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

Abstract

The present invention belongs to the technical field of pig genome breeding chips, and specifically discloses a pig functional mutation site gene chip based on the binding strength of interference transcription factors, and its construction method and application. The chip fully considers the influence of the regulatory effect of genomic regulatory elements on mutation sites, and screens out 81,000 mutation sites with higher binding strength of interference transcription factors from a large number of pig genome functional mutation sites. Compared with traditional pig breeding chips and existing functional sites, each marker on the chip provided by the present invention is more closely associated with the phenotype. Breeding with a functional site chip designed based on the interference effect of transcription factors can improve the accuracy of subsequent breeding value calculation, thereby improving breeding efficiency.

Description

Technical Field

[0001] 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

[0002] Pig breeding has a history of thousands of years. In my country, due to the complex and diverse ecological environment and socio-economic conditions, a rich and colorful local pig breed resource has gradually been formed. Before the 20th century, my country mainly bred and raised local pigs that were adapted to the local environment, had strong disease resistance, and had an appearance that suited local cultural aesthetics and consumer needs. After the founding of the People's Republic of China and before the reform and opening up, my country's pig production was based on binary hybridization of introduced fat-type and dual-purpose breeds with local pigs, and the selection of some excellent hybrid combinations to produce fattening pigs. After the reform and opening up, lean meat pigs have become more and more popular. The construction of a good breeding system has been continuously advanced, and a framework for breeding, expansion, promotion and application has been initially formed. In recent years, although the application of genomic breeding has accelerated the breeding process, my country's breeding of pigs is still immature compared to breeding powers. At present, my country still has to face problems such as an imperfect breeding system, relatively lagging application of new technologies, low investment in breeding, and weak sustainability. Therefore, strengthening the development of modern biological breeding technology marked by the integration of science and technology such as genetic technology and artificial intelligence is an inevitable trend and an important bottleneck for breaking through the development of the seed industry.

[0003] Genomic breeding is a method of using high-throughput sequencing technology to study populations, select individuals more accurately based on economic traits such as growth rate or disease resistance, and obtain genomic information that is beneficial to humans to improve livestock and poultry breeding. However, current genomic breeding methods mainly rely on common molecular markers to identify genetic information in the genome of livestock and poultry organisms. Although this method has been proven to improve breeding accuracy, it ignores the functional attributes of genetic variation: that is, it does not consider the regulatory effects of genomic regulatory elements on gene expression and traits.

[0004] Gene expression is affected by regulatory elements. The identification of lineage-specific regulatory elements can reveal the genetic basis of complex phenotypes in livestock and poultry, and the occurrence of variations in the regulatory element region may lead to changes in gene expression patterns, thereby affecting the phenotype of the organism. At present, the widespread application of genome resequencing technology has identified a large number of genomic mutation sites, but it is still a challenge to screen the functions of mutation sites from massive data. Currently, the existing breeding chips are mainly divided into two categories: breeding chips based on ordinary molecular markers and breeding chips that consider molecular markers located in regulatory regions. Both chips do not consider the interference of molecular markers on transcription factor binding. Compared with the two existing chips, the functional site chip that considers the interference of mutation sites on transcription factors contains markers with regulatory functional attributes. Each marker on the chip is more closely associated with the phenotype than other marker sites. The use of functional site chips designed based on the interference of transcription factors for breeding can improve the accuracy of subsequent breeding value calculations, thereby improving breeding efficiency. Summary of the invention

[0005] 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.

[0006] The present invention also aims to provide a pig functional mutation site gene chip based on the interference transcription factor binding strength and its application.

[0007] In order to achieve the above object, the present invention adopts the following technical solutions:

[0008] A method for constructing a gene chip of animal functional mutation sites based on interference transcription factor binding strength comprises the following steps:

[0009] S1. Epigenomic data collection: Collection of epigenomic data, genomic variation data, and quantitative trait loci data;

[0010] S2. Data comparison and quality control: Perform quality control on the high-throughput sequencing data collected in S1, and align the qualified data to the corresponding animal genome. After the sequence alignment is completed, remove the redundant data and merge them;

[0011] S3. Identification of genomic transcription factor binding functional regions: Based on the comparison quality control data in S2, the genomic transcription factor binding functional regions were identified, including chromatin open regions, nucleosome-free regions, transcription factor significant binding peaks, transcription factor footprints, transcription factor motifs and histone H3K27ac narrow peak regions;

[0012] S4. Division of transcription factor binding functional regions: According to the possibility of transcription factors binding to the genomic functional regions, the genomic functional regions are divided into different categories and assigned scores;

[0013] S5. Screening of functional mutation sites that strongly interfere with transcription factor binding and construction of functional site gene chips: Divide the genome into windows of 10Kb-20Kb, retain the mutation sites 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.

[0014] Furthermore, S4 of the above construction method may be specifically as follows:

[0015] 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 1a, 1b, 1c, and 1d There are 4 subcategories, the score of category 1a 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 the intersection of four functional regions: quantitative trait locus region, chromatin open region, nucleosome-free region, and 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: 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. There are 4 subcategories, the score of category 2a is 8, including the intersection of four functional regions: 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 intersection of three functional regions: 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 three functional regions: quantitative trait locus region, chromatin open region, and 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: chromatin open region and transcription factor footprint region containing part of the transcription factor motif; the third category is the region with a smaller possibility of affecting transcription factor binding, which is further divided into 3a and 3b. There are 2 subcategories. 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 related to gene expression, with a score of 1, including the histone H3K27ac narrow peak region.

[0016] Based on the following characteristics: (1) Life has retained many common genes and genome structures during the evolution process, so the functions of animal gene expression regulation and genome regulatory regions are similar, (2) The genetic mechanisms of organisms are similar to a large extent, 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 on 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, shrimps, shellfish, and farmed fish.

[0017] Based on the above method, the present invention screened out 81,000 functional mutation sites in the pig genome, the combination of which is shown in Table 2. The physical positions of the mutation sites in Table 2 are determined based on the pig susScr11 reference genome, where the tissues in which the A-type mutation sites exert their effects are the pig's fat, muscle, liver, intestine and hypothalamus, the tissues in which the B-type mutation sites exert their effects are the pig's lungs, spleen, kidneys and pancreas, and the tissues in which the C-type mutation sites exert their effects are the pig's cerebellum, brain, cerebral cortex and heart. Probes are designed based on the functional mutation sites and their preceding and following sequences to construct a gene chip containing functional mutation sites with higher binding strength of interfering transcription factors, and the chip has the following uses:

[0018] (1) Application in pig breeding;

[0019] (2) Application in identification of pig breeds and kinship;

[0020] (3) Application in target trait analysis, such as disease resistance traits (disease resistance breeding), economic traits (back fat thickness, average daily weight gain, litter size, etc.) and traits related to basic physiological functions.

[0021] 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 mutation is disclosed; (2) Compared with the traditional molecular marker site chip, the genomic mutation site selected according to the binding strength of the interfering pig genome transcription factor is 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 mutation sites in the pig gene regulatory region, not only taking the three-dimensional transcriptional regulation of the genome as one of the elements of the gene chip construction, but also reducing the input of genomic mutations located in the regulatory region but not having regulatory functions through preliminary screening. In summary, the chip construction method disclosed in 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, and accelerate the pig genome breeding process. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 This is the technical development route of the pig functional mutation site gene chip based on interfering with the binding strength of pig genome transcription factors described in the present invention. DETAILED DESCRIPTION

[0023] 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.

[0024] Example 1 Preparation Method of Pig Whole Genome Functional Mutation Site Gene Chip (I) Acquisition of Pig Whole Genome Mutation Sites

[0025] The whole genome variant sites of pigs provided in published studies were downloaded from the Ensemble database. The variant sites with the minimum allele frequency greater than 0.047 (MAF>0.047) in the genome mutation data of 63 breeds in the ISwine database were deduplicated to obtain 66,310,990 variant sites.

[0026] 2. Identification of functional characteristics of the whole pig genome

[0027] (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, and collecting the ChIP-seq data of H3K27ac, ChIP-seq data of CTCF and ATAC-seq data of pigs from the NCBI database;

[0028] (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 4 bases was uniform, there was no obvious GC separation, and the data that passed the quality control were aligned to the porcine genome (susScr11.fa). The correlation between biological replicates was then calculated, and the data with a correlation greater than 0.8 were used to detect signal peaks;

[0029] (3) ATAC-seq data were detected using MACS2 to detect open chromatin regions (OCRs) and their peaks were filtered with a P value < 1 × 10 -5 ; H3K27ac ChIP-seq data were used to identify nucleosome-free regions (NFRs) using the “-nfr” parameter of Homer v4.11.1; H3K27ac ChIP-seq data were used to detect histone H3K27ac narrow peaks using MACS2 in ENCODE chipseq_pipeline, and the peaks were filtered with a P value < 1 × 10 -5 CTCF ChIP-seq data were detected using the spp included in the ENCODE chipseq_pipeline to detect transcription factor binding sites (TF-binding sites), and the peaks were filtered, requiring P value < 1 × 10 -5 ATAC-seq data were detected using the "--broad" parameter of MACS2 software and the peaks were filtered, requiring P value < 1 × 10 -5 , then the HINT-Regulatory Analysis Toolbox (v0.13.2) software was used to identify transcription factor footprints;

[0030] (4) The OCR, NFR, footprint, and TF-binding site regions identified above were combined, and the “fimo” command of MEME (v5.5.5) software was used to identify transcription factor motifs, with a P value of <5×10 -6 .

[0031] 3. Classification of pig genome-wide functional characteristics

[0032] (1) Collect quantitative trait locus (QTL) data from the Animal QTL database;

[0033] (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.

[0034] Table 1. Criteria for classifying the importance of genomic functional regions

[0035]

[0036] Specifically, the first category is the region with the greatest possibility of affecting transcription factor binding, which is further divided into 4 subcategories: Category 1a: the intersection of five functional regions, namely, quantitative trait loci 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 loci 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 the region with a medium possibility of affecting transcription factor binding, which is further divided into 4 subcategories: Category 2a: the intersection of four functional regions, namely, the quantitative trait locus region, the chromatin open 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 open 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 open 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 open region and the transcription factor footprint region containing part of the transcription factor motif, with a score of 5. The third category is the region with a low possibility of affecting the binding of transcription factors, which is further divided into two subcategories: Category 3a: the intersection of the three functional regions of chromatin open region, nucleosome-free region, and transcription factor footprint region, with a score of 4; Category 3b: the intersection of the chromatin open region and the transcription factor motif region, or the intersection of the nucleosome-free region and the transcription factor motif region, with a score of 3. Category 4 is the region with the least possibility of affecting the binding of transcription factors, including the union of the four functional regions of chromatin open region, nucleosome-free region, transcription factor footprint region, and transcription factor binding site region, with a score of 2. Category 5 is the region related to gene expression, including the histone H3K27ac narrow peak region, with a score of 1.

[0037] (IV) Screening of pig genome mutation sites

[0038] Functional mutation sites located in the first and second categories of regions in (III) were extracted and defined as functional mutation sites that interfered with the binding of transcription factors with high intensity. A total of 250,188 important genomic mutation sites were obtained. The genome was divided into windows of 20Kb in size, and the mutation sites located in the highest-scoring regions in each 20Kb window were retained. 145,120 functional mutation sites were screened out, and 81,000 were selected according to the rule of uniform site distribution to construct a gene chip containing functional mutation sites that interfered with the binding of transcription factors with high intensity. The locations of the 81,000 functional mutation sites in the pig reference genome (susScr11) are shown in Table 2.

[0039] Table 2 Information on pig functional mutation sites based on high-intensity interfering transcription factor binding

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[0227] (V) Construction of gene chip

[0228] (1) Design probes based on the 81,000 functional mutation sites screened in (IV) above and the 60 bp sequences before and after them, and label the probes with biotin.

[0229] (2) Use biotin-modified probes to hybridize with the target genomic region in liquid to form a double strand.

[0230] (3) Streptavidin-coated magnetic beads are then used to molecularly adsorb the biotin-modified probe, thereby capturing the target hybridized with the probe.

[0231] (4) The captured target sequence is eluted, amplified, and sequenced to finally obtain the genotype of the target mutation site.

[0232] Example 2 Application of pig functional mutation site gene chip in breeding

[0233] Back fat 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 back fat directly affects the lean meat rate and growth. The thicker the back fat, the lower the lean meat rate. In breeding work, improving the daily weight gain of pigs and reducing the back fat thickness are one of the breeding goals. Daily weight gain and back fat thickness are affected by the gene expression of muscle, liver, and adipose tissue. 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 muscle, liver, and adipose tissue, the breeding value of daily weight gain and pig back fat thickness is evaluated.

[0234] (1) The back fat thickness of 867 individuals and the daily weight gain phenotypic data of 872 individuals were measured, and DNA samples of the aforementioned individuals were collected and extracted;

[0235] (2) Quality inspection, capture, library construction and sequencing of DNA samples, and genotyping after obtaining raw data;

[0236] (3) In combination 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;

[0237] (4) Use the shuf command to randomly select 11,000 common marker sites in the whole genome as the control group;

[0238] (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.

[0239] Table 3. Reliability of estimated breeding values ​​for average daily gain and backfat thickness

[0240]

[0241] a Note: The numbers with superscript a in the table represent the reliability of breeding calculations. There is no specific unit and the distribution range of the numbers is [0-1]. The larger the number, the higher the reliability.

[0242] 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 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 evaluating breeding values.

[0243] In addition, breeding for other traits related to disease resistance and pig physiological functions can adopt the same strategy as the two growth traits of backfat thickness and daily weight gain mentioned above. Therefore, the use of functional site gene chips designed based on interference with transcription factor binding for breeding can improve the accuracy of subsequent breeding value calculations, thereby improving breeding efficiency.

[0244] Example 3 Application of pig functional mutation site gene chip in identification of pig breeds and kinship

[0245] The mutation data of the functional site gene chip constructed by the present invention are derived from 63 Chinese and foreign pig breeds. The breed-specific sites are fully considered when constructing the chip, so it can be used for pig breed identification. Specific steps:

[0246] (1) using the molecular probe combination of the present invention to detect genomic mutations in pigs to be identified;

[0247] (2) Analyze the relationship between pigs and known breeds using genomic mutations combined with phylogenetic evolutionary trees and population structure;

[0248] (3) Determine the breed of pigs through evolutionary trees and population structure.

[0249] Example 4 Application of pig functional mutation site gene chip in pig genetic relationship identification

[0250] The loci of the functional locus gene chip constructed by the present invention are evenly distributed on the pig genome, and the mutation data of different databases and different pig breeds are taken into account, so it can be used for pig kinship identification. Specific steps:

[0251] (1) Detecting genomic mutations in pigs using the molecular probe combination of the present invention;

[0252] (2) Use genomic mutation information to construct a kinship matrix of pig populations;

[0253] (3) Set a kinship threshold, 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 comparison and quality control: Perform quality control on the high-throughput sequencing data collected in S1, and align the qualified data to the corresponding animal genome. After the sequence alignment is completed, remove the redundant data and merge them; S3. Identification of genomic transcription factor binding functional regions: Based on the comparison quality control data in S2, the genomic transcription factor binding functional regions were identified, 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. 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 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, and the first category score is 9-12; the second category is the region with a medium possibility of affecting transcription factor binding, and the second category score is 5-8; the third category is the region with a small possibility of affecting transcription factor binding, and the third category score is 3-4; the fourth category is the region with the least possibility of affecting transcription factor binding, and the fourth category score is 2; the fifth category is the region related to gene expression, and the fifth category score is 1; S5. Screening of functional mutation sites that strongly interfere with transcription factor binding and construction of functional site gene chips: 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, farmed fish.

3. The construction method according to claim 1, wherein S4. Division of transcription factor binding functional regions: the genomic functional regions are divided into 5 major categories and 12 minor categories. The first category is further divided into 4 minor categories, namely, 1a, 1b, 1c, and 1d. The score of category 1a is 12, including the intersection of five functional regions, namely, the quantitative trait locus region, the chromatin open region, the nucleosome-free region, the transcription factor footprint region, and the 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, namely, the chromatin open region, the nucleosome-free 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, namely, 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, namely, 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 further divided into 2a, 2b, 2c, and 2d. The score of category 2a is 8, including the intersection of four functional regions: 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 intersection of three functional regions: 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 three functional regions: quantitative trait locus region, chromatin open region, and 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: chromatin open region and transcription factor footprint region containing part of the transcription factor motif. The third category is further divided into 3a and 3b. There are 2 subcategories. Category 3a has a score of 4, including the intersection of the three functional regions of chromatin open region, nucleosome-free region, and transcription factor footprint region; category 3b has a score of 3, including the intersection of the chromatin open region and the transcription factor motif region, or the intersection of the nucleosome-free region and the transcription factor motif region; the fourth category has a score of 2, including the union of the four functional regions of chromatin open region, nucleosome-free region, transcription factor footprint region, and transcription factor binding site region; the fifth category has 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 combination of Class A mutation sites in Table 2. The tissues in which the Class A mutation sites in Table 2 exert their effects are pig fat, muscle, liver, intestine and hypothalamus. The physical positions of the mutation sites in Table 2 are determined based on the pig susScr11 reference genome.

5. A molecular probe combination, characterized in that: The molecular probe combination detects the combination of Class B mutation sites in Table 2. The tissues in which the Class B mutation sites in Table 2 exert their effects are the lungs, spleen, kidneys and pancreas of pigs. The physical locations of the mutation sites in Table 2 are determined based on the pig susScr11 reference genome.

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

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 interference 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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