SNP (Single Nucleotide Polymorphism) haplotype molecular marker related to rib logarithmic character of Mishuangsu pig and application thereof

Abstract

The invention discloses an SNP (Single Nucleotide Polymorphism) haplotype molecular marker related to the logarithmic character of ribs of a Mishuangsu pig and application of the SNP haplotype molecular marker. The molecular marker is characterized in that (1) the SNP polymorphism of a chr791137916 site is G / T; (2) the SNP (Single Nucleotide Polymorphism) polymorphism of the chr791167100 site is A / G; and (3) the SNP polymorphism of the chr791517487 site is T / A, and the molecular marker is mainly used for screening / identifying excellent varieties. The method has the advantages that the breeding efficiency and the selection accuracy can be improved, meanwhile, the gene frequency of the excellent economic character of the multi-rib logarithm in a group can be improved, the rib logarithm character of the group is improved in a targeted mode, and therefore the carcass performance and meat production economic benefits of the whole group are improved.

Description

Technical Field

[0001] This invention belongs to the field of animal genetics and breeding technology, specifically the field of SNP haplotype molecular markers related to the logarithmic traits of ribs in Plum Blossom Star Pig and their applications. Background Technology

[0002] my country has a long history of pig farming and abundant pig breeds. Local Chinese pigs, as an important component of my country's pig germplasm resource bank, have developed unique genetic backgrounds and production performance through long-term natural and artificial selection. The Meihuaxing pig, belonging to the Yangxin pig subgroup, is one of the excellent local pig breeds in Hubei, China, known for its good reproductive performance, tolerance to roughage, good disease resistance, and tender, flavorful meat. Slaughter testing experiments on Meihuaxing pigs revealed significant differences in the number of rib pairs, exhibiting richer genetic variation in this trait compared to other regional pig breeds. The number of rib pairs is an important economic trait in pigs, significantly impacting the economic benefits of pig farming. The rib pair trait affects body shape and carcass performance; studies show that each additional rib increases carcass length by 80 mm and carcass weight. Furthermore, research indicates a correlation between the genetic mechanisms of teat number and rib number, with some genes related to rib development being strong candidate genes influencing teat number. Therefore, identifying genetic markers associated with the number of ribs in pigs and applying them to molecular breeding is of great significance for improving pig production efficiency and economic benefits.

[0003] Currently, genome-wide association studies (GWAS) have become an important tool for elucidating the genetic structure of complex traits. GWAS mainly involves analyzing the association between genome-wide genetic variations (such as single nucleotide polymorphisms, SNPs) and target traits, combined with systematic data collection, preprocessing, and result interpretation, in order to discover genomic regions or loci significantly associated with target traits. Existing studies have identified genetic loci associated with rib number on multiple chromosomes in different pig breeds. For example, patent CN104232630A discloses a major marker located on chromosome 7 of pigs that increases rib number and its application in the genetic improvement of breeding pigs. This patent, through GWAS analysis, located the major marker affecting rib number within a 947kb range on chromosome 7. In addition, studies have also located chromosome 6 in the Beijing Black pig population. MIB1 The alternative splicing mutation at the 6:107016475 A>G site in the gene was significantly associated with the number of ribs.

[0004] However, traditional GWAS analysis may be affected by linkage disequilibrium (LD) when dealing with complex traits, resulting in the detection of significant signals that are not independent causal variations, but false positive signals that are highly linked to the true causal loci. In order to more accurately identify independent genetic signals, the Conditional & Joint Analysis (COJO) method has been introduced into subsequent GWAS analysis. GCTA-COJO is a commonly used tool that uses stepwise regression to include the most significant SNP as a covariate in the model, thereby identifying independent associated signals and effectively distinguishing between independent signals and false positive signals caused by LD. In addition, GWAS studies on logarithmic traits of pig ribs still have the following shortcomings: (1) Population specificity limitation. Currently identified genetic markers often show population specificity, that is, markers that are significant in specific pig breeds may perform poorly or lack statistical significance in other pig breeds or strains, which greatly restricts their universality in broader breeding practices; (2) Insufficient independent validation. Some studies have failed to fully replicate the significant associated loci in independent validation populations. The lack of such verification may lead to false positive results, thereby weakening the reliability of the discovered markers and their value in practical applications; (3) The potential of high-precision data utilization has not been fully explored. With the rapid development of high-throughput sequencing technology, GWAS analysis after filling chip data can provide high-density and high-precision genotype information.

[0005] In summary, current research has not yet fully utilized these high-precision data, and their potential in uncovering more refined genetic variations and improving the power of association analysis remains to be further explored. Summary of the Invention

[0006] This application aims to address the aforementioned problems in the prior art by combining high-density genotype data, advanced GWAS analysis methods (including GCTA-COJO), and independent validation populations to identify more reliable and universal genetic markers for the trait of rib number in pigs, thereby providing a more effective tool for molecular breeding of pigs.

[0007] To achieve the above objectives, the present invention provides a technical solution. The present invention provides a SNP haplotype molecular marker related to the rib logarithmic trait of the Meihua Star Pig. The SNP molecular marker includes the following sites: (1) The SNP polymorphism at the chr7_91137916 site is G / T, where the number of rib logarithmic pairs of the TT genotype is greater than the number of rib logarithmic pairs of the GT genotype, which is greater than the number of rib logarithmic pairs of the GG genotype; (2) The SNP polymorphism at the chr7_91167100 site is A / G, where the number of rib logarithmic pairs of the AA genotype is greater than the number of rib logarithmic pairs of the AG genotype, which is greater than the number of rib logarithmic pairs of the GG genotype; (3) The SNP polymorphism at the chr7_91517487 site is T / A, where the number of rib logarithmic pairs of the TT genotype is greater than the number of rib logarithmic pairs of the TA genotype, which is greater than the number of rib logarithmic pairs of the AA genotype. The SNP molecular markers were obtained by comparing the porcine genome Sus scrofa 11.1; Among the haplotypes formed by the three significantly related loci, the number of rib pairs in haplotype GAT > the number of rib pairs in haplotype TAT > the number of rib pairs in haplotype GGA > the number of rib pairs in haplotype GAA.

[0008] Furthermore, the molecular markers chr7_91137916, chr7_91167100, and chr7_91517487 correspond to genotypes or haplotypes with more rib pairs, which are the dominant genotypes and can explain 22.11% of the variance in the rib pair phenotype.

[0009] Furthermore, the present invention also provides amplification primers for detecting the logarithmic trait of the ribs of the Plum Blossom Star Pig, and the upstream and downstream amplification primers for detecting the SNP polymorphism at the chr7_91137916 site are shown in SEQ ID NO.1-SEQ ID NO.2; The upstream and downstream amplification primers used to detect the SNP polymorphism at the chr7_91167100 site are shown in SEQ ID NO.3-SEQ ID NO.4; The upstream and downstream amplification primers used to detect the SNP polymorphism at the chr7_91517487 site are shown in SEQ ID NO.5-SEQ ID NO.6.

[0010] Furthermore, using the above primers for multiplex amplification, the total reaction volume was 20 μL, consisting of the following reagents: 2 μL 10× buffer 0.3 μL of HotTaq with a mass concentration of 5 U / μL 2.4 μL of dNTPs with a mass concentration of 2.5 mM 1.2 μL of MgCl2 with a mass concentration of 25 mM 10.1 μL ddH2O 2 μL sample DNA 2 μL of the amplification primers; The reaction procedure includes: (1) denaturation at 95℃ for 2 min, 1 cycle; (2) denaturation at 95℃ for 20 s, annealing to 64℃ for 40 s, extension at 72℃ for 1 min, 11 cycles, with the reaction temperature decreasing by 0.5℃ in each cycle; (3) denaturation at 95℃ for 20 s, annealing to 60℃ for 30 s, extension at 72℃ for 1 min, 24 cycles; (4) extension at 72℃ for 1 min, 1 cycle.

[0011] Furthermore, the validity of the PCR amplification of the samples was confirmed by electrophoresis of the amplification products on a 1.5% agarose gel, including: The multiplex PCR products of each panel of the same sample were quantitatively mixed based on the electrophoresis brightness and the number of fragments in each panel. The mixed multiplex PCR product was diluted 10-20 times and used as a template for the subsequent Index PCR step, i.e., a specific tag sequence was added. Using primers with index sequences, specific tag sequences compatible with the Illumina platform were introduced into the ends of the library via PCR amplification. Prepare the following mixture (20 μL) in a 96-well plate: 4 μL 5×Reaction Buffer, 2.4 μL dNTP (2.5 mM), and 0.8 μL NGMPCRF (10 μM). 2 μL NGMPCRR (4 μM) 0.2 μL Herculase ® IIFusion DNA Polymerase, 2 μL diluted PCR product, 10.6 μL ddH2O, The reaction procedure is as follows: (1) denaturation at 95°C for 2 min, 1 cycle; (2) denaturation at 95°C for 20 s, annealing to 60°C and holding for 30 s, extension at 72°C for 30 s; (3) extension at 72°C for 3 min, 1 cycle.

[0012] Furthermore, the present invention also provides a probe for detecting the logarithmic traits of the ribs of the Plum Blossom Star Pig, used to detect the SNP polymorphisms of chr7_91137916, chr7_91167100, and chr7_91517487.

[0013] Furthermore, the chip used to detect the logarithmic traits of the ribs of the Plum Blossom Star Pig mainly includes the aforementioned probes.

[0014] Furthermore, KASP was used to detect the logarithmic traits of the ribs of the Meihuaxing pig, and the SNP polymorphisms of chr7_91137916, chr7_91167100, and chr7_91517487 were also detected.

[0015] Furthermore, the kit for detecting the rib characteristics of Plum Blossom Star Pig mainly includes the aforementioned probes / chips / KASP.

[0016] This invention also provides a method for detecting the logarithmic traits of ribs in Plum Blossom Star pigs. The method uses a probe / KASP / chip / reagent kit to detect the sample and determines the logarithmic traits of the pig ribs based on the detection results.

[0017] The determination of the number of rib pairs in a sample based on the test results refers to the SNP polymorphism G / T at the chr7_91137916 locus of the sample to be tested, where the number of rib pairs in the TT genotype is greater than that in the GT genotype, which is greater than that in the GG genotype. And / or the SNP polymorphism A / G at the chr7_91167100 site, where the number of rib pairs in the AA genotype > the number of rib pairs in the AG genotype > the number of rib pairs in the GG genotype; And / or the SNP polymorphism T / A at the chr7_91517487 site, where the number of rib pairs in the TT genotype > the number of rib pairs in the TA genotype > the number of rib pairs in the AA genotype; Among the haplotypes formed by the above three significantly related loci, the number of rib pairs in haplotype GAT > the number of rib pairs in haplotype TAT > the number of rib pairs in haplotype GGA > the number of rib pairs in haplotype GAA. The molecular markers chr7_91137916, chr7_91167100, and chr7_91517487 correspond to genotypes or haplotypes with more rib pairs, which are the dominant genotypes and can explain 22.11% of the variance in the rib pair phenotype.

[0018] This invention also provides the application of molecular markers for the porcine rib trait, using the molecular markers chr7_91137916, chr7_91167100, and chr7_91517487 for: (1) Detection of pig body size; (2) Screening and / or identification of pig breeds with rib traits; (3) Adjustment of group output; (4) Screening and / or identification of superior varieties; (5) Screening and / or identification of genetic resources of superior varieties; (6) Identification of parentage.

[0019] Furthermore, the present invention also provides a breeding prediction method for pig rib traits, including using a haplotype rib number prediction model to calculate the detection results of the test sample, and judging the breeding value of the pig rib trait of the test sample based on the calculation results. The haplotype rib count prediction model uses an ordered logistic regression model, with the GGA haplotype as a reference, to construct a rib count prediction formula based on three main haplotypes:

[0020] in , , The expected doses for haplotypes GAA, GAT, and TAT (range 0-2) are given, and Z is the linear prediction value. Preferably, based on the linear prediction value Z, the rib number prediction rule is as follows: Predicted number of ribs = In summary, the present invention achieves beneficial effects by adopting the above technical solution: 1. Improved accuracy and resolution of genetic marker identification: By filling the microarray data, higher density and more accurate genotypic information were obtained, enabling GWAS analysis to more precisely locate genetic loci associated with the number of ribs in pigs, thus improving the accuracy and resolution of candidate locus identification.

[0021] 2. Effective identification of independent causal signals: The introduction of GCTA-COJO for conditional and joint analysis can effectively distinguish independent causal SNP sites from false positive signals caused by linkage disequilibrium, thereby ensuring that the identified significant markers are more biologically significant and have greater application value.

[0022] 3. Enhanced the reliability and universality of markers: By validating in independent validation populations, false positive results were eliminated, the stability of the identified genetic markers under different genetic backgrounds was confirmed, and the reliability and universality of the markers in actual production were significantly improved.

[0023] 4. Provide more effective tools for molecular breeding of pigs: The significant markers identified in this application can be used as molecular selection tools for the rib number trait in pigs, applied to assisted selection breeding of pigs, accelerating the selection process of superior breeding pigs, increasing the rib number of pig herds, and thus improving carcass quality and economic benefits. Attached Figure Description

[0024] Figure 1 This is a diagram of the DNA electrophoresis results of this invention; Figure 2 Manhattan plot for genome-wide association analysis; Figure 3Haplotype analysis of three loci significantly associated with the logarithmic traits of pig ribs; Figure 4 Association analysis between different haplotypes and logarithmic traits of pig ribs (statistical analysis graph); Figure 5 Association analysis of the three SNP loci genotypes and the logarithmic trait of pig ribs in this invention (statistical analysis graph). Detailed Implementation

[0025] Example 1: Obtaining SNP haplotype molecular markers related to logarithmic traits of pig ribs 1. Sample and phenotypic data collection Tissue samples were collected from 417 Sika Star pigs. Rib images of each pig were taken using a Vetoo GJ-2 high-frequency digital X-ray machine (DR), and phenotypic analysis and statistical analysis were performed. DNA extraction was conducted using conventional techniques in the field, which will not be specifically described here. According to the statistical results, in one embodiment of this invention, the number of rib pairs in the 417 Sika Star pigs was between 13 and 16, as shown in Table 1. Table 1. Descriptive statistics of the logarithmic traits of ribs in 417 Meihua Star pigs

[0026] Since genotyping requires certain DNA quality, and low DNA quality can affect the genotyping results, 2% agarose gel electrophoresis is used to detect DNA integrity. The electrophoresis results are as follows: Figure 1 As shown in the image, the DNA sample bands are clear, bright, complete, and without tailing, indicating that the DNA has not been degraded. Detection using an ultra-micro spectrophotometer revealed a minimum DNA concentration of 107.5 ng / μL, a maximum concentration of 1853.9 ng / μL, and an average concentration of 622.86 ng / μL. OD 260 / 280 The minimum value was 1.72, the maximum value was 2.09, and the average value was 1.93. The above test results indicate that the DNA quality of the experimental population is good and meets the requirements for genotyping.

[0027] 2. Genotype Data Acquisition and Processing 2.1 Genotyping 417 DNA samples that passed quality inspection were sent to Wuhan Shadow Gene Technology Co., Ltd. to perform whole-genome SNP genotyping using the pig 80K functional site gene chip to obtain the original SNP chip data for each individual.

[0028] 2.2 Quality Control and Filling Comprehensive quality control is performed on the microarray data obtained from SNP genotyping. The quality control standards for microarray data in this application are as follows (all of the following quality control processes can be completed using PLINK software): 1) Individual genotype detection rate > 90%; 2) Marker genotype detection rate > 90%; 3) Only autosomal SNP markers are retained.

[0029] The quality-controlled genotype data were populated using the 1KCIGP online website (https: / / 1kcigp.com / home). The resulting BCF format files were then used with bcftools software to remove R-values. 2 Loci with a mean allele frequency (MAF) less than 0.9 and a mean allele frequency (MAF) less than 0.05 were identified. After removing duplicate loci, the final number of effective SNP loci in the population was 8,073,180.

[0030] 3. Genome-wide association analysis (GWAS) Genome-wide association analysis (GWAS) was performed on the genotype data and rib number phenotype data obtained in the above steps. In one embodiment of the invention, single-marker regression analysis was used in GCTA software for GWAS analysis, and the mixed linear model is as follows:

[0031] In the above model, y represents the phenotypic value; b represents the SNP effect; and a represents the residual polygenic effect. , Let be the individual additive genetic variance, G be the kinship matrix constructed based on SNPs, X and Z be the association matrices of b and a, respectively; e represents the residual effect vector. , This represents the residual variance.

[0032] Genome-wide association analysis (GWAS) of the logarithmic rib trait in 417 Plum Blossom Star Pigs was performed using GCTA software, and the results were visualized using R software. Figure 2 A total of 9942 SNP loci were found to be significantly associated with the logarithmic trait of ribs in Meihuaxing pigs (P<0.05). After locating the SNP loci significantly associated with the logarithmic trait of ribs in Meihuaxing pigs, it was found that they were all located on chromosome 7.

[0033] 4. Obtaining Molecular Markers For significant association loci obtained from GWAS analysis, conditional and gene-based association analyses were performed using GCTA software. Through COJO conditional and conjoint analyses, based on the summary data from genome-wide association analysis and linkage disequilibrium information from the reference population, a stepwise regression method was used to iteratively incorporate the most significant SNP loci as covariates into the model to identify independent causal SNP loci and exclude false positive signals due to linkage disequilibrium. Simultaneously, based on the association analysis results, the advantages of both mBAT-combo integration and fastBAT tests were utilized to overcome the "masking effect" caused by SNPs canceling each other out due to opposite effects in different gene regions. Ultimately, three loci significantly associated with the rib number trait in Plum Blossom pigs were identified as candidate loci: chr7_91137916, chr7_91167100, and chr7_91517487. These positions were obtained by comparing the porcine genome Sus scrofa 11.1. The above three significantly related loci constitute the GAT haplotype, TAT haplotype, GGA haplotype, and GAA haplotype.

[0034] Among the four haplotypes mentioned above, the number of rib pairs in haplotype GAT > the number of rib pairs in haplotype TAT > the number of rib pairs in haplotype GGA > the number of rib pairs in haplotype GAA.

[0035] Application Example 1: Independent Population Validation Experiment In addition, a separate population of SNP-positive pigs (with a different genetic background or individual origin than the GWAS population) was selected as a validation population. Genotyping of the significant loci analyzed above was performed using multiplex PCR, yielding genotyping results for the corresponding SNP loci in 219 SNP-positive pigs suitable for valid validation. The specific steps are as follows: 1. Sample quality control A 1 μL DNA sample was taken from each pig in the validation population and its quality was tested (concentration and integrity) using 1% agarose gel electrophoresis. The samples were then diluted to a working concentration of 5-10 ng / μL based on the concentration results of the DNA sample from each pig.

[0036] 2. Multiplex PCR reaction of target fragments in samples: Target region amplification of the sample: Multiplex PCR amplification was performed using the optimized multiplex PCR primer panel as follows;

[0037] The reaction system (20 μL) contained 2 μL 10× buffer (TAKARA), 0.3 μL HotTaq 5U / μL (TAKARA), 2.4 μL dNTP (2.5mM), 1.2 μL MgCl2 (25mM), 10.1 μL ddH2O, 2 μL sample DNA, and 2 μL multiplex PCR panel primers. The reaction conditions were: 95°C denaturation for 2 min, 1× cycle; 95°C denaturation for 20 s, 64°C annealing for 40 s, 72°C extension for 1 min, 11× cycle (-0.5°C / cycle); 95°C denaturation for 20 s, 60°C annealing for 30 s, 72°C extension for 1 min, 24× cycle; 72°C extension for 1 min, 1× cycle; 4°C hold, cycle forever. Then, the PCR amplification validity of the sample was confirmed by 1.5% agarose gel electrophoresis; and the multiplex PCR products of each panel of the same sample were quantitatively mixed according to the electrophoresis brightness and the number of fragments in each panel; the mixed multiplex PCR products were diluted 10-20 times and used as templates for the subsequent index PCR step, i.e., specific tag sequences were added.

[0038] The specific tag sequence added to the samples was as follows: using primers with the index sequence, a specific tag sequence compatible with the Illumina platform was introduced into the ends of the library via PCR amplification; the following system (20 μL) was prepared in a 96-well plate: 4 μL 5×Reaction Buffer, 2.4 μL dNTP (2.5 mM), 0.8 μL NGMPCRF (10 μM). 2 μL NGMPCRR (4 μM) 0.2 μL Herculase ®II. Fusion DNA Polymerase, 2 μL diluted PCR product, 10.6 μL ddH2O. The reaction program was: 95°C denaturation for 2 min, 1× cycle; 95°C denaturation for 20 s, 60°C annealing for 30 s, 72°C extension for 30 s, 11 cycles; 72°C extension for 3 min, 1× cycle; 4°C hold, forever. All sample index PCR products were then mixed proportionally according to amplification efficiency. The samples were then mixed and gel extracted, including preparing a 2% agarose gel, taking 50 μL of the index PCR mixture, and electrophoresis at 120V for 35 minutes; according to the commercial DNA Marker B, the brighter band region near the target product size was excised; gel extraction was performed according to the TIANGEN Gel Extraction kit instructions. Finally, library quantification and sequencing were performed, including validating the fragment length distribution of the library using an Agilent 2100 Bioanalyzer; after accurate quantification of the library molar concentration, high-throughput sequencing was finally performed on the Illumina platform in 2×150 bp paired-end sequencing mode to obtain FastQ data.

[0039] 3. Association analysis between haplotype and phenotype Association analysis between genotypes and the number of rib logs at the three SNP loci was performed using the MIXED procedure in SAS software. Different genotypes at the three loci significantly affected the number of rib logs in the Meihuaxing pig. The specific model is as follows:

[0040] in, y i The phenotypic value is μ; the population mean is μ. G i This represents the genotype effect, where i indicates different genotypes; e i This is a random residual effect.

[0041] An additive linear model was used to estimate the joint effect of the three significant loci. The model fitting results showed that the three significantly related loci explained 22.11% of the variance in the rib logarithmic phenotype. Haplotype analysis of the three significant loci revealed that the loci chr7_91137916, chr7_91167100, and chr7_91517487 were in a relatively strong linkage state and could form a haplotype block, with the GGA haplotype having a higher frequency. Significance tests on different haplotypes and phenotypes showed that the number of rib logarithmic phenotypes in the GAT and TAT haplotypes was significantly higher than that in the GGA and GAA haplotypes, as shown in Table 2.

[0042] Table 2. Association analysis between different haplotypes and logarithmic traits of pig ribs (statistical results)

[0043] 4. Association analysis between genotype and phenotype The association analysis between genotypes and rib logarithmic traits at three significant loci in 219 Sika Star pigs was performed using the MIXED procedure in SAS software. The specific models are as follows:

[0044] y i For phenotypic values; μ It is the group mean; G i This represents the genotype effect, where i indicates different genotypes; e i This is a random residual effect.

[0045] The results of the association analysis between genotype and phenotype at significant loci are shown in Table 3.

[0046] Table 3. Association analysis between genotypes of three SNP loci and logarithmic traits of pig ribs (statistical results)

[0047] Table 3 shows that the correlation between the three SNP loci and the number of rib pairs in pigs was extremely significant (P<0.01). At the chr7_91137916 locus, the number of rib pairs was 15.00±0.15 for the TT genotype, 14.65±0.07 for the GT genotype, and 14.24±0.05 for the GG genotype. The TT and GT genotypes had significantly more rib pairs than the GG genotype. At the chr7_91167100 locus, the number of rib pairs was 14.68±0.10 for the AA genotype and 14.5 for the AG genotype. The number of rib pairs was 14.25±0.06 for the GG genotype, and significantly more for the AA and AG genotypes than for the GG genotype. At the chr7_91517487 locus, the number of rib pairs was 15.06±0.13 for the TT genotype, 14.67±0.06 for the TA genotype, and 14.21±0.05 for the AA genotype. The number of rib pairs was significantly more for the TT genotype than for the TA and AA genotypes. Furthermore, these three significant loci showed a high degree of linkage, forming a haplotype block. The number of rib pairs in haplotype GAA was 14.19±0.07, in haplotype GGA it was 14.34±0.04, in haplotype TAT it was 14.77±0.05, and in haplotype GAT it was 15.14±0.14. The number of rib pairs in haplotypes GAT and TAT was significantly higher than that in haplotypes GGA and GAA (Table 2).

[0048] Furthermore, an additive linear model was used to estimate the combined effects of the haplotypes of this invention. The model fitting results showed that the haplotype could explain 22.11% of the logarithmic variance of the rib phenotype. These loci have significant genetic effects in the population, and the number of individuals with the dominant genotypes in the population is relatively small (specifically: the TT genotype at chr7_91137916, the AA genotype at chr7_91167100, the TT genotype at chr7_91517487, and the TAT haplotype is relatively small in the population).

[0049] The above results indicate that there is still significant room for improvement in the rib count trait of breeding pigs. In practical breeding, individuals carrying the dominant genotype associated with multiple ribs should be prioritized to increase the gene frequency of superior alleles in the population, thereby increasing the overall rib count, improving carcass structure, increasing body length, and ultimately enhancing the economic benefits of commercial pig production.

[0050] Example 2: Model Construction and Validation To more accurately predict the number of ribs in individuals, we further constructed a prediction model based on haplotypes at three SNP loci. Through ordered logistic regression analysis of whole-genome data and rib logarithmic phenotypic data from 219 Sika Star pigs, the following prediction formula was established: 1. Haplotype frequency distribution The frequencies of eight possible haplotypes were estimated using the expectation-maximization (EM) algorithm (Table 4). The GGA haplotype had the highest frequency (0.6275), followed by the TAT haplotype (0.2259) and the GAA haplotype (0.1213).

[0051] Table 4 Haplotype Frequency Distribution

[0052] 2. Prediction Model Construction Using an ordinal logistic regression model and referencing the GGA haplotype, a formula for predicting rib count based on three main haplotypes was constructed:

[0053] in , , The values ​​are the expected doses of haplotypes GAA, GAT, and TAT (ranging from 0 to 2), and Z is the linear prediction value.

[0054] 3. Prediction Rules Based on the linear prediction value Z, the rib count prediction rule is as follows: Predicted number of ribs = 4. Model Validation and Performance Evaluation The model performance was evaluated using 5-fold cross-validation. The overall accuracy of the model was 67.1%, the mean absolute error (MAE) was 0.42 roots, and the accuracy within ±1 root was 85.0%. The established prediction formula can provide a molecular marker-assisted selection tool for breeding pigs.

[0055] Of course, those skilled in the art can also design specific detection probes, KASP, detection kits and other tools based on the molecular markers disclosed in this invention, and can also fix the probes on solid-phase chips for use. Since these design methods are all existing technologies, they will not be described in detail for the sake of saving space.

[0056] As can be seen from the embodiments of this invention, the molecular markers obtained by this invention can be widely applied in scenarios such as pig farming, breeding, genetic resource protection, and forensic identification, possessing the dual technical effects of dominant phenotypic prediction and recessive genetic value mining. At the dominant phenotypic prediction level, it can accurately detect pig body size, suitable for early body size potential assessment of piglets in breeding enterprises and prediction of adult pig specifications in slaughtering and processing enterprises. It overcomes the lag and subjectivity of traditional observation and weighing, allowing for the identification of dominant individuals at the piglet stage, reducing ineffective breeding costs, and providing targeted improvements for body size traits. It can also achieve in vivo non-destructive screening and identification of breeds with rib traits, assisting breeding bases and conservation farms in screening individuals with high meat yield potential, providing molecular evidence for breed identification, avoiding breed mixing, and ensuring the purity of purebred resources. Based on these detection results, large-scale breeding bases can stratify the genetic potential of the population, optimize breeding strategies, dynamically adjust population yield, achieve "selection of the best among the best," and improve the overall production performance and economic benefits of the population. In terms of exploring the value of recessive genetic traits, this technology can be applied throughout the entire process of cultivating superior varieties. It helps breeding companies accurately screen parents with superior genotypes, build a molecular marker-assisted breeding system, shorten the breeding cycle, reduce breeding risks, and cultivate high-quality new varieties in a targeted manner. It can also explore the potential value of existing genetic resources, screen rare and superior individuals to establish core groups, monitor genetic diversity, avoid variety degradation, and ensure the sustainable use of superior genetic resources. At the same time, it can accurately identify parentage, ensure accurate breeding pedigrees and reliable variety traceability, provide scientific evidence for genetic disputes, and regulate industry order.

[0057] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-described technical content to create equivalent embodiments without departing from the scope of the present invention. Any simple modifications, equivalent changes, and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the scope of the present invention.

Claims

1. A SNP haplotype molecular marker associated with the logarithmic trait of ribs in Plum Blossom Star pigs, characterized in that, The SNP molecular markers include the following sites: The chr7_91137916 locus has a polymorphism of G / T; the chr7_91167100 locus has a polymorphism of A / G; and the chr7_91517487 locus has a polymorphism of T / A. These three loci constitute the GAT, TAT, GGA, and GAA haplotypes, respectively. Among them, the number of rib pairs in the GAT haplotype is greater than that in the TAT haplotype, which is greater than that in the GGA haplotype, which is greater than that in the GAA haplotype. The SNP molecular markers were obtained by comparing the porcine genome Sus scrofa 11.

1.

2. The molecular marker according to claim 1, characterized in that, The molecular markers chr7_91137916, chr7_91167100, and chr7_91517487 correspond to genotypes or haplotypes with more rib pairs, which are the dominant genotypes and can explain 22.11% of the variance in the rib pair phenotype.

3. Amplification primers for detecting the logarithmic trait of ribs in Plum Blossom Star Pigs, characterized in that, The upstream and downstream amplification primers used to detect the SNP polymorphism at the chr7_91137916 site are shown in SEQ ID NO.1-SEQ ID NO.2; the upstream and downstream primers used to detect the SNP polymorphism at the chr7_91167100 site are shown in SEQ ID NO.3-SEQ ID NO.4; and the upstream and downstream primers used to detect the SNP polymorphism at the chr7_91517487 site are shown in SEQ ID NO.5-SEQ ID NO.

6.

4. A probe for detecting the logarithmic traits of the ribs of the Plum Blossom Star Pig, characterized in that, Used to detect SNP polymorphisms of chr7_91137916, chr7_91167100, and chr7_91517487.

5. A gene chip for detecting the logarithmic trait of ribs in Plum Blossom Star pigs, characterized in that, Includes the probe described in claim 2.

6. A method for detecting KASP markers for the logarithmic traits of ribs in Plum Blossom Star Pigs, characterized in that, Used to detect SNP polymorphisms of chr7_91137916, chr7_91167100, and chr7_91517487.

7. A kit for detecting the logarithmic traits of ribs in Plum Blossom Star Pig, characterized in that, It includes the amplification primers of claim 3, the probe of claim 4, the gene chip of claim 5, or the KASP marker of claim 6.

8. A method for detecting the logarithmic traits of ribs in Plum Blossom Star pigs, characterized in that, The sample to be tested is detected using the amplification primers of claim 3, the probe of claim 4, the gene chip of claim 5, or the KASP marker of claim 6, and the logarithmic trait of the pig ribs of the sample is determined based on the detection results.

9. The amplification primer of claim 3, the probe of claim 4, the gene chip of claim 5, or the KASP marker of claim 6 is used for: (1) Detection of the body size of the Plum Blossom Star Pig; (2) Screening and / or identification of Meihuaxing pig breeds with rib characteristics; (3) Adjustment of the production of the Meihuaxing pig herd; (4) Screening and / or identification of superior Meihua Star Pig breeds; (5) Screening and / or identification of genetic resources of superior Meihua Star pig breed.

10. A breeding prediction method for the logarithmic trait of ribs in Meihua Star pigs, characterized in that, The haplotype rib number prediction model was used to calculate the test results of the test samples, and the breeding value of the pig rib trait of the test samples was determined based on the calculation results. The haplotype rib count prediction model uses an ordered logistic regression model, with the GGA haplotype as a reference, to construct a rib count prediction formula based on three main haplotypes: in , , The expected doses for haplotypes GAA, GAT, and TAT (range 0-2) are given, and Z is the linear prediction value. Preferably, based on the linear prediction value Z, the rib number prediction rule is as follows: Predicted number of ribs = .

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