Gene chip based on pig functional mutation sites interfering with transcription factor binding strength and construction method therefor, and use

Abstract

The present invention relates to the technical field of pig genome breeding chips. Specifically disclosed are a gene chip based on pig functional mutation sites interfering with transcription factor binding strength and a construction method therefor, and a use. According to the chip, the impact of the regulation effect of a genome regulatory element on mutation sites is fully considered, and 81,000 mutation sites highly interfering with transcription factor binding strength are screened for from massive pig genome functional mutation sites. Compared with conventional pig breeding chips and existing functional sites, each mark on the chip provided in the present invention is more tightly associated with the phenotype. The chip designed on the basis of functional sites interfering with a transcription factor is used for breeding, so that the accuracy of subsequent breeding value calculations can be improved, thereby improving the breeding efficiency.

Description

A pig functional mutation site gene chip based on interfering transcription factor binding strength and its construction method and application

[0001] Cross-reference information

[0002] This application claims priority to Chinese patent application No. 202410160017.7 filed on February 4, 2024, the entire contents of which are incorporated herein by reference. Technical Field

[0003] The present invention belongs to the technical field of pig genome breeding chips, and in particular relates to a pig functional mutation site gene chip based on interference transcription factor binding strength, and a construction method and application thereof. Background Art

[0004] Pig breeding has a history of thousands of years. In my country, due to the complex and diverse ecological environment and socioeconomic conditions, a rich and diverse resource of local pig breeds has gradually emerged. Strengthening the development of modern biobreeding technologies, characterized by the integration of genetic technology, artificial intelligence, and other technologies, is an inevitable trend and a key breakthrough in the development of the seed industry.

[0005] Genomic breeding utilizes high-throughput sequencing technology to study populations, more precisely selecting individuals based on economic traits such as growth rate or disease resistance. This allows for the acquisition of genomic information beneficial to humans and the improvement of livestock and poultry breeding. However, current genomic breeding methods primarily rely on conventional molecular markers to identify genetic information within the genomes of livestock and poultry organisms. While this approach has been shown to improve breeding accuracy, it neglects the functional nature of genetic variation: that is, it fails to consider the regulatory effects of genomic regulatory elements on gene expression and traits.

[0006] Gene expression is influenced by regulatory elements. The identification of lineage-specific regulatory elements can reveal the genetic basis of complex phenotypes in livestock and poultry. Variation within regulatory element regions can lead to altered gene expression patterns, thereby affecting the organism's phenotype. The widespread application of genome resequencing technology has identified a vast number of genomic mutation sites, but screening the functional role of these mutation sites from this vast data set remains a challenge. Existing breeding microarrays fall into two main categories: those based on general molecular markers and those that consider molecular markers located in regulatory regions. Neither of these microarrays considers the potential interference of molecular markers with transcription factor binding. Compared to these two existing microarrays, the functional site microarray, which considers the interference of mutation sites with transcription factors, contains markers with regulatory properties. Each marker on the microarray is more closely associated with the phenotype than other marker sites. Using functional site microarrays designed based on transcription factor interference for breeding can improve the accuracy of subsequent breeding value calculations, thereby enhancing breeding efficiency. Summary of the Invention

[0007] The purpose of the present invention is to provide a method for constructing a pig functional mutation site gene chip based on interfering transcription factor binding strength.

[0008] The present invention also aims to provide a pig functional mutation site gene chip based on the interfering transcription factor binding strength and its application.

[0009] In order to achieve the above object, the present invention adopts the following technical solutions:

[0010] A method for constructing a gene chip of animal functional mutation sites based on interfering transcription factor binding strength comprises the following steps:

[0011] S1. Epigenomic data collection: Collection of epigenomic data, genomic variation data, and quantitative trait loci data;

[0012] S2. Data alignment and quality control: Perform quality control on the high-throughput sequencing data collected in S1. Align the qualified data to the corresponding animal genome. After sequence alignment, remove redundant data and merge them.

[0013] S3. Identification of genomic transcription factor binding functional regions: Based on the alignment quality control data in S2, identify genomic transcription factor binding functional regions, including chromatin open regions, nucleosome-free regions, transcription factor significant binding peaks, transcription factor footprints, transcription factor motifs, and histone H3K27ac narrow peak regions;

[0014] S4. Classification of transcription factor binding functional regions: Based on the likelihood of transcription factor binding to the functional regions of the genome, the functional regions of the genome are divided into different categories and assigned scores;

[0015] S5. Screen for functional mutation sites that strongly interfere with transcription factor binding and construct a functional site gene chip: Divide the genome into windows of 10Kb-20Kb, retain the mutation sites located in the highest-scoring area in each 10Kb-20Kb window, and then select functional mutation sites for gene chip construction based on the rule of uniform site distribution.

[0016] Furthermore, S4 of the above construction method may be specifically as follows:

[0017] S4. Division of transcription factor binding functional regions: Based on the possibility of transcription factors binding to the functional regions of the genome, the functional regions of the genome are divided into 5 major categories and 12 minor categories, and different scores are assigned according to the importance of different categories: the first category is the region with the greatest possibility of affecting transcription factor binding, which is further divided into 4 minor categories: la, lb, lc, and ld. The score of category la is 12, including the intersection of five functional regions: quantitative trait locus region, chromatin open region, nucleosome-free region, transcription factor footprint region, and transcription factor motif region completely located in the transcription factor footprint region; the score of category 1b is 11, including the intersection of four functional regions: chromatin open region, nucleosome-free region, transcription factor footprint region, and transcription factor motif region completely located in the transcription factor footprint region; the score of category 1c is 10, including quantitative trait locus region, chromatin open region, nucleosome-free region, transcription factor footprint region, and transcription factor motif region completely located in the transcription factor footprint region. The intersection of the four functional regions of the transcription factor footprint region, the score of category 1d is 9, including the intersection of the three functional regions of chromatin open region, nucleosome-free region, and transcription factor footprint region containing part of the transcription factor motif; the second category is the region with a medium possibility of affecting transcription factor binding, which is further divided into 2a, 2b, 2c, and 2d. The score of category 2a is 8, including the intersection of the four functional regions of the quantitative trait locus region, chromatin open region, transcription factor footprint region, and transcription factor motif region completely located in the transcription factor footprint region; the score of category 2b is 7, including the chromatin open region The score of category 2c is 6, including the intersection of three functional regions: the quantitative trait locus region, the chromatin open region, and the transcription factor footprint region containing part of the transcription factor motif; the score of category 2d is 5, including the intersection of two functional regions: the chromatin open region and the transcription factor footprint region containing part of the transcription factor motif; the third category is the region with a small possibility of affecting the binding of transcription factors, which is further divided into 3a and 3b. The score of category 3a is 4, including The score of category 3b is 3, including the intersection of the open chromatin region and the transcription factor motif region, or the intersection of the nucleosome-free region and the transcription factor motif region; the fourth category is the region with the least possibility of affecting transcription factor binding, with a score of 2, including the union of the four functional regions of open chromatin region, nucleosome-free region, transcription factor footprint region, and transcription factor binding site region; the fifth category is the region that may be related to gene expression, with a score of 1, including the histone H3K27ac narrow peak region.

[0018] Based on the following characteristics: (1) Life has retained many common genes and genome structures during evolution, so the functions of animal gene expression regulation and genome regulatory regions are similar; (2) The genetic mechanisms of organisms are largely similar. For example, basic genetic processes such as DNA replication, transcription, and translation are similar in most organisms; (3) Even in different species, the functions of certain genes may be conserved, that is, they perform similar biological functions in different species; (4) The same strategies can be adopted for obtaining various epigenetic data, data analysis methods, genome mutation data acquisition, and chip design ideas in the present invention. Therefore, some genomic features in pigs may share similarities with other mammals or even other animals. Animals to which the construction method of the present invention is applicable include: pigs, cattle, sheep, chickens, ducks, horses, rabbits, shrimp, shellfish, and farmed fish.

[0019] Based on the above method, the present invention screened 81,000 functional mutation sites in the porcine genome. The combination of these functional mutation sites is shown in Table 2. The physical locations of the mutation sites in Table 2 were determined based on the porcine susScr11 reference genome. Class A mutation sites exert their effects on porcine fat, muscle, liver, intestine, and hypothalamus; class B mutation sites exert their effects on porcine lungs, spleen, kidneys, and pancreas; and class C mutation sites exert their effects on porcine cerebellum, cerebrum, cerebral cortex, and heart. Probes were designed based on these functional mutation sites and their preceding and following sequences to construct a gene chip containing functional mutation sites that interfere with transcription factor binding with increased strength. This chip has the following uses:

[0020] (1) Application in pig breeding;

[0021] (2) Application in identification of pig breeds and kinship;

[0022] (3) Application in target trait analysis, such as disease resistance traits (disease resistance breeding), economic traits (backfat thickness, average daily weight gain, litter size, etc.) and traits related to basic physiological functions.

[0023] Compared with the prior art, the present invention has the following advantages and beneficial effects: (1) A new method for constructing an efficient breeding chip based on the binding strength of the interfering pig genome transcription factor based on genomic mutations is disclosed; (2) Compared with the traditional molecular marker site chip, the genomic mutation sites selected based on the binding strength of the interfering pig genome transcription factor are used for the construction of the gene chip. On the one hand, the influence of the regulatory effect of the genomic regulatory element on the mutation site is taken into account, and each marker on the chip is more closely associated with the phenotype than other common marker sites; (3) Compared with the chip considering the site located in the regulatory element region, the present invention selects mutation sites with higher binding strength of the interfering pig genome transcription factor from a large number of pig gene regulatory region mutation sites, not only taking the three-dimensional transcriptional regulation of the genome as one of the elements of gene chip construction, but also reducing the input of genomic mutations located in the regulatory region but without regulatory function through preliminary screening. In summary, the chip construction method disclosed by the present invention and the constructed pig functional site chip can improve the accuracy of subsequent breeding value calculation, thereby improving breeding efficiency, and at the same time provide a new thinking direction for the construction of gene chips, accelerating the pig genome breeding process. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] FIG1 is a technical development route of the pig functional mutation site gene chip based on interfering with the binding strength of pig genome transcription factors according to the present invention. DETAILED DESCRIPTION

[0025] The following examples are provided to facilitate a better understanding of the present invention. It should be understood that the following examples are only for illustration purposes and are not intended to limit the scope of the present invention.

[0026] Example 1 Preparation Method of Pig Whole Genome Functional Mutation Site Gene Chip (I) Acquisition of Pig Whole Genome Mutation Sites

[0027] The whole-genome variant sites of pigs provided in published studies were downloaded from the Ensemble database. The variant sites with a minimum allele frequency greater than 0.047 (MAF>0.047) in the genomic mutation data of 63 breeds in the ISwine database were combined and deduplicated to obtain 66,310,990 variant sites.

[0028] (II) Identification of functional characteristics of the pig genome

[0029] (1) Combining the ChIP-seq data and ATAC-seq data of H3K27ac from 12 tissues of Meishan, Dabai, Enshi and Duroc pigs completed by the inventors in the early stage, as well as the ChIP-seq data of H3K27ac, CTCF and ATAC-seq data of pigs collected from the NCBI database;

[0030] (2) The high-throughput data collected above were quality controlled. The quality control standards were as follows: the sequencing adapters were removed, the alignment quality of the selected bases was not less than 25, the quality distribution of each base was concentrated, there was no obvious skewed distribution, the sequence length distribution was concentrated, there were no excessive short sequences, the proportion of repeated sequences was low, the proportion of the ATCG four bases was uniform, and there was no obvious GC separation. The data that passed the quality control were aligned to the porcine genome (susScr11.fa), and the correlation between biological replicates was calculated. The data with a correlation greater than 0.8 were used to detect signal peaks.

[0031] (3) ATAC-seq data were detected using MACS2 to detect open chromatin regions (OCRs) and their peaks were filtered, requiring a P value < 1 × 10 -6 The H3K27ac ChIP-seq data were analyzed using the "-nfr" parameter of Homer v4.11.1 to identify nucleosome-free regions (NFRs). The H3K27ac ChIP-seq data were analyzed using MACS2 included in the ENC0DE chipseq_pipeline to detect histone H3K27ac narrow peaks, and the peaks were filtered, requiring Pva1ue < 1 × 10 -5 The CTCF ChIP-seq data were detected using the spp tool in the ENCODE chipseq_pipeline to detect transcription factor binding sites (TF-binding sites), and the peaks were filtered, requiring a P value < 1 × 10 -5 ATAC-seq data were detected using the "--broad" parameter of MACS2 software and the peaks were filtered with a P value < 1 × 10 -5 , then HINT-Rogulatory Analysis Toolbox (v0.13.2) software was used to identify transcription factor footprints (footprint);

[0032] (4) The OCR, NFR, footprint, and TF-binding site regions identified above were merged and the transcription factor motifs were identified using the “fimo” command of MEME (v5.5.5) software, with a P value of 5 × 10 -6 .

[0033] (III) Classification of pig genome-wide functional characteristics

[0034] (1) Collect quantitative trait locus (QTL) data from the Animal QTL database;

[0035] (2) Based on the genomic functional regions and quantitative trait loci data identified in the above steps, the genomic functional regions are divided into different categories and assigned scores according to the possibility of transcription factors binding to the genomic functional regions. The specific classification criteria are shown in Table 1.

[0036] Table 1. Criteria for classifying the importance of genomic functional regions

[0037] Specifically, the first category is the region with the greatest possibility of affecting transcription factor binding, which is further divided into four subcategories: Category 1a: the intersection of five functional regions, namely, quantitative trait locus region, chromatin open region, nucleosome-free region, transcription factor footprint region, and transcription factor motif region completely located in the transcription factor footprint region, with a score of 12; Category 1b: the intersection of four functional regions, namely, chromatin open region, nucleosome-free region, transcription factor footprint region, and transcription factor motif region completely located in the transcription factor footprint region, with a score of 11; Category 1c: the intersection of four functional regions, namely, quantitative trait locus region, chromatin open region, nucleosome-free region, and transcription factor footprint region containing part of the transcription factor motif, with a score of 10; Category 1d: the intersection of three functional regions, namely, chromatin open region, nucleosome-free region, and transcription factor footprint region containing part of the transcription factor motif, with a score of 9. The second category is regions with a medium possibility of affecting transcription factor binding, which are further divided into four subcategories: Category 2a: the intersection of four functional regions, namely, the quantitative trait locus region, the chromatin access region, the transcription factor footprint region, and the transcription factor motif region completely located in the transcription factor footprint region, with a score of 8; Category 2b: the intersection of three functional regions, namely, the chromatin access region, the transcription factor footprint region, and the transcription factor motif region completely located in the transcription factor footprint region, with a score of 7; Category 2c: the intersection of three functional regions, namely, the quantitative trait locus region, the chromatin access region, and the transcription factor footprint region containing part of the transcription factor motif, with a score of 6; Category 2d: the intersection of two functional regions, namely, the chromatin access region and the transcription factor footprint region containing part of the transcription factor motif, with a score of 5. The third category is regions with a low likelihood of affecting transcription factor binding, which are further divided into two subcategories: Category 3a: The intersection of three functional regions: chromatin open regions, nucleosome-free regions, and transcription factor footprint regions, with a score of 4; Category 3b: The intersection of chromatin open regions and transcription factor motif regions, or the intersection of nucleosome-free regions and transcription factor motif regions, with a score of 3. Category 4 is the region with the lowest likelihood of affecting transcription factor binding, including the intersection of four functional regions: chromatin open regions, nucleosome-free regions, transcription factor footprint regions, and transcription factor binding site regions, with a score of 2. Category 5 is regions potentially associated with gene expression, including narrow histone H3K27ac peak regions, with a score of 1.

[0038] (IV) Screening of pig genome mutation sites

[0039] Functional mutation sites located in the first and second categories of regions in (III) were extracted and defined as those that strongly interfere with transcription factor binding. A total of 250,188 significant genomic variant sites were identified. The genome was divided into 20Kb windows, and the variant sites located in the regions with the highest scores in each 20Kb window were retained. This screened out 145,120 functional mutation sites. Based on the rule of uniform site distribution, 81,000 functional mutation sites were selected for constructing a gene chip containing functional mutation sites that strongly interfere with transcription factor binding. The locations of these 81,000 functional mutation sites in the porcine reference genome (susScr11) are shown in Table 2.

[0040] Table 2 Information on pig functional mutation sites based on high-intensity interfering transcription factor binding

[0041] (V) Construction of gene chips

[0042] (1) Based on the 81,000 functional mutation sites screened in (IV) above and the 60 bp sequences before and after them, probes were designed and labeled with biotin.

[0043] (2) Biotin-modified probes are used in liquid to hybridize with the target region of the genome to form a double strand.

[0044] (3) Streptavidin-coated magnetic beads are then used to molecularly adsorb the biotin-modified probe, thereby capturing the target hybridized with the probe.

[0045] (4) The captured target sequence is eluted, amplified, and sequenced to finally obtain the genotype of the target mutation site.

[0046] Example 2 Application of pig functional mutation site gene chip in breeding

[0047] Backfat thickness and daily weight gain are two important economic traits in pig farming. Among them, daily weight gain is directly related to the growth rate of pigs and directly determines the economic benefits of pigs. In market transactions, the price of lean meat is much higher than that of fat meat. The thickness of pig backfat directly affects the lean meat rate and growth. The thicker the backfat, the lower the lean meat rate. In breeding work, improving the daily weight gain of pigs and reducing the backfat thickness are one of the breeding goals. Daily weight gain and backfat thickness are affected by the gene expression of muscles, livers, and adipose tissues. Combined with the pig functional mutation site gene chip based on the interference transcription factor binding intensity designed by the present invention, combined with the functional mutation sites with high intensity interference transcription factor binding in muscles, livers, and adipose tissues, the breeding values ​​of daily weight gain and pig backfat thickness are evaluated.

[0048] (1) Measured the backfat thickness of 867 individuals and the daily weight gain phenotypic data of 872 individuals, and collected and extracted DNA samples from the aforementioned individuals;

[0049] (2) Perform quality control, capture, library construction, and sequencing of DNA samples, and perform genotyping after obtaining raw data;

[0050] (3) Combined with the genomic mutation sites screened in Example 1, 10,544, 6,049, and 3,801 high-intensity interference transcription factor binding function mutation sites belonging to the first and second categories, located in important functional regions of the pig genome regulating economic traits, were obtained in pig muscle, liver, and adipose tissue, respectively;

[0051] (4) Use the shuf command to randomly select 11,000 common marker sites across the entire genome as a control group;

[0052] (5) Combining the variant sites screened in muscle, liver, and adipose tissue in (3) above, the GBLUP model was used to construct four different genomic relationship matrices to evaluate the accuracy of the breeding values ​​of pig backfat thickness and daily weight gain.

[0053] Table 3. Reliability of estimated breeding values ​​for average daily gain and backfat thickness

[0054] a Note: The numbers with superscript a in the table represent the reliability of breeding calculation without specific units. The distribution range of the numbers is [0-1]. The larger the number, the higher the reliability.

[0055] As shown in Table 3, the reliability of pig backfat thickness predicted based on high-intensity interference transcription factor binding sites in muscle, liver and fat is similar (~0.31-0.38), while the reliability of prediction using 11K randomly selected sites is the lowest (~0.27). Similarly, the reliability of average daily weight gain predicted based on high-intensity interference transcription factor binding sites in the three tissues is higher than that of randomly selected SN sites. In general, the accuracy of breeding value assessment using mutation sites in the functional site gene chip disclosed in the present invention is improved by 19% to 42%. Under normal circumstances, the accuracy of breeding value assessment using ordinary marker site chips and additional algorithm optimization strategies is less than 5%. Therefore, this result shows that the mutation sites located in the important functional regions of the pig genome that regulate economic traits screened by the present invention are indeed effective in improving the reliability of breeding value assessment.

[0056] In addition, breeding for other traits related to disease resistance and pig physiological function can adopt the same strategies as those for backfat thickness and daily weight gain mentioned above. Therefore, using functional loci gene chips designed based on interference with transcription factor binding can improve the accuracy of subsequent breeding value calculations, thereby increasing breeding efficiency.

[0057] Example 3 Application of pig functional mutation site gene chip in pig breed and kinship identification

[0058] The mutation data of the functional locus gene chip constructed by the present invention are derived from 63 Chinese and foreign pig breeds. The breed-specific loci are fully considered when constructing the chip, so it can be used for pig breed identification. Specific steps:

[0059] (1) using the molecular probe combination of the present invention to detect genomic mutations in pigs to be identified;

[0060] (2) Analyze the relationship between pigs and known breeds using genomic mutations combined with phylogenetic evolutionary trees and population structure;

[0061] (3) Determine the breed of pigs through evolutionary tree and population structure.

[0062] Example 4 Application of pig functional mutation site gene chip in pig genetic relationship identification

[0063] The functional locus gene chip constructed by the present invention has sites evenly distributed on the pig genome and takes into account mutation data from different databases and different pig breeds, so it can be used for pig kinship identification. Specific steps:

[0064] (1) Detecting genomic mutations in pig populations using the molecular probe combination of the present invention;

[0065] (2) Use genomic mutation information to construct a kinship matrix of pig populations;

[0066] (3) Set a threshold based on kinship, divide the matrix according to the threshold, and determine the kinship of each individual in the group.

Claims

1. A method for constructing a gene chip of animal functional mutation sites based on interfering transcription factor binding strength, characterized in that: The following steps are involved: S1. Epigenomic data collection: Collection of epigenomic data, genomic variation data, and quantitative trait loci data; S2. Data alignment and quality control: Perform quality control on the high-throughput sequencing data collected in S1. Align the qualified data to the corresponding animal genome. After sequence alignment, remove redundant data and merge them. S3. Identification of genomic transcription factor binding functional regions: Based on the alignment quality control data in S2, identify genomic transcription factor binding functional regions, including chromatin open regions, nucleosome-free regions, transcription factor significant binding peaks, transcription factor footprints, transcription factor motifs, and histone H3K27ac narrow peak regions; S4. Classification of transcription factor binding functional regions: Based on the likelihood of transcription factor binding to the functional regions of the genome, the functional regions of the genome are divided into different categories and assigned scores; S5. Screen for functional mutation sites that strongly interfere with transcription factor binding and construct a functional site gene chip: Divide the genome into windows of 10Kb-20Kb, retain the mutation sites located in the highest-scoring area in each 10Kb-20Kb window, and then select functional mutation sites for gene chip construction based on the rule of uniform site distribution.

2. The construction method according to claim 1, wherein the animal comprises: Pigs, cattle, sheep, chickens, ducks, horses, rabbits, shrimps, shellfish, and farmed fish.

3. The construction method according to claim 1, wherein S4. Division of transcription factor binding functional regions: based on the possibility of transcription factors binding to genomic functional regions, the genomic functional regions are divided into 5 major categories and 12 minor categories, and different scores are assigned according to the importance of different categories: the first category is the region with the greatest possibility of affecting transcription factor binding, which is further divided into 1a, 1b, 1c, and 1d. The score of category la is 12, including the intersection of five functional regions: quantitative trait locus region, chromatin open region, nucleosome-free region, transcription factor footprint region, and transcription factor motif region completely located in the transcription factor footprint region; the score of category 1b is 11, including chromatin open region, nucleosome-free region, transcription factor footprint region, and transcription factor motif region completely located in the transcription factor footprint region. The intersection of four functional regions: the microsomal region, the transcription factor footprint region, and the transcription factor motif region completely located in the transcription factor footprint region. The score of category 1c is 10, including the intersection of four functional regions: the quantitative trait locus region, the chromatin open region, the nucleosome-free region, and the transcription factor footprint region containing part of the transcription factor motif. The score of category 1d is 9, including the intersection of three functional regions: the chromatin open region, the nucleosome-free region, and the transcription factor footprint region containing part of the transcription factor motif. The second category is the region with a medium possibility of affecting transcription factor binding, which is further divided into 2a, 2b, 2c, and 2d. The score of category 2a is 8, including the quantitative trait locus region, the chromatin open region, the nucleosome-free region, and the transcription factor footprint region containing part of the transcription factor motif. The score of category 2b is 7, including the intersection of the three functional regions of chromatin open region, transcription factor footprint region and transcription factor motif region completely located in the transcription factor footprint region; the score of category 2c is 6, including the intersection of the three functional regions of quantitative trait locus region, chromatin open region and transcription factor footprint region containing part of transcription factor motif; the score of category 2d is 5, including the intersection of the two functional regions of chromatin open region and transcription factor footprint region containing part of transcription factor motif; the third category is the region that affects transcription factor binding. Regions with lower likelihood are further divided into two subcategories, 3a and 3b. Category 3a has a score of 4, including the intersection of three functional regions: chromatin open region, nucleosome-free region, and transcription factor footprint region; Category 3b has a score of 3, including the intersection of chromatin open region and transcription factor motif region, or the intersection of nucleosome-free region and transcription factor motif region; Category 4 is the region with the least possibility of affecting transcription factor binding, with a score of 2, including the union of four functional regions: chromatin open region, nucleosome-free region, transcription factor footprint region, and transcription factor binding site region; Category 5 is the region that may be related to gene expression, with a score of 1, including the histone H3K27ac narrow peak region.

4. A molecular probe combination, characterized in that The molecular probe combination detects the Class A mutation site combination in Table 2. The tissues in which the Class A mutation sites in Table 2 exert their effects are fat, muscle, liver, intestine and hypothalamus of pigs.

5. A molecular probe combination, characterized in that The molecular probe combination detects the Class B mutation site combination in Table 2. The tissues in which the Class B mutation sites in Table 2 exert their effects are the lung, spleen, kidney and pancreas of pigs.

6. A molecular probe combination, characterized in that The molecular probe combination detects the Class C mutation site combination in Table 2. The tissues in which the Class C mutation sites in Table 2 exert their effects are the cerebellum, cerebrum, cerebral cortex and heart of pigs.

7. A molecular probe combination, characterized in that The molecular probe combination detects the combination of Class A, Class B and Class C mutation sites shown in Table 2, and the physical positions of the mutation sites in Table 2 are determined based on the pig susScr11 reference genome.

8. A pig functional mutation site gene chip based on the binding strength of interfering transcription factors, characterized in that: The gene chip is loaded with the molecular probe combination according to any one of claims 4 to 7.

9. Use of the molecular probe combination according to any one of claims 4 to 7 in pig growth and breeding.

10. Use of the molecular probe combination according to any one of claims 4 to 7 in pig disease resistance breeding.

11. Use of the molecular probe combination according to any one of claims 4 to 7 in breeding for traits related to basic physiological functions of pigs.

12. Use of the molecular probe combination according to any one of claims 4 to 7 in pig breeding or identification of breeds and kinship.

13. Use of the gene chip according to claim 8 in pig breeding or identification of breeds and kinship.

14. The use according to claim 13, wherein pig breeding refers to pig growth breeding, disease resistance breeding or breeding for traits related to basic physiological functions.

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