SNP Molecular Marker for the Trait of Litter Weight at Birth in Large White Pigs and Its Application

Abstract

The present invention belongs to the technical field of molecular genetics, and particularly relates to an SNP molecular marker for the trait of the birth litter weight of Large White pigs and its application. The SNP locus is at position 31744436 on chromosome 10 of the pig genome Sus scrofa version 11.1, and the nucleotide at the SNP locus is A / G; and the SNP locus is at position 31744633 on chromosome 10 of the pig genome Sus scrofa version 11.1, and the nucleotide at the SNP locus is A / G. The two SNP molecular markers of the present invention can be used as effective molecular markers in the molecular marker-assisted selection breeding for the trait of the birth litter weight of Large White pigs, have high accuracy for quickly and accurately screening Large White pigs with high birth litter weight, and can significantly improve the breeding efficiency of Large White pigs; and have potential application value in large-scale molecular precision breeding of Large White pigs.

Description

Technical Field

[0001] The present invention belongs to the technical field of molecular genetics, and particularly relates to a molecular marker related to the birth litter weight trait of Large White pigs and its application. Background Art

[0002] The pig industry in China has a long history, rich pig breed resources, wide distribution, and great utilization potential and broad development prospects. With the rapid development of China's economy, the living standards of the people have gradually improved, and the demand for pork has also become higher and higher. Therefore, people are increasingly concerned about how to improve the reproductive performance of pigs. The reproductive traits of sows are one of the important economic traits in the pig industry, and the level of reproductive performance is directly related to the economic benefits and production efficiency of pig farms. The reproductive performance of pigs mainly includes indicators such as total number of born piglets, number of live born piglets, number of healthy born piglets, and birth litter weight. The birth litter weight of pigs is the total weight of all piglets at birth when the sow gives birth. This indicator not only reflects the production performance of the sow but also is an important indicator to measure the health status of piglets. In addition, research shows that there is a significant positive correlation between birth litter weight and traits such as total number of born piglets, number of live born piglets, and number of healthy born piglets. Therefore, targeted improvement of the birth litter weight of pigs can significantly increase the survival rate and growth potential of piglets, thereby bringing higher economic benefits to farmers and enterprises.

[0003] The reproductive traits of sows are one of the three core traits mentioned in the "National Pig Genetic Improvement Plan (2021 - 2035)", and their importance to pig farms is self-evident. Since reproductive traits belong to low heritability traits and have complex genetic mechanisms, they are more susceptible to environmental factors than genetic effects. Therefore, the genetic progress achieved and the genetic mechanisms analyzed in reproductive traits are relatively limited. Birth litter weight is an important economic indicator of sow reproductive performance. In recent years, although scientists have identified some genetic markers and candidate genes related to birth litter weight through traditional genetic research methods such as pedigree analysis and selection experiments. However, these studies are often limited to small-scale populations and are affected by the complex interaction of genetic and environmental factors, resulting in limited reliability and application scope of the results. Therefore, it is difficult to achieve significant genetic progress relying solely on traditional breeding work. With the continuous development of Genome-wide Association Study (GWAS), genetic variations affecting pig reproductive traits can be more accurately identified. Through systematic data collection, preprocessing, association analysis, and result interpretation, the association between genetic variations and phenotypes can be revealed.

[0004] Based on the above needs, inventing a molecular marker related to the birth litter weight trait of Large White pigs to solve the problem of rapid and accurate selection in large populations is of great significance in the technical field of molecular genetics. Summary of the Invention

[0005] The present invention first provides an application of a molecular marker in the genome of a Large White pig or a substance for detecting a molecular marker in the genome of a Large White pig in identifying or assisting in identifying the trait of the litter weight at birth of a Large White pig.

[0006] Among them, the molecular marker is an SNP, the SNP locus is at position 31744436 on chromosome 10 of the Sus scrofa 11.1 version of the pig genome, and the nucleotide at the SNP locus is A / G.

[0007] In particular, the litter weight at birth of pigs with the GG genotype > the litter weight at birth of pigs with the GA genotype > the litter weight at birth of pigs with the AA genotype;

[0008] Among them, the SNP locus can also be at position 31744633 on chromosome 10 of the Sus scrofa 11.1 version of the pig genome, and the nucleotide at the SNP locus is A / G.

[0009] In particular, the litter weight at birth of pigs with the GG genotype > the litter weight at birth of pigs with the GA genotype > the litter weight at birth of pigs with the AA genotype.

[0010] Among them, the substance is an mPCR primer or a kit.

[0011] In particular, the mPCR primer sequences are as shown in SEQ ID NO.1 - SEQ ID NO.4.

[0012] Specifically, the primer sequences are:

[0013] SEQ ID NO.1: CGGAGTTCTGGTGAAGCTGGTAG;

[0014] SEQ ID NO.2: CACAAGGGAAGATCATAGGCATTT;

[0015] SEQ ID NO.3: TTTTTTTTTTTTTTTTTTTTTTTTTTGTTTAAGYGGATCTAAATAAAAGTGGAGAA;

[0016] SEQ ID NO.4: TTTTTTTTTTTTTTTTTTTTTTTACACACACACTTCATTTTTTTTTTAATTTAA.

[0017] The present invention also provides an mPCR primer for identifying or assisting in identifying the trait of the litter weight at birth of a Large White pig, and the primer sequences are as shown in SEQ ID NO.1 - SEQ ID NO.4.

[0018] The present invention further provides a kit for identifying or assisting in the identification of the litter weight trait at birth of Large White pigs, wherein the kit contains the above-mentioned mPCR primers.

[0019] The present invention further provides an application of the above-mentioned mPCR primers or kit in any of the following:

[0020] (A1) Identifying or assisting in the identification of the litter weight trait at birth of Large White pigs;

[0021] (A2) Identifying or assisting in the identification of the germplasm resources of Large White pigs;

[0022] (A3) Breeding of Large White pigs.

[0023] The present invention further provides a method for identifying or assisting in the identification of the litter weight trait at birth of Large White pigs, including the step of detecting the above-mentioned SNP loci to be tested.

[0024] The present invention further provides an application of the above method in any of the following:

[0025] (A1) Identifying or assisting in the identification of the litter weight trait at birth of Large White pigs;

[0026] (A2) Identifying or assisting in the identification of the germplasm resources of Large White pigs;

[0027] (A3) Breeding of Large White pigs.

[0028] Compared with the prior art, the present invention has at least the following beneficial effects:

[0029] 1. In actual breeding, by detecting the two SNP loci related to litter weight at birth provided by the present invention, it can be used for early selection and retention in breeding, retaining individuals with the GG genotype, and improving the litter weight at birth of Large White pigs.

[0030] 2. The mPCR primers provided by the present invention can amplify the nucleotide sequences of the above two SNP molecular markers, with high efficiency and strong specificity.

[0031] 3. Using the mPCR primers or kit or method provided by the present invention can improve the accuracy of breeding for the litter weight trait at birth of Large White pigs, and has potential application value in large-scale molecular precision breeding of Large White pigs. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] Figure 1 It is a DNA agarose gel electrophoresis result diagram of a group. In the figure, M represents DL2000 DNA Marker, and 1-16 represent DNA samples;

[0033] Figure 2 It is a principal component analysis diagram of the Large White pig population;

[0034] Figure 3Results diagram of haplotype analysis for two loci;

[0035] Figure 4 Diagram of the association analysis between the genotypes and phenotypes of two loci. Detailed implementation manners

[0036] To make the technical problems, technical solutions and advantages to be solved by the present invention clearer, the following will be described in detail with reference to the accompanying drawings and specific embodiments.

[0037] Example 1 Screening of loci related to the litter weight at birth trait of Large White pigs and development of their molecular markers

[0038] 1. Collection of genotype data of the natural population of Large White pigs

[0039] Collect 3655 Large White sows' germplasm resources to form a natural population. Use conventional methods to collect ear tissue samples from them and extract the DNA therein to obtain DNA samples of qualified quality for the above-mentioned natural population of Large White pigs, as Figure 1 shown.

[0040] Since SNP chip sequencing has certain requirements for the quality of DNA, too low DNA quality will affect the sequencing results. Therefore, 2% agarose gel electrophoresis is used to detect whether the extracted DNA is degraded. The electrophoresis results are as Figure 1 shown. It can be seen from the picture that the DNA sample bands are clear, bright, complete and without trailing, indicating that the DNA is not degraded; the detection results by an ultra-micro spectrophotometer show that the lowest concentration of DNA is 50.05 ng / ul, the highest concentration is 633.10 ng / ul, and the average concentration is 525.32 ng / ul. The minimum value of OD260 / 280 is 1.75, the maximum value is 2.06, and the average value is 1.86. It can be known from the above detection results that the DNA quality of the test population is good and meets the genotyping requirements.

[0041] 2. Phenotypic determination

[0042] Under conventional breeding conditions, investigate the litter weight at birth trait of each of the above-mentioned Large White pigs to obtain the phenotypic data of the natural population.

[0043] 3. Identification of polymorphic loci related to the litter weight at birth trait of Large White pigs and marker development

[0044] 3.1 Perform SNP genotyping on the DNA samples of qualified quality, which can be assisted by a biological company. In an embodiment of the present invention, Neogene Biotechnology (Shanghai) Co., Ltd. performs SNP genotyping (GeenSeek GGP-Porcine 50K SNP chip) on the samples collected in the present invention.

[0045] 3.2 Quality control and imputation

[0046] Comprehensive quality control is performed on the chip data obtained from SNP genotyping. The quality control criteria for the chip data in this application are as follows (the following quality control processes can all be completed using the PLINK software): SNPs with unknown physical positions or located on the X chromosome are excluded; the call rate of SNPs at a single locus reaches over 90%; the call rate of individuals reaches over 90%.

[0047] The imputed genotype data is imputed using the PHARP v3 online website. The VCF format file obtained after imputation is used to exclude loci with R2 less than 0.9 and MAF less than 0.05 using the bcftools software, and duplicate loci are removed at the same time. Finally, 13,469,068 effective SNP loci are obtained.

[0048] 3.3 Genetic background analysis

[0049] Since population stratification is one of the main reasons for false positives in GWAS analysis results, if the test samples come from different populations, it is necessary to understand the population structure of the test samples to ensure the accuracy of GWAS analysis results. In an embodiment of the present invention, the PLINK software is used to perform principal component analysis (PCA) on the sample population, perform cluster analysis on the genetic similarity of individuals, and determine whether there is population stratification; the PLINK software is used to evaluate the degree of genome-wide linkage disequilibrium (LD) of the population. The R software is used to draw the PCA scatter plot and LD decay plot. PCA is mainly used to control population stratification and standardize genetic variation, while LD analysis is used to identify and utilize linkage disequilibrium to improve the efficiency and accuracy of association analysis.

[0050] The results of the principal component analysis are as Figure 1 shown. Each point in the figure represents 1 individual. Large white pigs from different origins can be distinguished by PC1 (principal component 1) and PC2 (principal component 1). It can be seen from the figure that the samples collected in this application are divided into three populations due to different genetic backgrounds and kinship. First, the 3 populations are analyzed separately, and finally Meta-analysis is performed.

[0051] Through observation and research, it is found that these three populations have undergone 5 to 10 years of closed breeding after being introduced from the original place. Population 1 is originally from Canada and has a fast growth rate. Populations 2 and 3 are originally from France and have good reproductive performance but a slow growth rate. Population 2 was collected in Shanghai, and Population 3 was collected in Wuhan, Hubei, and the reproductive performance of Population 3 is higher than that of Population 2.

[0052] In one embodiment of the present invention, when the genetic background and kinship of the collected samples are clearly known and consistent, this step can be ignored and GWAS analysis can be directly performed.

[0053] 3.4 GWAS Analysis

[0054] The repeatability model is used to evaluate the breeding value of the initial litter weight trait, and the breeding value + residual is used as the corrected phenotypic value to replace the original phenotypic value for subsequent GWAS analysis.

[0055] To avoid false positive loci in the GWAS analysis results caused by population stratification, the present invention performs GWAS analysis on different populations separately. In this experiment, the single marker regression analysis method in GCTA software is used for GWAS analysis, and the mixed linear model is as follows:

[0056]

[0057] In the above model, y represents the corrected phenotypic value; b is the SNP effect, a represents the remaining polygenic effect, , is the individual additive genetic variance, G is the genetic relationship matrix, X and Z are the association matrices of b and a respectively; e represents the residual effect vector, , is the residual variance.

[0058] 3.5 Meta-analysis

[0059] Using the weighted Z-score method in METAL software, the results of GWAS analysis after filling the Large White pig population are used for Meta-analysis of the initial litter weight among the three populations. It converts the P-value and effect direction into signed Z-scores, and the Z-scores of each allele are weighted and summed according to the sample size of the experimental population. The weight is proportional to the size of the sample size of the experimental population. The specific calculation formula is as follows:

[0060]

[0061] Among them, represents the P-value of GWAS analysis of the i-th population; represents the cumulative distribution function of the standard normal distribution; represents the direction of the SNP effect of GWAS analysis of the i-th population; represents the size of the reference population of GWAS analysis of the i-th population.

[0062] In this experiment, the Bonferroni method was used to correct the results of single-trait GWAS and the results of cross-population meta-analysis. The genome-wide significant threshold was 0.05 / N (N is the number of SNPs), and the chromosome-level significant threshold was 1 / N. Finally, SNP locus data significantly related to the litter weight at birth trait of Large White pigs was obtained.

[0063] 3.6 Gene analysis

[0064] After obtaining the SNP loci significantly related to the litter weight at birth trait of Large White pigs by using single-trait GWAS and meta-analysis, the pig Sus scrofa 11.1 version database on the Ensemble online platform was used to search for relevant gene genetic factors within the 200 Kb region upstream and downstream of the significant SNP loci. Finally, 17 SNP loci were screened and obtained.

[0065] 3.7 Validation

[0066] Fifty Large White pigs outside the analysis population were selected to extract DNA and then pooled with equal mass. The genetic variation at the genome-wide level of this resource population was detected by using next-generation sequencing, that is, all gene loci were scanned to obtain the pooled results. And the SNP loci of the FRMD3 gene in 300 Large White pigs in the new population were scanned for large-scale validation.

[0067] 3.8 Genotyping

[0068] The multiplex PCR technique was used to genotype the SNP loci of heterozygotes and missense mutations in the exon regions of the samples, and the genotyping results of the corresponding SNP loci of 300 Large White pigs used for validation were obtained. The specific reactions are as follows:

[0069] (1) Take 1 μl of the DNA sample for 1% agarose gel electrophoresis to check the quality of the sample and estimate the concentration, and then dilute the sample to the working concentration of 5 - 10 ng / μl according to the estimated concentration.

[0070] (2) Multiplex PCR reaction:

[0071] 2a) PCR primers (as shown in Table 1):

[0072] Table 1 Multiplex PCR primers

[0073]

[0074] 2b) PCR conditions

[0075] The reaction system (20 μl) contains 1x HotStarTaq buffer, 3.0 mM Mg2+, 0.3 mM dNTP, 1 U HotStarTaq polymerase (Qiagen Inc.), 1 μl of sample DNA, and 1 μl of multiplex PCR primers.

[0076] PCR cycling program

[0077]

[0078] (3)Purification of PCR products

[0079] Add 5 U of SAP enzyme and 2 U of Exonuclease I enzyme to 20 μl of PCR products, incubate at 37 for 1 hour, then inactivate at 75 for 15 minutes.

[0080] (4)SNaPshot multiplex single-base extension reaction

[0081] 4a) Extension primers (as shown in Table 2):

[0082] Table 2 Multiplex PCR extension primers

[0083]

[0084] 4b) Extension reaction

[0085] The extension reaction system (10 μl) includes 5 μl of SNaPshot Multiplex Kit (ABI), 2 μl of purified multiplex PCR products, 1 μl of extension primer mixture, and 2 μl of ultrapure water.

[0086] Reaction program

[0087]

[0088] (5)Purification of extension products

[0089] Add 1 U of SAP enzyme to 10 μl of extension products, incubate at 37 for 1 hour, then inactivate at 75 for 15 minutes.

[0090] (6)Loading the extension products onto the ABI3730XL sequencer

[0091] Take 0.5 μl of purified extension products, mix with 0.5 μl of Liz120 SIZE STANDARD and 9 μl of Hi-Di, and incubate at 95 After denaturation for 5 minutes, the samples were loaded onto an ABI 3730XL sequencer. The sequencing results of the PCR products were aligned using GeneMapper 4.1 (Applied biosystems) software, and the sequencing peak maps were analyzed to complete genotyping. Haplotype analysis was performed on two loci, and the results are as Figure 3 shown

[0092] 3.9 Association analysis

[0093] The MIXED procedure in SAS software was used to perform association analysis between the genotypes of 17 SNP loci of FRMD3 and the trait of litter weight at birth. Finally, it was found that different genotypes at the rs3471771544 and rs3472440282 loci had a significant effect on the litter weight at birth of Large White pigs. The specific model is as follows:

[0094]

[0095] is the phenotypic value of reproductive traits, and is the litter weight at birth; is the population mean; is the genotype effect, where i represents different genotypes; is the random residual effect. The results of the association analysis between the genotypes of the two loci and the phenotype are shown in Table 3, and the least squares analysis results of the polymorphic loci are shown in Table 4. The association analysis graph between the genotypes of the two loci and the phenotype is as Figure 4 shown:

[0096] Table 3 Association analysis between the genotypes of the two loci and the phenotype

[0097]

[0098] Table 4 Least squares analysis of polymorphic loci

[0099]

[0100] From Table 3, Table 4 and Figure 4It can be seen that the two SNP sites rs3471771544 and rs3472440282 were significantly correlated with litter weight at birth (LWB) (P<0.05). The litter weight of the Large White pigs with the GG genotype of the rs3471771544 site was 19.76±2.01, the litter weight of the Large White pigs with the GA genotype was 17.48±0.72, and the litter weight of the Large White pigs with the AA genotype was 15.92±0.39. The litter weight of the Large White pigs with the GG genotype was significantly greater than that of the GA and AA genotypes; the litter weight of the Large White pigs with the GG genotype of the rs3472440282 site was 20.35±2.15, the litter weight of the Large White pigs with the GA genotype was 17.53±0.73, and the litter weight of the Large White pigs with the AA genotype was 15.92±0.39. The litter weight of the Large White pigs with the GG genotype was significantly greater than that of the GA and AA genotypes. The additive effect and dominant effect were both negative, indicating that the mutation of the two bases to A was associated with lower litter weight. Figure 2 The haplotype analysis results show that the two loci are in a relatively strong linkage state and can form a haplotype block, and the frequency of the AA haplotype is higher. The genotyping results of 300 pigs show that there are more individuals with the AA genotype at the two loci in the population, and the litter weight of the AA genotype Large White pigs is significantly lower than that of the GG genotype, and the population still has great potential for improvement. Therefore, in actual breeding, selecting and retaining the GG genotype Large White pigs can increase the gene frequency of excellent traits in the population, increase the litter weight of the population, and improve the litter weight of the population in a targeted manner to achieve the purpose of improving the production performance and economic benefits of the entire population.

[0101] Application Examples

[0102] The ear tissues of 50 large white sows with known litter weights were collected and their DNA was extracted. Then the genotypes of the two loci rs3471771544 and rs3472440282 were detected by the method in Example 1. The test results showed that the genotypes of rs3471771544 and rs3472440282 of numbers 3-7, 9, 12-13 and 34, 42, and 50 were all GG, and their litter weights were all higher than 20kg / litter, which was significantly higher than the litter weights of other numbers. It can be seen that the SNP molecular markers of the large white pig litter weight trait provided by the present invention can accurately determine the litter weight of large white sows, providing support for large white pig breeding.

[0103] It can be seen that the two loci rs3471771544 and rs3472440282 provided by the present invention can be used as effective molecular markers in the assisted selection breeding of Large White pigs' litter weight traits, and can quickly and accurately improve the litter weight traits in the population to achieve the purpose of improving the production performance and economic benefits of the entire group.

[0104] The above are the preferred embodiments of the present invention. It should be noted that for those of ordinary skill in the art, without departing from the principle of the present invention, several improvements and refinements can be made, and these improvements and refinements should also be regarded as the protection scope of the present invention.

Claims

1. Use of a molecular marker combination in the genome of Large White pigs or a substance for detecting the molecular marker combination in the genome of Large White pigs in identifying or assisting in identifying the trait of the litter weight at birth of Large White pigs; The molecular marker combination is the first SNP and the second SNP locus, wherein, The first SNP locus is at position 31744436 on chromosome 10 of the pig genome Susscrofa 11.1 version, the nucleotide at the SNP locus is A / G, and the litter weight at birth of pigs with the GG genotype > the litter weight at birth of pigs with the GA genotype > the litter weight at birth of pigs with the AA genotype; The second SNP locus is at position 31744633 on chromosome 10 of the pig genome Sus scrofa 11.1 version, the nucleotide at the SNP locus is A / G, and the litter weight at birth of pigs with the GG genotype > the litter weight at birth of pigs with the GA genotype > the litter weight at birth of pigs with the AA genotype.

2. The application according to claim 1, wherein: The substance is an mPCR primer or a kit.

3. The application according to claim 2, wherein: The sequences of the mPCR primers are as shown in SEQ ID NO.1 - SEQ ID NO.

4.

4. A method for identifying or assisting in the identification of the initial litter weight trait of Large White pigs, characterized in that: It includes the step of detecting the SNP locus described in claim 1 to be tested.

5. Use of the method according to claim 4 in identifying or assisting in identifying the trait of the litter weight at birth of Large White pigs.

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