SNP (Single Nucleotide Polymorphism) molecular marker related to pig fat content and application of SNP molecular marker

Abstract

The invention belongs to the technical field of molecular markers, and particularly discloses an SNP molecular marker related to the fat content of the whole body of a pig and application of the SNP molecular marker. Computed tomography (CT) imaging is carried out on 795 boars of Duroc, Changbai, Pietrain and other varieties, phenotypic data of the whole body fat content of the boars are accurately obtained, GWAS analysis is carried out in combination with high-throughput whole genome re-sequencing data, and finally an SNP site remarkably related to the whole body fat content is identified on the first chromosome of the boars. The site is located at the chr1: 7680166 position of an Enmbl Sscrofa 11.1 reference genome, C / G polymorphism exists, an individual carrying a CG genotype shows lower whole body fat content compared with an individual carrying a CC genotype, and the CG is a beneficial genotype.

Description

Technical Field

[0001] This invention belongs to the field of molecular marker technology, specifically relating to a single nucleotide polymorphism (SNP) molecular marker related to the total body fat content of pigs and its application in pig breeding. Background Technology

[0002] my country is a world-leading producer and consumer of pork, and pig farming plays a crucial role in the country's agriculture and national economy. With socio-economic development and rising living standards, consumers are demanding higher quality pork, with carcass fat percentage being a key indicator of both quality and economic value. Excessively high fat percentages not only affect feed conversion efficiency and increase farming costs but also contradict modern health-conscious consumption concepts. Therefore, developing superior pig breeds with low fat and high lean meat percentages is of great significance for enhancing the overall competitiveness and economic benefits of my country's pig industry.

[0003] For a long time, pig breeding has mainly relied on traditional phenotypic selection and breeding value estimation. This method has the disadvantages of long breeding cycle, low selection efficiency and high cost. Moreover, for complex traits such as fat percentage, which are controlled by multiple genes and are difficult to measure accurately, breeding progress is relatively slow.

[0004] In recent years, with the rapid development of genomics technology, molecular techniques such as genome-wide association studies (GWAS) have provided powerful tools for elucidating the genetic basis of complex traits. GWAS, by detecting the association between genome-wide genetic variations (such as SNP markers) and target traits, can efficiently and accurately identify key genes or functional loci controlling important economic traits. Currently, GWAS technology has been widely applied in livestock and poultry breeding, successfully identifying a large number of molecular markers related to traits such as growth, reproduction, and meat quality, providing a theoretical basis and technical support for marker-assisted selection (MAS) breeding. However, the number of effective and reliably validated SNP molecular markers currently available for precise selection of fat traits in pigs remains limited.

[0005] Therefore, those skilled in the art urgently need to develop a SNP molecular marker that is significantly associated with pig fat content and establish a corresponding detection method for application in early selection and genetic improvement of pigs, so as to achieve precise and efficient breeding of pig fat percentage traits. Summary of the Invention

[0006] This invention provides a SNP molecular marker that is significantly associated with the total body fat content of pigs and its application, providing a new SNP molecular marker resource for marker-assisted selection breeding of pig fat content traits.

[0007] To achieve the above objectives, the present invention adopts the following technical solution:

[0008] This invention utilizes computed tomography (CT) imaging of 795 Duroc, Landrace, and Pietrain boars to accurately obtain phenotypic data on their total body fat content. Combined with high-throughput whole-genome resequencing data, GWAS analysis was performed, ultimately identifying a SNP locus on chromosome 1 of the pig that is significantly associated with total body fat content. This locus is located at chr1:7680166 of the Ensembl Sscrofa 11.1 reference genome and exhibits C / G polymorphism. The study found that individuals carrying the CG genotype exhibited lower total body fat content compared to individuals with the CC genotype, indicating that CG is a favorable genotype. Attached Figure Description

[0009] Figure 1 This is a Manhattan plot based on GWAS analysis, with the solid line representing the significance threshold line.

[0010] Figure 2 These are the phenotypic values ​​corresponding to different genotypes of the SNP sites screened in this invention. Detailed Implementation

[0011] The technical solutions in the embodiments of the present invention will be clearly and completely described below. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0012] Example 1: Screening and identification of SNP loci associated with total body fat content in pigs

[0013] (1) Experimental population and phenotypic determination

[0014] This invention selected 795 healthy boars of four breeds—Duroc, Landrace, Pietrain, and Large White—with clear pedigrees as the experimental group.

[0015] After anesthetizing the pigs, they were placed in a prone position and scanned using a Siemens AS plus CT scanner. The scanning parameters were set as follows: 110 kV / 160 mA, 512×512 matrix, axial direction, and 5 mm slice thickness. The scan range covered the entire pig except for the head.

[0016] The acquired 3D CT image data was processed through the following steps to extract phenotypic values ​​related to pig fat content:

[0017] First, identify and remove irrelevant backgrounds such as the CT scan bed.

[0018] Second, apply a Hounsfield Unit (HU) threshold (-200 < HU < 0) to the segmented pig body image to precisely separate the adipose voxels throughout the body.

[0019] Next, perform linear interpolation on the entire CT image to obtain isotropic voxels of 1×1×1 mm, ensuring alignment with the true physical size. The HU values of all voxels are uniformly increased by 200 to ensure non - negative values in subsequent processing.

[0020] Then, generate two - dimensional sagittal plane projection images from the three - dimensional adipose volume data by summing all voxel values along the sagittal axis direction.

[0021] Finally, crop, center, normalize the generated two - dimensional projection images, scale them to the grayscale range of 0 - 255, and save them in PNG format. Subsequently, use these standardized two - dimensional projection images as inputs, and employ a deep - learning - based automated phenotypic analysis method to extract and quantify high - dimensional image variant phenotypes related to the total body fat content. These image variant phenotypes are used as the final phenotypic values for subsequent association analysis, as follows:

[0022] In the first step, the phenotypic analysis network includes an encoder and a generator. The phenotypic analysis model includes a generator and an encoder. The encoder outputs the hidden vector of the two - dimensional projection image data in the extended latent space at the layer, and the generator decodes the hidden vector at the layer into reconstructed two - dimensional projection image data;

[0023] In the second step, use the trained encoder to obtain the hidden vector of the two - dimensional projection image data in the extended latent space at the layer;

[0024] In the third step, use a data - driven method (such as independent component analysis) to decouple the extended latent space and calculate mutually independent directions in the extended latent space as interpretable bases. Each direction represents an interpretable basis, and each direction corresponds to an independent and interpretable image variation trend in the image (such as the depth of the overall color, etc.). Construct each interpretable basis as an interpretable basis vector, and construct mutually independent interpretable basis vectors;

[0025] In the fourth step, project the hidden vector of the two - dimensional projection image data in the extended latent space at the layer onto On interpretable basis vectors, obtain The scalar values ​​of each projection vector, treated as a quantitative trait, are defined as an image variation phenotype (IVP). Image variant phenotypes.

[0026] (2) Genotyping and quality control

[0027] Ear tissue samples were collected from all experimental individuals, and genomic DNA was extracted. Genotyping was performed using whole-genome resequencing. Quality control of the raw sequencing data was performed using software such as FASTP, and high-quality sequences were aligned to the pig reference genome (Sscrofa 11.1) using BWA software. Variation detection was performed using Sentieon software to obtain an initial SNP dataset. Subsequently, rigorous quality control of the genotype data was performed using PLINK v1.90 software, with the following criteria: minimum allele frequency (MAF) > 0.05, locus detection rate > 0.9, and individual detection rate > 0.9. After quality control, missing genotypes were filled using Beagle software, resulting in a high-quality SNP dataset of 17,810,683 genotypes for subsequent analysis.

[0028] (3) Genome-wide association analysis (GWAS)

[0029] GWAS analysis was performed using a mixed linear model (MLM) in rMVP software, which effectively controls for false positives caused by population stratification. The model treated variety and batch number as fixed effects, the genomic phylogenetic matrix as a random effect, and included the first three principal components (PCs) as covariates. The Bonferroni correction method was used to determine the genome-wide significance threshold.

[0030] (4) Results Analysis

[0031] GWAS results show ( Figure 1 A signal peak highly significantly associated with total body fat content was found on pig chromosome 1. The SNP locus with the smallest p-value (1.99887e-13) was located at chr1:7680166, and this locus exhibited a C / G base mutation. Genotypic effect analysis of this locus showed that ( Figure 2 Individuals with the CG genotype had significantly lower total body fat content than individuals with the CC genotype (p < 0.05), indicating that the C-to-G mutation is beneficial for reducing fat deposition.

[0032] Genotyping of the chr1:7680166 locus in breeding pig populations allows for the prioritization of individuals with the CG genotype in breeding planning, or the allocation of a higher weight to this genotype when calculating breeding values. Continuous selection and utilization of individuals carrying the favorable G allele can effectively reduce the overall body fat content and increase lean meat percentage in offspring pig herds, thereby improving pork quality and enhancing economic benefits.

Claims

1. A SNP molecular marker associated with porcine fat content, characterized in that, The nucleotide sequence of the SNP molecular marker is shown in SEQ ID NO.1 or 2, with the 51st nucleotide position being a C / G polymorphic site.

2. A reagent kit for detecting the fat content trait in pigs, characterized in that, The kit contains reagents for detecting nucleotide 7680166 on pig chromosome 1, with reference genome version Sscrofa 11.

1.

3. The application of the molecular marker of claim 1 or the kit of claim 2 in predicting the fat content trait in pigs or in pig breeding.

4. The application according to claim 3, characterized in that, Individuals with the CG genotype have lower body fat percentages compared to those with the CC genotype.

5. The application according to claim 3, characterized in that, The breeds of pigs mentioned are Duroc, Landrace, Pietrain, and Large White.

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