTTTCCTGG

[0311] CAGAGCTCTCTTCCCACGTGGGTCTGTCGTACTCTTTAGCCAATCAGATGCGAGCTTACCATTCAACCAATCATATCATAAGCAACCG

[0312] AGAGTGCATTCAAGGAGCACGATTCTGCGCACCTTTTGCGCAATGTTGATTAATTAGAACGGAAATTAAGCCTTTACATCAGTAATCC

[0313] CAAAGACAGTAAAAGAAATGGACACAGCGACCCAATTGGAACTCAATTGAGACAAGTGAAGCCAATTTTTAGCGATTTTTAGCACTTC

[0314] CGTTTCTGACGGCAGACTCAAACAAAGGAAGCTTCTGCCATTATTGTGACTTCATCTGAACATGTGCAAAACTACTCTCCTTGAATG

[0315] CACTCTCAGTGGCTTATGATATGATTGGTTGAATGGTAAGCTCGCATCTGATTGGCTAAAGAGTACGACAGACCCACGTGGGAAGAGA

[0316] GCTCTGCCAGGAAAGTCTCAAAAACACACACACACAAATCCGAGTGGATTCGTAGTAAATGACGATTCGTACATTTTTAAACATTCAAA

[0317] GGTTTTTGTGCACAGTTGTATATCTACGAATTGTGTTTTGCATTTGTGAAATGTATTTTATGAATATATAATTTTATTTTGCGCATGT

[0318] GAATTGCTTTTGTGAATATATTTTTTTTTCACGCACGTGAATTTTTTTTGTGTGTACGTGAATTAGGTTTCATGCATGTGAAATTG

[0319] TCTACGCATGTGTGAATCGTGTTTTTTGCGTGAATTATATTTGAGACTAATCTGCTTCCATACATATATAGTATGAAATATAGTTTTA

[0320] AAAAAGTTAAATTATATATATATATATATATATATATATATATATATATATTTGA

[0321] (SEQ ID NO: 15 represents the sequence before insertion, and SEQ ID NO: 16 represents the sequence after insertion).

[0322] The PAV sequences related to the linoleic acid content of carp are shown in SEQ ID NO: 17-22, wherein the genome sequence of DEL Chr20: 2258997 is shown below, and the variation type is deletion.

[0323] (SEQ ID NO:17)

[0324] ATTTTTTTAAATGCTTTTGAAGTATATAATAATATATAAAGCATTATTTTTATTTAATATTTTATAAATTACATATATATATATATAT

[0325] ATATATATATATATATATATATATATATATATATATATATATTTATTTATTTATTTATTTATTTTTACATTTTTGTGTATTTTTCCAT

[0326] CTTTTCCACTTTTTAGGAAAAAATGTGTTTGTTGTGAAAATGTACAAAAATTGTAATATTCCTTAAAATGGGCAACTACAATTTTTGT

[0327] GGAAACCATGATACATTTTTTTGTCAGATTTCTTGAATAAAAAGTTCAAAAGAACAGAATTTCTTTGAAATGGGAATCTTTTGTAACA

[0328] TTATAAATGCCTTTACTGTCACTTTTGATCAATTTAATGTATCCCTGATGTGTAAAAGTAATCATTTCTAATTATAAGTAAAAAACCG

[0329] TGCTGATTTCAAAGTTTTAAACAAAAGAGTACGTTATCTGGGAGTAATGTGACTAATTGAACCACTAACTGAACTACAGCGAGTAACT

[0330] GGACACGTTCTGATTGAACTCGGCAGCAGGTTACGTACGATCTTTCTGTTGGCCCTCCTCGTCCAGATAGATGTTGGTCTTCATGTTC

[0331] CATATGAACTGATGAGCGAGGAGCTGGGACTTCTGTGCGGCCCACAGGATGTACTCGCGCACGTAACCCATCTGTCAAAAACGCCAGC

[0332] ATAGATGTCAACAGCAGCCCAGGGCTGTGAGAGTCAGATCAAACCACTGCTGTGTTAAAAAAGGGAGTTCAGGAAAGGTCACAGTTCA

[0333] CTCTTCCTCACCTTGTCATACCGCAGCGACTGAACGATTTGAGGAATGTAAAATAAAATGGCGTCCTGTAAAAGAAACGCAAGTGAGA

[0334] AATCACAGAAACATTATAGACAAACCTTAATCTGTTCCTCACACAAGGAATAAAACGGGTTTAAACCATAATCACCCTTCAAAACATC

[0335] TCCTTTTGTGTTTCACAGAAGAAAGAAAGTCATATAGGTTTAAAACAACATTTTTTCCTCGATCAAAAGTGTCAAGGTCTAAATACAT

[0336] CAATGGAAACTGTAGCTTAAAGCATAAATGCTGTCAAATATCTGATATTGATTGACAGCAAGTTAATTGCTCAGCTGAAGTATGAAAC

[0337] AGGACATTTTTAAAACATGGTTTCTAGACCTGGAAAAGTCACAGGAATTATTTTTTTTTAATAAAAAGTCAAGTAATTTTTTAGTTAA

[0338] CATTTTTTTTATTATGGCAAACTCTAAAATATTTTATTAGTTAGAAATTGCATTTCTAGACCTGGAAAAGTCACAGGAATTAATATTA

[0339] AAAATATTTAAAATATATATTTAAAAGTCAAGGAAACGTTTATAATGATTTTTTTTTTTTTTTTTTATAAAAAGTCAAGTAATTTTGT

[0340] AGTTAACATTTTTTTTATTATTATGTCAAACTCTAAAATATTTTATTAGTTAGAAATTGCATTTCTAGACCTGGAAAAGTCATGGGAA

[0341] TTAATATTTAAAATGTTATTAAAACATTAAAAAGTCAAGAAAATGTTTATAATGAATATTGTTTCTAGTTATATCAAGCTGTAAAATA

[0342] TTTTATTGGGCAGAAATTGCTATTTAAAAAATGTATTTCTAGACCTGGAAAAATCCTGGGAATTAATATAAATTAATAATAAGTAA

[0343] ATGAAATTTTTTTTACAATCTAATTTTTTTCTAGTTATATCAAGCTCTAATATATTTCATTGTGTAGAAATTGCTCTTTGAATCATGT

[0344] GATTTGTAGACATGCAGAAGTCATGGGAATTAATAATAAGTCAAGGACTTTAAGGACTTACTTTTTTTCTAGTTCTGTCAAACTAAAA

[0345] TATTTTATAAAAAAAATAAATTGCTATTTTAAAATTGTGATTTCTAGACCTGAAAAAGCCATGGGAGTCAACATTAACATCGACATTTT

[0346] TATAATAAATCACTTTTTTTCTACTTAGCTCAAGCTC

[0347] (SEQ ID NO:18)

[0348] ATTTTTTTAAATGCTTTTGAAGTATATAATAATATATAAAGCATTATTTTTATTTAATATTTTATAAATTACATATATATATATATAT

[0349] TTTTCACATTTTTGTATATTTTTCCATCTTTTCCACTTTTTAGGCGAAAATCTGTTTGTTGTGAAAATGTAAAAAAAAGTCTTAATAT

[0350] TCCTTAAAATTGGCAAAATGCCTTTACTGTCACTTTTGATCAATTTAATGTATCCCTGATGTGTAAAAGTAATCATTTCTAATTATAA

[0351] GTAAAAAACCGTGCTGATTTCAAAGTTTTAAACAAAAGAGTACGTTATCTGGGAGTAATGTGACTAATTGAACCACTAACTGAACTAC

[0352] AGCGAGTAACTGGACACGTTCTGATTGAACTCGGCAGCAGGTTACGTACGATCTTTCTGTTGGCCCTCCTCGTCCAGATAGATGTTGG

[0353] TCTTCATGTTCCATATGAACTGATGAGCGAGGAGCTGGGACTTCTGTGCGGCCCACAGGATGTACTCGCGCACGTAACCCATCTGTCA

[0354] AAAACGCCAGCATAGATGTCAACAGCAGCCCAGGGCTGTGAGAGTCAGATCAAACCACTGCTGTGTTAAAAAAGGGAGTCAGGAAAG

[0355] GTCACAGTTCACTCTTCCTCACCTTGTCATACCGCAGCGACTGAACGATTTGAGGAATGTAAAATAAAATGGCGTCCTGTAAAAGAAA

[0356] CGCAAGTGAGAAATCACAGAAACATTATAGACAAACCTTAATCTGTTCCTCACACAAGGAATAAAACGGGTTTAAACCATAATCACCC

[0357] TTCAAAACATCTCCTTTTGTGTTTCACAGAAGAAAGAAAGTCATATAGGTTTAAAACAACATTTTTTCCTCGATCAAGGTGTCAAGG

[0358] TCTAAATACATCAATGGAAACTGTAGCTTAAAGCATAAATGCTGTCAAATATCTGATATTGATTGACAGCAAGTTAATTGCTCAGCTG

[0359] AAGTATGAAACAGGACATTTTTAAAACATGGTTTCTAGACCTGGAAAAGTCACAGGAATTATTTTTTTTTAATAAAAAGTCAAGTAAT

[0360] TTTTTAGTTAACATTTTTTTTATTATGGCAAACTCTAAAATATTTTTAGTTAGAAATTGCATTTCTAGACCTGGAAAAGTCACAGG

[0361] AATTAATATTAAATATTTAAAATATTTAAAAGTCAAGGAAACGTTTATAATTTTTTTTTTTTTTTTTTTAAAAGTCA

[0362] AGTAATTTTGTAGTTAACATTTTTTATTTATGTCAAACTCTAAAATATTTTTAGTTAGAAATTGCATTTCTAGACCTGGAAA

[0363] AGTCATGGGAATTAATTAAAATGTTATTAAACATTAAAAGTCAAGAAAATGTTTATAATGAATATTGTTTCTAGTTATATCAA

[0364] GCTGTAAAATTTTATTGGGCAGAAATTGCTATTTAAAAAATGTATTTCTAGACCTGGAAAAATCCTGGGAATTAATAAAATTA

[0365] ATAAAAGTAATGAAATTTTTTCAACTAATTTTTTTCTAGTTATATCAAGCTCTAATATTTCATTTGTGTAGAAATTGCTCT

[0366] TTGAATCATGTGATTTGTAGACATGCAGAAGTCATGGGAATTAATAAGTCAAGGACTTAAGGACTTACTTTTTCTAGTTCTG

[0367] TCAAACTAAAATATTTTATAAAAAAAAATTGCTATTTTAAAATTGTGATTTCTAGACCTGAAAAAGCCATGGGAGTCAACATTAAC

[0368] ATCGACATTTTTATAATAATCACTTTTTTCTACTTAGCTCAAGCTC

[0369] (SEQ ID NO:17 represents the pre - deletion sequence, and SEQ ID NO:18 represents the post - deletion sequence).

[0370] The genomic sequence of INS Chr25:3825897 is shown below, and the mutation type is insertion.

[0371] (SEQ ID NO:19)

[0372] AGCCACGCAGCGGAGCATCTGCTTTAAGGTTTTGTTGAACCTTTCAACCAGCCCATCGGTCTGCAGGTGGTACACAGTGGTCCGAAGC

[0373] TGCTTGACCTTTAGGAGGTGGCAGAGGTCAGCCATCTTCCGGGACATGAAGAGGGTGCCCTGGTCGGTCAGTATCTCCCGGGGTATGC

[0374] CGACCCGGCTGCACAGCAGGAAGAGCTCCTGGGCGATGGCTTTGGACGTGGCTTTGCGCAGTGGAATGGCTTCGGGGTACCGGGTCGC

[0375] ATAATCCACGATGACTAGAATGTGTTTGTGTCCCCGGGCGGACTTTGGCAGCGGCCCTACGAGGTCCATTCCTATCCGCTCAAAGGGC

[0376] ACCTCAATGATGGGCAGCGGGATTAGCGGAGCTGGGGGAGGAGTTTGTGGCGACGTCCTTTGGCAGTCCGGACAGGCCTGACAAAACC

[0377] GCTTGATCTCCGCCTCCATGCCGGGCCAATGGAACCTGTCCCGGACTCATTGAGTGGTGTTTTGCGCCCCCAGGTGGCCCGCCATAGG

[0378] ATGGGAATGGGCGAGCTCGATGATAGTCTGTGTCTTTGTGCGTGGGACAGGCCGTTCTGAACCACGAAGTGTGGGGTTGGGTGTGGTG

[0379] GCGGTTGGACCTCTTTTCCCTCAACCACTCTCACCTGCTTCCAGCAGTGCTTCAGCCGGTCGTCCCCAAGCTGCTCCTTCATAAAACT

[0380] TCCCCCACCCCGTACCTGCTGGAACACATCATAGAAAAGGTTAGCACTTTGGGGGGGGGGGGGCTCACCATCTCGGGCGCTGTCCGAA

[0381] GCCAGGAGGACAGGGCGCCGTCGGGGACCCCGGCTGACGTTCTTTGTCTTCTGGCGGTTCCCGGCAGGGCTGGCAGGCCGGGTGACTT

[0382] GGGTGAGCAGTTGGTCCAAGCCTGGCCAGTCTCTCCCCAACAGGACGGGCATGGGCAGGTCCCTGGTAATGGAGAGGCGAGCTTTGGT

[0383] TCCCATCCGTGGGGGAAACAGGGAGGACTGGACCAGGCTTACCGCACTTCCAGAGTCCAACAGGGCTGTATACGGTCGGCCGTTAATG

[0384] GAAACCTCGGTCTGGGGTGCCCCTCGTGGTGGCTTCAAATGGACAGCACAGCCTGCCAGCCAGGCACGATTGGGAGAGGGTGGTGGCT

[0385] CCGTGGGCATCGGCTCATCCTGTATGCCGGTGCCACGGTGCGGTAGGGCGCACAAATACGGAAATACACGGAAATTTAGTGCCAAGTC

[0386] CAGTTCAGCGTCGTCTTTGATTATCATAAAAGCAAATCTCCTAAATGAAAAAATATGTTTCAATAAAGGAGACTCGCACCCGATTGCA

[0387] ATCTTTTTAATCGGTGAAACCAACTTTCCATAATGTGAAAGCACCTGCATCACAATATTATCACTAAGGAAAGGGGGGATATTCAATA

[0388] TTGTGACTCTCTTTGATGGTAACATGAGAGGCAAAACAGATGTAAGTATTTCATCAATGATTATACTTCGCGCAACCACCTCATTTGC

[0389] TCTTTCTGCCGTATTCAAAAAAAATCATGGTCGTGTTATTCATTCTGGATGCAGAGAGAATATTTTGATGGCCAACAACTTCTCCTAC

[0390] ACCCAGACAGCACACGGATACTATCTTGACTCTGTGACGCAATGTGAGGGAATCAAGCCCATTATCACCCTGAGCAAACAAAACACAC

[0391] ACAAGATAAATATTAAATCAAATAAACAAACTTAGACGTTAAAAAAAATTAAATAAAATTTACATTTACGCATTTAGCAGACGCTTTT

[0392] ATCCAAAGTAACTTACAGTGCATTCAGGCTAACATTTTTTGACCTAACATGTGTTCCCTGGGAATCAAACCCACTACATTTTGCGCTG

[0393] TTAACGCAATGCTCTACCATTGAGCCACAGGAACACACAAAATAAGGAAACACACAAAAAAGCAAATACACGCTCTCACACGCACTCC

[0394] ACAAACGCCCATGCATGCGCAAAGAGAGAGAGATTAGATCAGTTTATCATTATGGCAGTTATTGACTCAGGAATGGGAGTGAAGTGTT

[0395] CAGAAAAGTCAGGGGGGAAACGTCCTTTTCGCGATATACTTGTCCATCTATGTATAATTTGTCCACTGTAATGATTGCTCTTTTTCCT

[0396] TCATTTATTTTGTTTTCATATTGGTAAAAGTTGCTTTCAATGTTCAAGAATGTCCTTTGGGTATTGTTAATTTAGGGCCATAGTTTGT

[0397] GCCTTAAGTTGTCTGCCTTGCTTTTGAACCAGTTCTGTGTGTTTATAGTGTTCAAATTTTGCGATGATTGGTCGTGGTTGGTTGTTG

[0398] TTTTTGAGATCTGATACGGTGAACACGGTGAAAGGTGGTGATCTCTTCAGTTTTGGCTGGTAATTTGAGTTGTTTAATCATAAAGTCTT

[0399] TGACTGATTTCGCGGGGTCATCTGGAGTTTGTTCATGGATGACTGTGAAAACTAAACTGTCCCTCGTGCTGCGCATTTGCAAGTCCAA

[0400] AATGTTTTCTTTCATGGTTTTGTTTTCTGCAACAACAGAAGTGAGTCAGTGAACTGAATCCTTTAAAAAGTTATTTTCTTAGTGAGA

[0401] GTGTCTATATGTCCTTGGCTGAATTCTAAGCTGTGCCACAATTTCTGGAATTCCTTGTGTATAACTTCAAAATGATATTTGTGCATTT

[0402] TTCTAAGTAATATGTAAATCTTTCTGTTACCATTCGTTCCATTGTTTTCTCTAGTTGTAAAGTTAGTGCTACGCCATAGTGTATACG

[0403] TCTATAGTGTTACGCCCATGGACTGTTTTGTGTTTGTTTTCCTCCCCCATGTGCTCCATGTCCTAGTTTTCTCCCGTGTTTCCTTGTG

[0404] TCCATATTTGGTTCTTCATCTTCCTGTTCCCATTGAGTTACATTTGTTTCAGCTGTGTCGTTGCCCACTGCAAAAAAAAAAAATTAAC

[0405] TGACCCCGCTATTGTTGGGTTAATAACTTAGTACTCAGAAGCTAGATTGTTAACGGTATTTATTGAATGAACTAGGGCTGTGCGATAT

[0406] GAAAAAAAAAAAAAAAACCTATCATGATTTCTTTGATCAATTTTGCGATTTTGACACACACAGATATGCTTTACAGTCATAAATGCAT

[0407] TCAGGATGAATTTGAAACATATTTCCAAAAGAAAACCAATTAGAGCTTTATCAAAAAGTTGTAACGTCTCTAGAACAGACACAAGTCA

[0408] AAATAATCACTATAGTAAAGGTGTAACAAAATTTCTAGACTTATGGCAAAAAATATAAATAAATAAATCCTCAGTCCAGGGACTTTTT

[0409] TGCCATACATTGTTTACAGAGCTCATTAATTGTAGCGGTGTCGCAGGTGTTGAAATAGATTTGTTATACATTATACAGGTTCACACGT

[0410] ACTCCGTCAGCAGTGCGTAAATAGTATGTGTATTCGGTGCAGAAGCAGCAGCGAGAGCGCATTATTGTAACGCGAAAGCACATTAAAT

[0411] TAATGCATGAGCGCGTATCTATCCGCCCGCACGCAGATTTCCTCTGCTCTGTCACAAAACCAGACGCGTGTGCTCCGATACATGCTGC

[0412] TCTCGCTCGGAGAGAGTGCGCGCACTTAAAACATGTCTCCTCTCACTTAAACTGCGTCCTGTGCACTCTCTCTATGCTCATGTGCTAG

[0413] ATATTATTTTAGCATGTTTTTATTTCTAGCCTTTTACCGCGTGCAGTGTGAACGCTCTGATCCGTTAACATGGGTTTGGAAAAAAGGC

[0414] GCATCACAGACAGTGTGTGTGAACCTGGAGTTCCCTTTCGAAAGCTTCAGTCGATGCTGCGCTGCTCAGCGCATTGGGAAACGTCTTG

[0415] TTCGTGACCAGCTGTGAATAATGTGTGTAACACGTCGATAGAATTGACCCGGCGGTAATATAGCCTGGGTTGATGACGTCATCTGCGC

[0416] GCGCCCGCAACACGGGGCTATAAATAGATAAGCCACAGGTGCATCAACAGGTCTTTTGTCTTCAGATCATTCTGTGCATGTGTGCGTC

[0417] AGGAAGATTCTTTTTGTCTACTACAGATCTTCGTACGATGACCCAACGAAACAGTAAGTGTTGTGTTCCTCCATGCTCTCGTCCTATG

[0418] TATGAGCTGGATACGCATGATTACTGTTTTGTTTGTTTGGGGGAAGAGCACGCGGTTCTGGCTTTAGAGACGGGCGAGTGCGAGCATT

[0419] GTGAACTGCTCTCAGTGAAGATGCTTCGTGCTCGTCTGAACTATTTTCAGACCGCACCCACCTTTTCGTCGGAGGGCTCTCGTGCGGG

[0420] TCTGCGTGACGAGCGAGCGACGGGCCCGTCCCTTTCGCTTACAGTGTCACCAGATCCACGCATCCTCTCTCGTGATCTCGAAGCGTGC

[0421] TCCGGTGCTTCTTCGATTCACGAGGAGGATGATATATCTCTTGAGTCTTCGGGCGATGACCAGGCCGCCTCTGAACGCTCCTCTCGCG

[0422] AGAGGAAATCGGTCGAGGAGCTGCTCGAGGTGGTTACTCGCGCGGTGGACAGGCTGAAGCTTGACTGGCCACGCGACCAAGAGACCCC

[0423] CAAACGTAGTAAGCTGGAGGATAGGTTTCTGTCGGGTGGTAGGAGAGAGAGACCGCAACATCAGTCATTCCCCTTTTTCGAGGACCTC

[0424] CATGATGAACTGTCGAAGTCATGGAGCAAACCATATACTTCCCGAATCTTTGTGCCCTCGACGTTGACTTATCGTGGATGCAAAGTCA

[0425] CGGGGGTATTCGGAGATGCCTCGGGTTGAAGCGTCGCTTGCGAGCTATCTCTCGCCTGAATCTGCATCCTCGGTGAAGAAACCTACTC

[0426] TCCCCACTAAACCTTGTAGAATTACATCATCGCTAGTGGGTAAAGCTTATCAAGCTGCAGGTCAGGCTGGTGCTGCGCTGCACACTAT

[0427] GGCAGTGTTACAGGCATACCAGGCTGACCTATTGAAGGACTTGAGTGTGGGCAGTACAATCGATGAGGAAGCATTCGCTGAGCTTCGT

[0428] CGGGCCACAGATTTGTCTCTCCGGGCGACCAAGCAGACGGCTTGTGCCATTGGCCGGTCGATGTCAGCTTTAGTCAGCACGGAGAGGC

[0429] ATCTGTGGCTTAATTTGACGGGCATTAAAGAGAAGGAACGTTTGTTTCTTTTAGACTCTCCTATCTCTCCATCTGGCCTGTTTGGCGA

[0430] TTCGGTTAATACGGTCGTCGACAGGTTTAGGGAAGTAAAGAAACATGAAGAAGCGTTCGTTCAGTTTCTCCCTCGCCGCACTCAAGGG

[0431] GAGGGGCCATCAGCCACCCAGCCTCGCCCCGGTCCTTCAAAAACCAGGGAGGTTAAAAAGCAGAGCGTGGCAGACCGTGCTTCCCCTC

[0432] GTAGGGATTGGGGACAGGTTCGCCGTGCTCAGCAACCTCCCAAGCCAGACCTCAGGACTTTTATTAATAAGAAGAAGAAGTCCTGACG

[0433] CCATGGCGCCCGTGCTGGTGGGGGTAGTACCTCACGGGGTGGTGCGCGCTCGAGAGCTTTTCGCCCCGCCCCGTTACCCTCACAAAAC

[0434] CCCTCCATCCCCGCCGCTTCTTGTGCCTCGGGGGGCGGGGGTTTCCAGCGAGATATTAGATGTTCCAGTGCCTCCTGCCATCATTCTG

[0435] GACACGGAACATCTAACATCCCCTCAAAAGGAAGTATTGAAATTGGTACCACTCTCAGAGAGTCTGGCAGCGTGGAAACTTCTGCCAG

[0436] GCATTTCTGCATGGATGTTGAGCACAGTACGGATAGGATACAGCATCCAATTTATTCGTCATCCTCCACGTTTCAACGGCGTGGTTTC

[0437] CACTTCCGTGAAACCGGAGCTGATGCAGGTACTGTCTCAAGAGCTACAAACTCTTCTGGGCAAAGAGGCCATAGAACATGTTCCTCTT

[0438] CCACAGAGAGAGTCGGGCTATTACAGCAGATACTTCCTAGTTCCCAAAAAGGGTGGGGGAGTGCGTCCAATCTTGAATCTTCGAGGCT

[0439] TAAACCGTACAATCAGAGCACTCAAGTTCAAGATGTTAACCGTCAAGACGGTCGTGTCGCAAATTCGGCATCGCGATTGGTTTATCAC

[0440] GATCGATCTGAAGGACGCATATTTTCATATAGAGATTTTGCCACAACACAGGAAATTCTTGAGGTTCGCTTTCGGGGGCGAAGCATAC

[0441] CAGTTTCGGGTTCTTCCTTTCGGCCTAGCCTTATCACCCCGCACGTACACAAAATGCATGGATGCAGCGCTGGCTCCATTACGACTCC

[0442] AGGGCATTCGCATTTTGAATTATATCAACGATTGGCTGATACTAGCGCAATCGCGAGAGATGGCGCTACAACACAGGGACATCGTGTT

[0443] AGCTCATCTAGTTTCTCTGGGGTTGAGACTCAACACCGAGAAGAGTGTTCTCTGTCCGGCCCAGAGAACGACTTATCTCGGGATCGTT

[0444] TGGGATTCGATCACGATGCGGGCACAACTGTCTCCCGCTCGGATCGAGACCATTCAGCAGACCATGAGCAAGGTCAGGCTAGGCCAGG

[0445] ATCGCACTGTTCGTCAGTATCAAAAGATGTTAGGTTTCATGGCTTCAGCATCCACGGTGATTCCTTTGGGGCTGTTACACATGAGACC

[0446] GTTTCAGTTGTGGCTAAAAGCCAGAGGATTTCATCCGAGAGCCAATCCTCAAAGGCAAATAAAAGTGACGTGCCGCGGGCTTCGTACA

[0447] CTATCTCTGTGGCTCAGACCCCGGTTCCTTGCCTTGGGTCCCACTCTAGGGGCACCGTGTCGTCGCAGACTGCTAACGACAGATGCCT

[0448] CCCTGTTGGGCTGGGGAGCAGTCTTGGATGGCCACCCAGCTCAAGGGGAATGGGGGGGTCATCAGCTCGATTGGCACATCAATTGCCT

[0449] CGAGCCGATGGCCGTATTTCTGGCTCTGAAATATTTCCTCCATCATTTGAGAGACTGTCATGTTCTAGTACGGGTGGACAGCACAGCA

[0450] ACAGTCTCGTATATAAATCACCAGGGGGGTCTGCGCTCACGCAATCTGAACAAGATAGCGAGGCAGATTTTTCTTTGGGCCCAGGACA

[0451] AGCTCCTGTCACTCAGGGCAGTTTACATTCCGGGGCGTTGGAATGTGGGAGCGGATTTACTGTCCAGACAGACACTTCCGACAGGGGA

[0452] ATGGAAACTCCACCCAGAGGTAGTGAAACAGATTTGGGAAAAATTTTACGAAGCAGAGGTGGACCTCTTCGCCTCCCATCAGACAGCG

[0453] CAATGTCCCCTCTACTTCTCTCTGAGTCACCCAGCCCCCCTGGGTCTGGATGCGATGGCGCACACATGGCCCAGAATGCGCCTGTATG

[0454] CGTTTCCTCCAGTTTCTCTGCTCCCGGGAGTCCTAGCCAGAGTTCGCCAACAAGGGTCTTGCCTCTTGCTGATAGCGCCACGTTGGCC

[0455] GAACAGGGTATGGTTTTCGGAGATAATATCCATCCTCGACGGCTCGCCATGGGCGATTCCGGAGAGGAGGGACCTTCTGTCTCAGGCA

[0456] GGGGGTACGATATTCCATCCCAGGCCCGACCTGTGGAATCTTCATGTTTGGCCCCTGAAGGGTACCAGCTGAGAACACAGGGTGTC

[0457] GCCGGGTGTTATTGATACCATCCTTAGTGCTAGGGCTTTCTCCACCAGACAGAGTTATGCCAGTAAATGGGGTGTCTTTGACAGGTGG

[0458] TGTGTGGTACACAATGTAGATCCAGTCAACTGCCATATTGCTTCAGTTCTGGACTTTATGCAAGAGAAATTGTCAACAGGCACATGCC

[0459] CTGCTACTCTTAAGGTTTATGTGGCCGCTCTTTCGGCTTGCCACGCCTTGATTGACGGGATGCCCACTCGGAGACACCCTCTGCTCTC

[0460] TCGCTTTCTTCGTGGGGCCAGACGACTGAGGCCTACAGTAAAAAACCAAGATGCCTTCTTGGGACTTAGCTATAGTTCTCGAGGGTCTG

[0461] GTTGAAACCCCCTTTGAAACTTTAGAGTCAGCGTCTGATAGACTTCTAACTCTAAAAATGTTTTTCTCTCATGGCAATAACTTCTTTTGA

[0462] AGAGAATTGGGGATATGCAGGCTCTGTCTATCTCACCATCATGCTTAGACTTTGCCCAGGGAGCGTGAAAGTGATTCTGCATCCTCA

[0463] TCCTGATTACCTGCCTAAGGTTCCGTTTTCGGCTGTACATCCGGTCATTCTAGAGGCCTTCTGTCCTCCGCATTCGCAACGCCGGAG

[0464] CAGGAGAAATCTTATAGACTGTGTCCAGTCCGTGCTCTTCAGACTTACGTCCACTGCACTAGCCAGTGGCGTAAGTCGGAGCAACTGT

[0465] TCGTCTGTTATGGTGGTGGTAACAGAGGAGCAGCTGCCACCAGGCAGACCATGTCTCACTGGGTCAGGGATGCTATTGCTTTGGCTTA

[0466] TGAGGCGCGCGGTCAAGCTTCGCCTATAGGTCTTAGAGCTCACTCCACAAGGGGGGTCGCCTCCTCTAGTGCTTTAGCAAGGGGTGTC

[0467] CCCTTACAACAGGTTTGTGATGCGGCAGGTTGGTCCTCTCTGCACACATTCATAAGATTTTATAGTTTGGATGTCCATGCTACTCCGG

[0468] GCTCTCATGTCCTTGAGTCAACATCACAAGCTAATGTCTGAGGCCTTCTTGTGGTTTGGTAGCACACCCGCACAACCTTAGGGGTCCA

[0469] GACATTTTCAAGCATGGCGGCGTGGGTATTCTCATTCCCAATGCGCTGAGCAGCGCAGCATCGACTGAAGCTTTCGAAAGGGAACGTT

[0470] CCCGGTTACTTAGCTGTAACCTTGTTCCCTGAGAAAGCGGAACGAGATGCTGCGCTGAATTGCCGTACTGAAATATGTCCCAGGACTG

[0471] CTCTTCAGACAAAATGTCCTGTTGATGCACCTGTGGCTTATCTATTTATAGCCCCGTGTTGCGGGCGCGCGCAGATGACGTCATCAAC

[0472] CGAGGCTATATTACCGCCGGGTCAATTCTATCGACGTGTTACACACATTATTCACAGCTGGTCACGAACAAGACGTTTCCCAATGCGC

[0473] TGAGCAGCGCAGCATCTCGTTCCGCTTTCTCAGGGAACAAGGTTACAGCTAAATAACCGGGAACGTTACGTAGGCATATTCCCAGTCT

[0474] GTTCTGTTCTCGCGCTAGGCGCGTTGCATTCTGATTAGATCGTATGGCATACTGCGGAAGGTCAGGGCCCCGGAAAATCGTGCTATAA

[0475] AGCGATTTAGAAATCGCGCACGCTCGATCGTGATTTTATGATGATTTCTATCGCACAGCCCTAGAATGAACTGGCAATGAACAGAAAA

[0476] TAACTCGGGCTTATTTAATTATATATATGAAAAGGTGTTATCATGTTATGACTTAGATATTTTTACATATATTAACAATATTATTATT

[0477] TATTTTAATAGTAATTGGCGGCCCACCTGCAATACCATCGCGACCCACTAGGGGGCAGTGGCCCACAGGTTGAAAACCACTGGTTTAG

[0478] ACTATTAAAGGGGTCATATGATGCGATTTCAAGTTTTCTTTCTCTTTGGAGTGTTACATGCTCTTGGTGCATAAAGAAGATCTGTAAA

[0479] GTTGTAAAGACTAAAGTCTCAAACCCAAAGAGATATTCTTTATAAAAGTTGAGACTCGTCCACGCCCTCCTAAAACGCCTCATTTAAA

[0480] CACGCA

[0481] (SEQ ID NO:20)

[0482] CGACCTGCTGACTGTGCGCTGGGTGCCCTCTGGTACACCCTCCGGGGAAATGGCGGTGCTCGCTCCCCAGCCTCTCGTCGCATCGTCA

[0483] TCTCCGCTAGCTCAACGGCCTCGACGAGCTCTCCCACGGTGCGGTAGGGCGCACAAATACCGGTCCACGACGACACGTTCGGCCACTT

[0484] GGGCGGCACTGGGGCTCCCGGCACGTTCGGCTGCTTGGGCGCGGGCTGGCAACCGACTATTATACTCCCAGGCTCGGAACAATTGGGC

[0485] CGCGGCGATGGGAGACAAACCCACACGCCCCAGTATTTCCTTCAGTAGTTCATCATAGGATTCCTTTTGGCGGGCTGGCATGGAAAAG

[0486] TAGGCCTGTTGGGGTTCCCCGGTTAACAACGGTACCAGTATCCGTGCCCACTCTTCCCGGGGCCAGTCCTCTCAAGCGGCGATGGTTT

[0487] CAAACATTCCCAGATAATGCTCTATATCGTCGTGGACGGTCATCTTTGGCAGGAGCTGGGTGGCTTGCGTGCGGGGGTCCGGCAGCAG

[0488] GGCCCGCTGAGCGGCCGTGAAGCGGAGAGCGGCGACCTCACGCTCTGTTTCGCCCTGTCGAGTGGCCATGTGCTCGACGATCTGCTGC

[0489] TGTCGAATGCTTATTTCCGTCAGGTGCTTGAGAAGCTCTTCCATAGCTGGGGAGGGAGTCACCGCCTGACGAGAAAGCAGAGGGGGGA

[0490] AAAAAAACAACAACTTGGTGAACCAGGACAATCGTGTGAGTCGGTGATCCTTCAGTAATCTGCCCGCATTCTCCACCAGTGTCACGGG

[0491] CTGAAGAATCACACGACAGGGTTTAGTGCAAAAGTGAAGCCCCGGAGGGTGTATTTATTGACAGGTGCTTTGATTTGTGCTGAATCCT

[0492] GTGGTGTCCAGTGCTCGTCCCTCGGTGCCACGTGATCCTGTTTGCCGGCCGGCTGAGTGCAAGGTGACTTGGTGTCCGGTGCTCGGGT

[0493] GGCGGGCTGGTCGATCCACAGCTTTCTGGAGGGACAAGAGACACAACGTTAGTTTCAGTCATCTGAAGATCCTCTCCTCCTTGTGTCA

[0494] CCTTCAGGCGCAGCGTTTATAGTCCTCTCCTCATCCGCTTCAGCTGGGACCGATCAGCCCGCTGTGATTGCGTGCAGGTGAATCTCCC

[0495] TCGTTGCCAGGGCGACGCTAATAGGTGCCCAGGACACGGCTTACAATACTTAAGTAAAGTTCAAGTATATTTTGAATTTTTGTAAGTA

[0496] TAAGTCAAGTATACTTAATTGTCATTATAAATATATATCTTAGAAGTACATAAAGCCCATTTCTGAGAAGTACCATAAAAAGTAAACT

[0497] AAAAGTATACTTTCCTATTTTTTAGTTTAAAATAAGTATACTAATAGCACACTTGAATAAACTTCTTTTTCGTAAGGGTAGCTTCACT

[0498] CATTGATTCCGTTGAGGACGATCCAGCTTCCACAGGCCCCATGATAGATGTTCCTTCCCCAGCAGGTATATCCGCGGTCAGCACTGCA

[0499] TCGGCCAGACCGACCTCAGACGATTTTACAGTGAGCCTATCTTCCTCATTTGCAGCACTATCCTTATTACCTCCAGCTGCGTTACTCT

[0500] TGTCAGGGCATGAACGGATTAAGTGGCCTGATTTATTGCATCCGAAGCATTTCATTTTGTCAGTAAAACACTACATATTCAAAATCGTC

[0501] CACACGGAAATTTAGTGCCAAGTCCAGTTCAGCGTCGTCTTTGATTATCATAAAAGCAAATCTCCTAATGAAAAAATATGTTTCAAT

[0502] AAAGGAGACTCGCACCCGATTGCAATCTTTTTAATCGGTGAAACCAACTTTCCATAATGTGAAAGCACCTGCATCACAATATTATCAC

[0503] TAAGGAAAGGGGGGATATTCAATATTGTGACTCTCTTTGATGGTAACATGAGAGGCAAAACAGATGTAAGTATTTCATCAATGATTAT

[0504] ACTTCGCGCAACCACCTCATTTGCTCTTTCTGCCGTATTCAAAAAAATCATGGTCGTGTTATTCATTCTGGATGCAGAGAGAATATT

[0505] TTGATGGCCAACAACTTCTCCTACACCCAGACAGCACACGGATACTATCTTGACTCTGTGACGCAATGTGAGGGGAATCAAGCCCATTA

[0506] TCACCCTGAGCAAACAAAACACACACAAGATAAATATTAAATCAAATAAACAAACTTAGACGTTAAAAAAAATTAAATAAATTTACA

[0507] TTTACGCATTTAGCAGACGCTTTTATCCAAAGTAACTTACAGTGCATTCAGGCTAACATTTTTTGACCTAACATGTGTTCCCTGGGAA

[0508] TCAAACCCACTACATTTTGCGCTGTTAACGCAATGCTCTACCATTGAGCCACAGGAACACACAAAATAAGGAAACACACAAAAAAGCA

[0509] AATACACGCTCTCACACGCACTCCACAAACGCCCATGCATGCGCAAAGAGAGAGAGATTAGATCAGTTTATCATTATGGCAGTTATTG

[0510] ACTCAGGAATGGGAGTGAAGTGTTCAGAAAAGTCAGGGGGGAAACGTCCTTTTCGCGATATACTTGTCCATCTATGTATAATTTGTCC

[0511] ACTGTAATGATTGCTCTTTTTCCTTCATTTATTTTGTTTTCATATTGGTAAAAGTTGCTTTCAATGTTCAAGAATGTCCTTTGGGTAT

[0512] TGTTAATTTAGGCCATAGTTTGTGCCTTTAAGTTGTCTGCCTTGCTTTTGAACCAGTTCTGTGGTTTATAGTGTTCAAATTTTGCG

[0513] ATGATTGGTCGTGGTTGGTTGTTGTTTTGAGATCTGATACGGTGAACACGGTGAAAGGTGGTGATCTCTTCAGTTTTGGCTGGTAATT

[0514] TGAGTTGTTTAATCATAAAGTCTTTGACTGATTTCGCGGGGTCATCTGGAGTTTGTTCATGGATGACTGTGAAAACTAAACTGTCCCT

[0515] CGTGCTGGCATTTGCAAGTCCAAAATGTTTTCTTTCATGGTTTTGTTTTCTGCAACAACAGAAGTGAGTCAGTGAACTGAATCCTTT

[0516] AAAAAGTTATTTTCTTTAGTGAGAGTGTCTATGTCCTTGGCTGAATTCTAAGCTGTGCCACAATTTCTGGAATTCCTTGTGTATAA

[0517] CTTCAAAATGATATTTGTGCATTTTTCTAAATGATATGTAAATCTTTCTGTTACCATTCGTTCCATTGTTTTCTCTAGTTGTAAAGTT

[0518] AGTGCTACGCCCATAGTGTATACGTCTATAGTGTTACGCCCATGGACTGTTTTGTGTTTGTTTTCCTCCCCCATGTGCTCCATGTCCT

[0519] AGTTTTCTCCCGTGTTTCCTTGTGTCCATATTTGGTTCTTCATCTTCCTGTTCCCATTGAGTTACATTTGTTTCAGCTGTGTCGTTGC

[0520] CCACTGCAAAAAAAAAAAATTAACTGACCCCGCTATTGTTGGGTTAATAACTTAGTACTCAGAAGCTAGATTGTTAACGGTATTTATT

[0521] GAATGAACTAGGGCTGTGCGATATGAAAAAAAAAAAAAAAACCTATCATGATTTCTTTGATCAATTTTGCGATTTTGACACACACAGA

[0522] TATGCTTTACAGTCATAAATGCATTCAGGATGAATTTGAAACATATTTCCAAAAGAAAACCAATTAGAGCTTTATCAAAAGTTGTAA

[0523] CGTCTCTAGAACAGACACAAAGTCAAAATAATCACTATAGTAAAGGTGTAACAAAATTTCTAGACTTATGGCAAAAAATATAAATAAAT

[0524] AAATCCTCAGTCCAGGGACTTTTTTGCCATACATTGTTTACAGAGCTCATTAATTGTAGCGGTGTCGCAGGTGTTGAAATAGATTTGT

[0525] TATACATTATACAGGTTCACACGTACTCCGTCAGCAGTGCGTAAATAGTATGTGTATTCGGTGCAGAAGCAGCAGCGAGAGCGCATTA

[0526] TTGTAACGCGAAAGCACATTAAATTAATGCATGAGCGCGTATCTATCCGCCCGCACGCAGATTTCCTCTGCTCTGTCACAAAACCAGA

[0527] CGCGTGTGCTCCGATACATGCTGCTCTCGCTCGGAGAGAGTGCGCGCACTTAAAAACATGTCTCCTCTCACTTAAACTGCGTCCTGTGC

[0528] ACTCTCTCTATGCTCATGTGCTAGATATTATTTTAGCATGTTTTTATTTCTAGCCTTTTACCGCGTGCAGTGTGAACGCTCTGATCCG

[0529] TTAACATGGGTTTGGAAAAAAAGGCGCATCACAGACAGTGTGTGTGAACCTGGAGTTCCCTTTCGAAAGCTTCAGTCGATGCTGCGCTG

[0530] CTCAGCGCATTGGGAAACGTCTTGTTCGTGACCAGCTGTGAATAATGTGTGTAACACGTCGATAGAATTGACCCGGCGGTAATATAGC

[0531] CTGGGTTGATGACGTCATCTGCGCGCGCCCGCAACACGGGCTATAAATAGATAAGCCACAGGTGCATCAACAGGTCTTTTGTCTTCA

[0532] GATCATTCTGTGCATGTGTGCGTCAGGAAGATTCTTTTTGTCTACTACAGATCTTCGTACGATGACCCAACGAAACAGTAAGTGTTGT

[0533] GTTCCTCCATGCTCTCGTCCTATGTATGAGCTGGATACGCATGATTACTGTTTTGTTTGTTTGGGGGAAGAGCACGCGGTTCTGGCTT

[0534] TAGAGACGGGCGAGTGCGAGCATTGTGAACTGCTCTCAGTGAAGATGCTTCGTGCTCGTCTGAACTATTTTCAGACCGCACCCACCCTT

[0535] TTCGTCGGAGGGCTTCCGTGCGGGTCTGCGTGACGAGCGAGCGACGGGCCCGTCCCTTTCGCTTACAGTGTCACCAGATCCACGCATC

[0536] CTCTCTCGTGATCTCGAAGCGTGCTCCGGTGCTTCTTCGATTCACGAGGAGGATGATATATCTCTTGAGTCTTCGGGCGATGACCAGG

[0537] CCGCCTCTGAACGCTCCTCTCGGAGAGAAATCGGTCGAGGAGCTGCTCGAGGTGGTTACTCGCGCGGTGGACAGGCTGAAGCTTGA

[0538] CTGGCCACGCGACCAAGAGACCCCCAAACGTAGTAAGCTGGAGGATAGGTTTCTGTCGGGTGGTAGGAGAGAGACCGCAACATCAG

[0539] TCATTCCCCTTTTTCGAGGACCTCCATGATGAACTGTCGAAGTCATGGAGCAAACCATATACTTCCCGAATCTTTGTGCCCTCGACGAT

[0540] TGACTTATCGTGGATGCAAAGTCACGGGGGTATTCGGAGATGCCTCGGTTGAAGCGTCGCTTGCGAGCTATCTCTCGCCTGAATCTG

[0541] CATCCTCGGTGAAGAAACCTACTCTCCCCACTAAACCTTGTAGAATTACATCATCGCTAGTGGGTAAAGCTTATCAAGCTGCAGGTCA

[0542] GGCTGGTGCTGCGCTGCACACTATGGCAGTGTTACAGGCATACCAGGCTGACCTATTGAAGGACTTGAGTGTGGGCAGTACAATCGAT

[0543] GAGGAAGCATTCGCTGAGCTTCGTCGGGCCACAGATTTGTCTCTCCGGGCGACCAAGCAGACGGCTTGTGCCATTGGCCGGTCGATGT

[0544] CAGCTTTAGTCAGCACGGAGAGGCATCTGTGGCTTAATTTGACGGGCATTAAAGAGAAGGAACGTTTGTTTCTTTTAGACTCTCCTAT

[0545] CTCTCCATCTGGCCTGTTTGGCGATTCGGTTAATACGGTCGTCGACAGGTTTAGGGAAGTAAAGAAACATGAAGAAGCGTTCGTTCAG

[0546] TTTCTCCCTCGCCGCACTCAAGGGGAGGGGCCATCAGCCACCCAGCCTCGCCCCGGTCCTTCAAAAACCAGGGAGGTTAAAAAGCAGA

[0547] GCGTGGCAGACCGTGCTTCCCCTCGTAGGGATTGGGGACAGGTTCGCCGTGCTCAGCAACCTCCCAAGCCAGACCTCAGGACTTTTAT

[0548] TAATAAGAAGAAGAAGTCCTGACGCCATGGCGCCCGTGCTGGTGGGGGTAGTACCTCACGGGGTGGTGCGCGCTCGAGAGCTTTTCGC

[0549] CCCGCCCCGTTACCCTCACAAAACCCCTCCATCCCCGCCGCTTCTTGTGCCTCGGGGGGCGGGGGTTTCCAGCGAGATATTAGATGTT

[0550] CCAGTGCCTCCTGCCATCATTCTGGACACGGAACATCTAACATCCCCTCAAAAGGAAGTATTGAAATTGGTACCACTCTCAGAGAGTC

[0551] TGGCAGCGTGGAAACTTCTGCCAGGCATTTCTGCATGGATGTTGAGCACAGTACGGATAGGATACAGCATCCAATTTATTCGTCATCC

[0552] TCCACGTTTCAACGGCGTGGTTTCCACTTCCGTGAAACCGGAGCTGATGCAGGTACTGTCTCAAGAGCTACAAACTCTTCTGGGCAAA

[0553] GAGGCCATAGAACATGTTCCTCTTCCACAGAGAGAGTCGGGCTATTACAGCAGATACTTCCTAGTTCCCAAAAAGGGTGGGGGAGTGC

[0554] GTCCAATCTTGAATCTTCGAGGCTTAAACCGTACAATCAGAGCACTCAAGTTCAAGATGTTAACCGTCAAGACGGTCGTGTCGCAAAT

[0555] TCGGCATCGCGATTGGTTTATCACGATCGATCTGAAGGACGCATATTTTCATATAGAGATTTTGCCACAACACAGGAAATTCTTGAGG

[0556] TTCGCTTTCGGGGGCGAAGCATACCAGTTTCGGGTTCTTCCTTTCGGCCTAGCCTTATCACCCCGCACGTACACAAAATGCATGGATG

[0557] CAGCGCTGGCTCCATTACGACTCCAGGGCATTCGCATTTTGAATTATATCAACGATTGGCTGATACTAGCGCAATCGCGAGAGATGGC

[0558] GCTACAACACAGGGACATCGTGTTAGCTCATCTAGTTTCTCTGGGGTTGAGACTCAACACCGAGAAGAGTGTTCTCTGTCCGGCCCAG

[0559] AGAACGACTTATCTCGGGATCGTTTGGGATTCGATCACGATGCGGGCACAACTGTCTCCCGCTCGGATCGAGACCATTCAGCAGACCA

[0560] TGAGCAAGGTCAGGCTAGGCCAGGATCGCACTGTTCGTCAGTATCAAAAGATGTTAGGTTTCATGGCTTCAGCATCCACGGTGATTCC

[0561] TTTGGGGCTGTTACACATGAGACCGTTTCAGTTGTGGCTAAAAGCCAGAGGATTTCATCCGAGAGCCAATCCTCAAAGGCAAATAAAA

[0562] GTGACGTGCCGCGGGCTTCGTACACTATCTCTGTGGCTCAGACCCCGGTTCCTTGCCTTGGGTCCCACTCTAGGGGCACCGTGTCGTC

[0563] GCAGACTGCTAACGACAGATGCCTCCCTGTTGGGCTGGGGAGCAGTCTTGGATGGCCACCCAGCTCAAGGGGAATGGGGGGGTCATCA

[0564] GCTCGATTGGCACATCAATTGCCTCGAGCCGATGGCCGTATTTCTGGCTCTGAAATATTTCCTCCATCATTTGAGAGACTGTCATGTT

[0565] CTAGTACGGGTGGACAGCACAGCAACAGTCTCGTATATAAATCACCAGGGGGGTCTGCGCTCACGCAATCTGAACAAGATAGCGAGGC

[0566] AGATTTTTCTTTGGGCCCAGGACAAGCTCCTGTCACTCAGGGCAGTTTACATTCCGGGGCGTTGGAATGTGGGAGCGGATTTACTGTC

[0567] CAGACAGACACTTCCGACAGGGGAATGGAAACTCCACCCAGAGGTAGTGAAACAGATTTGGGAAAAATTTTACGAAGCAGAGGTGGAC

[0568] CTCTTCGCCTCCCATCAGACAGCGCAATGTCCCCTCTACTTCTCTCTGAGTCACCCAGCCCCCCTGGGTCTGGATGCGATGGCGCACA

[0569] CATGGCCCAGAATGCGCCTGTATGCGTTTCCTCCAGTTTCTCTGCTCCCGGGAGTCCTAGCCAGAGTTCGCCAACAAGGGTCTTGCCT

[0570] CTTGCTGATAGCGCCACGTTGGCCGAACAGGGTATGGTTTTCGGAGATAATATCCATCCTCGACGGCTCGCCATGGGCGATTCCGGAG

[0571] AGGAGGGACCTTCTGTCTCAGGCAGGGGGTACGATATTCCATCCCAGGCCCGACCTGTGGAATCTTCATGTTTGGCCCCTGAAGGGTA

[0572] CCAGCTGAGGAACACAGGGGTGTCGCCGGGTGTTATTGATACCATCCTTAGTGCTAGGGCTTTCTCCACCAGACAGAGTTATGCCAGT

[0573] AAATGGGGTGTCTTTGACAGGTGGTGTGTGGTACACAATGTAGATCCAGTCAACTGCCATATTGCTTCAGTTCTGGACTTTATGCAAG

[0574] AGAAATTGTCAACAGGCACATGCCCTGCTACTCTTAAGGTTTATGTGGCCGCTCTTTCGGCTTGCCACGCCTTGATTGACGGGATGCC

[0575] ACTCGGGAGACACCCTCTGCTCTCTCGCTTTCTTCGTGGGGCCAGACGACTGAGGCCTACAGTAAAAACCAAGATGCCTTCTTGGGAC

[0576] TTAGCTATAGTTCTCGAGGGTCTGGTTGAAACCCCCTTTGAAACTTTAGAGTCAGCGTCTGATAGACTTCTAACTCTAAAAATGTTTT

[0577] TTCTCATGGCAATAACTTCTTTGAAGAGAATTGGGGATATGCAGGCTCTGTCTATCTCACCATCATGCTTAGACTTTGCCCCAGGGAG

[0578] CGTGAAAGTGATTCTGCATCCTCATCCTGATTACCTGCCTAAGGTTCCGTTTTCGGCTGTACATCCGGTCATTCTAGAGGCCTTCTGT

[0579] CCTCCGCCATTCGCAACGCCGGAGCAGGAGAAATCTTATAGACTGTGTCCAGTCCGTGCTCTTCAGACTTACGTCCACTGCACTAGCC

[0580] AGTGGCGTAAGTCGGAGCAACTGTTCGTCTGTTATGGTGGTGGTAACAGAGGAGCAGCTGCCACCAGGCAGACCATGTCTCACTGGGT

[0581] CAGGGATGCTATTGCTTTGGCTTATGAGGCGCGCGGTCAAGCTTCGCCTATAGGTCTTAGAGCTCACTCCACAAGGGGGGTCGCCTCC

[0582] TCTAGTGCTTTAGCAAGGGGTGTCCCCTTACAACAGGTTTGTGATGCGGCAGGTTGGTCCTCTCTGCACACATTCATAAGATTTTATA

[0583] GTTTGGATGTCCATGCTACTCCGGGCTCTCATGTCCTTGAGTCAACATCACAAGCTAATGTCTGAGGCCTTCTTGTGGTTTGGTAGCA

[0584] CACCCGCACAACCTTAGGGGTCCAGACATTTTCAAGCATGGCGGCGTGGGTATTCTCATTCCCAATGCGCTGAGCAGCGCAGCATCGA

[0585] CTGAAGCTTTCGAAAGGGAACGTTCCCGGTTACTTAGCTGTAACCTTGTTCCCTGAGAAAGCGGAACGAGATGCTGCGCTGAATTGCC

[0586] GTACTGAAATATGTCCCAGGACTGCTCTTCAGACAAAATGTCCTGTTGATGCACCTGTGGCTTATCTATTTATAGCCCCGTGTTGCGG

[0587] GCGCGCGCAGATGACGTCATCAACCGAGGCTATATTACCGCCGGGTCAATTCTATCGACGTGTTACACACATTATTCACAGCTGGTCA

[0588] CGAACAAGACGTTTCCCAATGCGCTGAGCAGCGCAGCATCTCGTTCCGCTTTCTCAGGGAACAAGGTTACAGCTAAATAACCGGGAAC

[0589] GTTACGTAGGCATATTCCCAGTCTGTTCTGTTCTCGCGCTAGGCGCGTTGCATTCTGATTAGATCGTATGGCATACTGCGGAAGGTCA

[0590] GGGCCCCGGAAAATCGTGCTATAAAGCGATTTAGAAATCGCGCACGCTCGATCGTGATTTTATGATGATTTCTATCGCACAGCCCTAG

[0591] AATGAACTGGCAATGAACAGAAAATAACTCGGGCTTATTTAATTATATATATGAAAAGGTGTTATCATGTTATGACTTAGATATTTTT

[0592] ACATATATTAACAATATTATTATTTATTTTAATAGTAATTGGCGGCCCACCTGCAATACCATCGCGACCCACTAGGGGGCAGTGGCCC

[0593] ACAGGTTGAAAACCACTGGTTTAGACTATTAAAGGGGTCATATGATGCGATTTCAAGTTTTCTTTCTCTTTGGAGTGTTACATGCTCT

[0594] TGGTGCATAAAGAAGATCTGTAAAGTTGTAAAGACTAAAGTCTCAAACCCAAAGAGATATTCTTTATAAAAGTTGAGACTCGTCCACG

[0595] CCCTCCTAAAACGCCTCATTTAAACACGCA

[0596] (SEQ ID NO: 19 represents the sequence before insertion, and SEQ ID NO: 20 represents the sequence after insertion).

[0597] The genome sequence of DEL Chr44:16600200 is shown below, and the mutation type is deletion.

[0598] (SEQ ID NO:21)

[0599] TGCCAAATTATGTCATTTGATTATTATTTTTTTTTTTAATTTCTGTTATCCTGGAAAAATGTATCACAAAAATACTAAGGAGCATTG

[0600] TTTTCATTATGACATTAATAAGAAGAAATGTTTCTTGAGCACCATTTCCGTAGGATTACTTGACAAGTAATATTAATTGAAAATGCCA

[0601] TTTCTAAACAAAAAGAGTCTCACACTAGGGTACATATCATTGTGTT

[0602] (SEQ ID NO:22)GCTAAACCACATATTTG

[0603] (SEQ ID NO: 21 represents the sequence before deletion, and SEQ ID NO: 22 represents the sequence after deletion)

[0604] The present invention discloses a method for identifying fish genome structural variation based on a graph pan-genome. The present invention performs third-generation sequencing on mirror carp "Longke No. 11", carp "Longke No. 12", German mirror carp, Yuxuan Yellow River Carp No. 2, Furui Carp No. 2, Jianli No. 2, and Yuanjiang carp, and performs pan-genome analysis with the genomes of red carp, Songpu mirror carp, and Heilongjiang carp with existing third-generation sequencing data, and a graphical pan-genome map is constructed for a total of 10 carp germplasms. Subsequently, 266 carps of 5 germplasms, including carp "Longke No. 12", Songpu mirror carp, mirror carp "Longke No. 11", Yuxuan Yellow River Carp No. 2, and Jianli No. 2, are respectively subjected to second-generation resequencing, and the resequencing results are compared to the graph pan-genome for identification of presence / absence variation (PAV), and PAV-GWAS analysis is performed with quality trait phenotypes, thereby identifying multiple PAV molecular markers related to carp quality traits. The method for identifying fish genome structural variation based on the map-pan genome provided by the present invention can be used to detect structural variation of important economic traits of fish and select fish varieties with excellent traits such as intensive high yield, high quality, and disease resistance. The map-pan genome shows higher sensitivity and accuracy in detecting structural variation (SV). Compared with the traditional single linear reference genome, the map-pan genome can more effectively identify and characterize structural variation, and has broad application prospects in molecular marker-assisted breeding and genomic selection breeding of fish.

[0605] The number of modules and processing scales described here are used to simplify the description of the present invention. Applications, modifications and variations of the present invention are obvious to those skilled in the art.

[0606] Although the embodiments of the present invention have been disclosed as above, they are not limited to the applications listed in the specification and the implementation modes, and they can be fully applied to various fields suitable for the present invention. For those familiar with the art, additional modifications can be easily implemented. Therefore, without departing from the general concept defined by the claims and the scope of equivalents, the present invention is not limited to the specific details and the illustrations shown and described herein.

Claims

1. A method for identifying fish genome structural variation based on a pan-genome map, characterized in that: The steps include: 1) Compare the individual genome data of fish of different germplasms with the reference genome to determine the variation information between the individual genome sequence and the reference genome; 2) constructing a graphical pan-genome using the variation information; 3) Comparing the genome data of the individual fish to be identified with the graphical pan-genome to generate a data set of genome structural variations of the individual fish to be identified.

2. The method for identifying fish genome structural variation based on pan-genome analysis according to claim 1, characterized in that: In step 1), representative fish germplasms are selected for de novo genome assembly to obtain individual genome data of fish of different germplasms.

3. The method for identifying fish genome structural variation based on pan-genome analysis according to claim 1, characterized in that: In step 1), the genome assembly data was compared with the reference genome using minimap2 software to obtain variation information in vcf format, and then a graphical pan-genome was constructed.

4. The method for identifying fish genome structural variation based on pan-genome analysis according to claim 1, characterized in that: In step 3), the samples of the fish individuals to be identified are subjected to 10× high-depth resequencing, and each gene sequencing sample is aligned to the pan-genome using vg giraffe software to obtain a result file in GAM format; Then use "vg stats" to count the alignment results; use "vg gamsort" to sort the alignment results on the constructed pan-genome, use "vg call" to detect structural variations in samples of different germplasms, and generate a variation file in vcf format for each sample; Use bgzip to compress the variant files in vcf format and use tabix to create an index; use bcftoolsmerge software to merge the variant files of each sample, and finally generate a total variant data file in vcf format containing all samples.

5. The method for identifying fish genome structural variation based on pan-genome analysis as claimed in claim 4, characterized in that: Also includes: The total variation data files in vcf format were converted into map and ped formats using plink, and the phenotypic information of different germplasm samples was combined to perform PAV-GWAS using gcta64 to identify structural variations associated with economic traits of fish.

6. The method for identifying fish genome structural variation based on pan-genome analysis according to claim 1, characterized in that: The mirror carp "Longke 11" assembled and sequenced by T2T was used as the reference genome.

7. The method for identifying fish genome structural variation based on pan-genome analysis as claimed in claim 1, characterized in that: The genes in the graphical pan-genome are divided into core gene families, variable gene families and unique gene families according to the number of germplasms covered.

8. The method for identifying fish genome structural variation based on pan-genome analysis as claimed in claim 1, characterized in that: The fish individual to be identified belongs to one of the fish individuals of different germplasm in step 1).

9. A gene related to high-quality traits of carp, characterized in that: for: Chr1:11972915, the variant type is deletion, the nucleotide sequence of the wild type before deletion is shown in SEQ ID NO:1, and the nucleotide sequence of the mutant type after deletion is shown in SEQ ID NO:2; Chr1:21527167, the variant type is deletion, the nucleotide sequence of the wild type before deletion is shown in SEQ ID NO:3, and the nucleotide sequence of the mutant type after deletion is shown in SEQ ID NO:4; Chr1:23399634, the mutation type is insertion, the wild-type nucleotide sequence before insertion is shown in SEQ ID NO:5, and the mutant nucleotide sequence after insertion is shown in SEQ ID NO:6; Chr7:2062567, the variant type is deletion, the nucleotide sequence of the wild type before deletion is shown in SEQ ID NO:7, and the nucleotide sequence of the mutant type after deletion is shown in SEQ ID NO:8; Chr31:9671978, the variation type is insertion, the nucleotide sequence of the wild type before insertion is shown in SEQ ID NO:9, and the nucleotide sequence of the mutant type after insertion is shown in SEQ ID NO:10; Chr40:9140647, the mutation type is insertion, the nucleotide sequence of the wild type before insertion is shown in SEQ ID NO:11, and the nucleotide sequence of the mutant type after insertion is shown in SEQ ID NO:12; Chr48:8724756, the variant type is deletion, the nucleotide sequence of the wild type before deletion is shown in SEQ ID NO:13, and the nucleotide sequence of the mutant type after deletion is shown in SEQ ID NO:14; Chr50:4154584, the variant type is insertion, the wild-type nucleotide sequence before insertion is shown in SEQ ID NO:15, and the mutant nucleotide sequence after insertion is shown in SEQ ID NO:16; Chr20:2258997, the variant type is deletion, the nucleotide sequence of the wild type before deletion is shown in SEQ ID NO:17, and the nucleotide sequence of the mutant type after deletion is shown in SEQ ID NO:18; Chr25:3825897, the variant type is insertion, the wild-type nucleotide sequence before insertion is shown in SEQ ID NO:19, and the mutant nucleotide sequence after insertion is shown in SEQ ID NO:20; and / or, Chr44:16600200, the variation type is deletion, the nucleotide sequence of the wild type before deletion is shown in SEQ ID NO:21, and the nucleotide sequence of the mutant type after deletion is shown in SEQ ID NO:22; Among them, Chr1:11972915, Chr1:21527167, Chr1:23399634, Chr7:2062567, Chr31:9671978, Chr40:9140647, Chr48:8724756 and Chr50:4154584 were related to the intramuscular fat content of carp, and Chr20:2258997, Chr25:3825897 and Chr44:16600200 were related to the linoleic acid content of carp muscle.

10. Use of the gene as claimed in claim 9 in breeding carp with high-quality traits.

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