SNP marker combination for broiler breast intramuscular fat content prediction and assisted selection and application thereof

Abstract

The application discloses a SNP marker combination for broiler breast intramuscular fat content prediction and assisted breeding and application thereof, and belongs to the technical field of assisted breeding.The application aims to provide a method for broiler breast intramuscular fat content prediction and assisted breeding.The application provides 331 SNP markers related to broiler breast intramuscular fat content, which are used for identifying broiler breast intramuscular fat content.

Description

Technical Field

[0001] This invention belongs to the field of assisted breeding technology, specifically relating to an SNP marker combination for predicting intramuscular fat content in the pectoral muscles of broilers and for assisted breeding, and its application. Background Technology

[0002] Intramuscular fat (IMF) content is a crucial indicator of chicken quality, directly impacting tenderness, juiciness, and flavor. Appropriate IMF content significantly enhances the palatability and market value of chicken. However, genetic improvement related to IMF is challenging in broiler breeding. Firstly, fat deposition is a complex quantitative trait, typically regulated by multiple minor genes and environmental factors. Secondly, determining breast muscle IMF content usually relies on slaughter sampling, chemical analysis, or near-infrared spectroscopy, making large-scale, low-cost, and non-destructive testing in the early stages of in vivo observation difficult, thus limiting the efficiency of traditional phenotypic selection. Existing research indicates that the heritability of breast muscle IMF content is only 0.03, suggesting that relying solely on traditional phenotypic selection is insufficient for achieving stable and efficient genetic improvement. Genomic selection (GS) technology uses high-density SNP markers covering the entire genome to estimate individual breeding values, providing a new approach for precise breeding of traits with low heritability. However, in practical applications, only a portion of the millions of SNP markers in the whole genome are truly related to the target trait. The presence of a large number of irrelevant or weakly related markers introduces noise, reducing the accuracy of genomic breeding value (GEBV) estimation.

[0003] In existing technologies, the Genomic Best Linear Unbiased Prediction (GBLUP) model assumes that all SNPs contribute equally to the trait, failing to adequately consider the heterogeneity of different SNP effects. Although Bayesian methods can differentiate SNP effects by setting different prior distributions, their high computational complexity and long processing time limit their application in large-scale breeding populations. Therefore, how to accurately screen key markers closely related to intramuscular fat content in the pectoral muscle from a massive number of SNPs and improve the accuracy of breeding value estimation within the GBLUP model framework is a pressing technical challenge that needs to be addressed. Summary of the Invention

[0004] The purpose of this invention is to provide a method for predicting intramuscular fat content in the pectoral muscles of broilers and for assisting in breeding.

[0005] This invention provides a combination of SNP markers related to intramuscular fat content in the pectoral muscle of broiler chickens, the SNP marker combination being as follows:

[0006] The location and variation information of the loci are represented in the format of chromosome_physical location:reference genotype / variant allele. The physical locations of the 331 selected loci are determined based on the alignment results of the chicken Gallus6a version reference genome (Gallus gallus genome assembly GRCg6a - NCBI - NLM).

[0007] This invention provides a primer combination for amplifying the above-mentioned SNP marker combination.

[0008] This invention provides a kit for identifying the intramuscular fat content of the pectoral muscle of broiler chickens, the kit containing the primer combination described above.

[0009] Furthermore, the kit also includes reagents for amplifying the SNP marker combination as described in claim 1.

[0010] This invention provides the application of the above-mentioned SNP marker combination in the preparation of a kit for identifying the intramuscular fat content of broiler pectoral muscles.

[0011] This invention provides the application of the above-mentioned primer combination in the preparation of a kit for identifying the intramuscular fat content of broiler pectoral muscles.

[0012] This invention provides a method for identifying the intramuscular fat content of the pectoral muscle of broiler chickens. The specific steps of the method are as follows: Step 1: Extract DNA from broiler chickens; Step 2: Perform a PCR reaction using the primer combination described above, and sequence the PCR product to determine the genotype.

[0013] To further specify, the methods for genotyping are sequencing and microarray methods.

[0014] To further specify, the genotyping methods are the KASP genotyping method and the MassARRAY genotyping method.

[0015] To further specify, the genotyping method is the TaqMan probe method.

[0016] Beneficial effects: This invention proposes a key SNP marker combination construction method based on "LD screening + GWAS effect value screening" for the complex quantitative trait of intramuscular fat content in pectoral muscles, which has low heritability and is difficult to accurately measure in early in vivo. This method can effectively screen out a set of key sites that are more closely related to the target trait and have lower redundancy from a large number of whole genome markers.

[0017] This invention identified 331 SNP loci associated with the prediction of intramuscular fat content in broiler pectoral muscles and applied them to the construction of a GBLUP model. Compared with the unoptimized model, the GBLUP model based on these 331 SNP loci improved the prediction accuracy of intramuscular fat content in pectoral muscles from 0.12 to 0.57, indicating that the SNP marker combination provided by this invention can effectively improve the predictive ability of intramuscular fat content in pectoral muscles.

[0018] This invention first performs redundancy removal screening using LD (Learning to De-redundant) and then performs GWAS (Gross-Wirtschaft) effect value screening. This retains key information about the target trait while reducing interference from invalid or weakly correlated markers, which helps improve model stability, reduce computational complexity, and enhance the operability of the model in practical breeding scenarios.

[0019] The SNP marker combinations provided by this invention can not only be used to predict the intramuscular fat content of pectoral muscles, but also for early screening and molecular-assisted breeding of individuals with high intramuscular fat content in pectoral muscles. They have important application value for improving the efficiency of genetic improvement of meat quality-related traits in broilers. Attached Figure Description

[0020] Figure 1 : This is the cascading unbalanced LD decay diagram in Example 1; Figure 2 : This refers to the GWAS analysis results in Example 1. Detailed Implementation

[0021] Example 1. A method for constructing SNP marker combinations for predicting intramuscular fat content in broiler pectoral muscles and assisting in breeding. 1. Establishment of the experimental population and phenotypic determination Two hundred and forty-four broiler chickens were selected, and their pectoral muscle intramuscular fat content phenotypic data were recorded. Blood or tissue samples were collected from each individual for genomic DNA extraction. Pectoral muscle intramuscular fat content was determined using a chemical assay (Soxhlet extraction method), and the fat content in the muscle was calculated on a dry matter basis. The specific operational steps for the determination are as follows: (1) Sample thawing: The muscle tissue samples frozen at -20℃ were taken out and placed in a 4℃ freezer for thawing for 8 hours.

[0022] (2) Sampling: After thawing the muscle tissue, it was pulverized in a 200-type grinder; using a 0.0001 g analytical balance, approximately 2 g of sample (W1) was accurately weighed. The sample was carefully wrapped in filter paper and marked with a pencil. After marking, it was placed in a dry and clean aluminum box and placed on a clean tray for further processing.

[0023] (3) Drying the sample and filter paper package to constant weight: Place the tray and sample from (2) in an electric heating drying oven at 105°C and dry for 6 hours with the aluminum box lid half open (start timing from when the temperature reaches 105°C). After drying, remove the aluminum box and place it in a clean desiccator to cool for 30 minutes, then weigh it (record as W2); put the aluminum box back into the oven to dry for 1 hour, then put it back into the desiccator to cool for 30 minutes and weigh it again (record as W3). If the difference between W2 and W3 is less than 0.0005 g, the sample and filter paper package are considered to have reached constant weight.

[0024] (4) Petroleum ether extraction: First, place the filter paper package after constant weight in (5) into the extraction cup of the Soxhlet extractor, then add an appropriate amount of petroleum ether to completely soak the filter paper package for 12 h, and then heat it at 50°C to reflux the petroleum ether. During the reflux process, the speed should be controlled at 10 times per hour, and the extraction should be continued for 5 to 7 h, that is, 50 to 70 refluxes should be completed to fully extract the target substance.

[0025] (5) Drying to constant weight after extraction: After completing the extraction process, we removed the filter paper package and put it back into the aluminum box. Next, we placed the aluminum box in a fume hood at room temperature and let it stand for 30 minutes to allow the petroleum ether to evaporate completely. After the petroleum ether had completely evaporated, we opened the lid of the aluminum box and placed it in an electric heating drying oven set at 105°C for drying for 7 to 8 hours. After drying, we removed the aluminum box and placed it back into the drying oven to stand for 30 minutes to ensure it was completely cooled. Finally, we weighed it and obtained the weight (W4). Then, we put the aluminum box back into the oven to dry for 1 hour, and then put it back into the drying oven to cool for 30 minutes and weighed it a second time, recording it as W5. If the mass difference between W4 and W5 is less than 0.0005 g, it can be determined that the filter paper package and its contents have reached a constant weight state.

[0026] (6) The calculation method for the results is as follows: To ensure the accuracy and reliability of the results, three parallel samples were selected for each muscle tissue sample for measurement. After the measurement was completed, the average value of the three sample results was taken as the final output. The formula for calculating IMF content is:

[0027] Phenotypic data on intramuscular fat content in the pectoral muscles of 1984 Lindian chickens were obtained. Descriptive statistical analysis was performed on the phenotypic data, and the results are shown in Table 1.

[0028] Table 1. Phenotypic Descriptive Statistics

[0029] Note: A and B indicate that the difference in mean values ​​between different genders in the same column is highly significant.P <0.01) 2. Genotype Data Acquisition This study employed low-depth resequencing genotyping technology to obtain genotypic data from individuals in a reference population. The principle behind this method is to ensure the accuracy of population-based low-depth sequencing genotyping analysis through a large sample size. The process involves first generating ancestral haplotypes using large-scale population sequencing, and then using the inferred ancestral haplotype information to fill in the true genotype at each locus for each individual. Genotyping accuracy is significantly correlated with sample size and to some extent with population heterozygosity. When the average sequencing depth of the population sample exceeds 0.5×, further increases in sequencing depth have limited impact on accuracy. This method has been reported in multiple articles and exhibits very high genotyping accuracy.

[0030] Two days prior to slaughter, wing vein blood samples were collected from all selected chickens. Whole blood DNA was extracted after EDTA anticoagulation and whole-genome resequencing was performed at a depth of 0.74×. The sequencing data were compared with a reference genome to obtain raw SNP data for each individual. Subsequently, software such as VCFtools, PLINK, and Beagle were used for quality control (QC) and missing value imputation of the SNP data. The processed data were then used for subsequent genetic association analysis and genome breeding value estimation studies.

[0031] The quality control and imputation process for genotype data is as follows: (1) Remove individuals with no phenotypic information; (2) Retain SNP sites with a minimum allele frequency (MAF) greater than 1%; (3) Only SNP sites with two allelic variations (ALT) are retained; (4) The fractional score INFO_SCORE > 0.4 (5) The missing values ​​in the genotype data were filled using Beagle software without using an external reference population. The filling process was based on the genotype information of the candidate population itself and was completed using default parameter settings.

[0032] 3. Group segmentation The 2044 individuals were randomly divided into a training group and a validation group at an 8:2 ratio, with 1635 individuals in the training group and 409 individuals in the validation group. The training group was used for SNP labeling and model building, while the validation group was used for model accuracy verification.

[0033] 4. Chain disequilibrium screening In this study, LD (Lower-Range Spectrum) was used to screen SNP subsets, with r² as the threshold. Different r² thresholds (e.g., 0.1, 0.2, 0.3) were set during LD subset construction for LD block partitioning, and representative SNPs were selected from these. By running the model under multiple threshold schemes and comparing their breeding value estimation accuracy, the optimal screening strategy and parameter thresholds were finally determined. Based on the breeding value estimation accuracy of the corresponding SNP subsets in different models, and combined with LD decay analysis, the optimal window and step size for the population were found, and the threshold conditions were further optimized to obtain the optimal SNP subset. In this embodiment, by comparing the breeding value estimation accuracy under different thresholds, SNP sites satisfying LD r² < 0.3 were ultimately retained, resulting in the first candidate SNP set. The results are shown below. Figure 1 And Table 2: Table 2

[0034] 5. GWAS effect size screening Using the intramuscular fat content phenotype of the pectoral muscles in the training population as the target trait, genome-wide association analysis was performed on the first candidate SNP set using GEMMA software and a mixture linear model. The specific model is as follows:

[0035] In the model, y represents the target trait; X is the covariate matrix, including sex and pre-slaughter live weight; β is the fixed-effects parameter of the covariates; Z is the genotype kinship matrix; α is the random-effects parameter of genotype, taking into account the kinship of individuals; ε indicates that the model follows an N(0, 1) / N( ... I σ 2 e The random residuals of the distribution.

[0036] The effect size of each SNP locus on the intramuscular fat content of the pectoral muscle was calculated. Using the absolute value of the effect size as the screening criterion, SNP loci that met the condition of an absolute effect size ≥ 0.3 were retained to obtain the second candidate SNP set.

[0037] In this embodiment, under the conditions of LD r² < 0.3 and absolute effect value ≥ 0.3, a total of 331 SNP loci were screened and used as SNP marker combinations for predicting intramuscular fat content in broiler pectoral muscles and assisting in breeding. The numbers, chromosomal locations, reference alleles, and mutant alleles of the 331 SNP loci are listed in Table 3 and Figure 2 .

[0038] 6. Constructing a GBLUP prediction model based on SNP marker combinations A genome relationship matrix was constructed using the genotype data of the aforementioned 331 SNP loci, and a GBLUP prediction model was further established, as detailed below:

[0039] Where y is the phenotypic value vector, Xβ is the fixed effect (sex, strain); Z is an n × m genotype matrix or pedigree matrix, connecting individuals to their random effects; μ is the random effect following the rules of genotyping. e is the residual follows ...

[0040] The intramuscular fat content of the pectoral muscle in broiler chickens was predicted. The genotypes of 331 SNP loci from each individual in the validation population were input into the established GBLUP model to obtain estimated breeding values ​​for the intramuscular fat content of the pectoral muscle in the corresponding individuals. The accuracy of the model was evaluated by the correlation between the estimated breeding values ​​and the actual phenotypic values.

[0041] The results show that the SNP marker combination provided by this invention can effectively improve the predictive ability of intramuscular fat content in broiler pectoral muscles. Without SNP screening and directly using the basic GBLUP model, the prediction accuracy of intramuscular fat content in pectoral muscles was 0.12; after constructing the GBLUP model using the 331 SNP sites selected by this invention, the prediction accuracy improved to 0.24.

[0042] Example 2. Based on the obtained 331 SNP sites, probes for detecting the 331 SNP marker sites were designed to obtain probe combinations. (Table 3-14) Table 3

[0043] Table 4

[0044] Table 5

[0045] Table 6

[0046] Table 7

[0047] Table 8

[0048] Table 9

[0049] Table 10

[0050] Table 11

[0051] Table 12

[0052] Table 13

[0053] Table 14

[0054] `rsid` represents the locus's identifier in the dbSNP database; `final_id` is the final locus identifier used for subsequent analysis and display. If the locus matches an `rsid`, `final_id` retains the `rsid`; if `rsid` is NA, it means the locus did not match a publicly available `rsid` identifier, so it uses the format "chromosome_location" as a unique identifier, such as 1_163862. The choice between A1 and A2 is linked to high pectoral muscle intramuscular fat content, which depends on the Beta value. If beta > 0, then A1 is associated with higher pectoral muscle intramuscular fat content; if beta < 0, the opposite is true.

[0055] From the chicken Gallus6a version reference genome ( Gallus gallus genome assembly GRCg6a - NCBI - NLM (https: / / www.ncbi.nlm.nih.gov / datasets / genome / GCF_000002315.6 / )

[0056] Example 3. The method for functional gene detection using the probe combination prepared in Example 2 includes the following steps: Step 1: Preparation of the sample library; Blood samples were collected from individual broiler chickens, and genomic DNA was extracted using a standard genomic DNA extraction kit. DNA concentration and purity were measured using a nucleic acid analyzer, with a preferred concentration of at least 20 ng / μL and an OD260 / 280 of 1.8–2.0. Agarose gel electrophoresis was used to assess DNA integrity, ensuring no significant degradation of the sample. The qualified genomic DNA samples were randomly fragmented, preferably into 200–300 bp fragments. Subsequently, end repair, 3' end A addition, adapter ligation, and PCR pre-amplification were performed sequentially to obtain the DNA library to be captured. The library was purified and quantitatively analyzed for later use.

[0057] Step 2, probe hybridization; Step 2.1, Probe preparation; Oligonucleotide capture probes were designed based on the chromosomal locations of the 331 target SNP sites. The probes preferably cover the target SNP sites and their upstream and downstream flanking sequences, with each probe typically ranging from 80 to 120 nt in length. The probes may be biotin-tagged for subsequent binding with magnetic beads for capture.

[0058] Step 2.2: DNA library hybridization capture; After denaturing the DNA library obtained in step 1, it is mixed with the probe combination described in step 2.1 in a hybridization buffer system. Preferably, denaturation is performed at 95°C for 5–10 min, followed by hybridization at 60–65°C for 12–24 h, so that the probe specifically binds to the DNA fragment containing the target SNP site.

[0059] Step 2.3: The hybridization fragment is combined with the hybridization magnetic beads; Add streptavidin magnetic beads to the hybridization reaction system to allow the biotin-labeled probe-target DNA complex to bind to the magnetic beads. Incubate at room temperature for 20–30 min, and gently vortex to mix.

[0060] Step 2.4: Elute to remove unbound DNA; The magnetic beads were washed multiple times with washing buffer to remove unbound or non-specifically bound DNA fragments. Ideally, 3–5 washes were performed. The target DNA fragments bound to the magnetic beads were then eluted to obtain the enriched target library.

[0061] Step 3: PCR enrichment; The enriched library obtained in step 2.4 was used as a template for PCR amplification to increase the abundance of the target fragment. After PCR amplification, the product was purified, and the concentration and fragment distribution of the purified library were detected.

[0062] Step 5: Sequencing; The amplification products obtained in step 4 are then sequenced. A high-throughput sequencing platform is preferably used for paired-end sequencing to obtain sequencing data covering the target 332 SNP sites.

[0063] Step 6: Compare.

[0064] After quality control of the raw sequencing data to remove adapter contamination and low-quality sequences, the valid data were aligned to the chicken reference genome. Based on the alignment results, base information of 331 target SNP sites was extracted to obtain the genotype results of each individual at each target site.

[0065] Example 4. Application of the SNP marker combination in assisted breeding of intramuscular fat content in broiler pectoral muscles Select a group of broiler chickens to be bred, and collect blood samples, feather marrow samples or tissue samples from each individual to extract genomic DNA.

[0066] Genotyping was performed on 331 SNP loci of the individuals to be selected, obtaining genotypic information for each locus. The genotyping method can be sequencing, microarray, KASP, MassARRAY, TaqMan probe method, or other suitable SNP detection methods. The genotypic results of each individual at the 331 SNP loci were encoded according to a unified rule to construct an individual × locus genotypic matrix. This genotypic matrix was then input into the GBLUP model established in Example 1 to obtain the predicted value of intramuscular fat content in the pectoral muscle or the genomic breeding value for each individual. Candidate individuals were ranked according to the predicted value or the genomic breeding value, with priority given to individuals with higher predicted values ​​as breeding stock, mating candidates, or core breeding individuals, thereby achieving early prediction and assisted breeding of intramuscular fat content in the pectoral muscle of broilers. In practical applications, the selection ratio can be set according to the breeding goals, with individuals ranking higher in predicted values ​​being preferred for the next round of breeding.

Claims

1. A combination of SNP markers related to intramuscular fat content in the pectoral muscle of broiler chickens, characterized in that, The SNP tag combinations are as follows: The location and variation information of the loci are represented in the format of chromosome_physical location:reference genotype / variant allele. The physical locations of the 331 selected loci are determined based on the alignment results of the chicken Gallus6a version reference genome.

2. Primer combinations for amplifying the SNP marker combination as described in claim 1.

3. A reagent kit for identifying the intramuscular fat content of broiler pectoral muscles, characterized in that, The kit contains the primer combination as described in claim 2.

4. The reagent kit according to claim 3, characterized in that, The kit also includes reagents for amplifying the SNP marker combination as described in claim 1.

5. The application of the SNP marker combination according to claim 1 in the preparation of a kit for identifying the intramuscular fat content of broiler pectoral muscles.

6. The application of the primer combination according to claim 2 in the preparation of a kit for identifying the intramuscular fat content of broiler pectoral muscles.

7. A method for identifying the intramuscular fat content of broiler pectoral muscles, characterized in that, The specific steps of the method are as follows: Step 1: Extract DNA from broiler chickens; Step 2: Perform a PCR reaction using the primer combination described in claim 2, and sequence the PCR product to determine the genotype.

8. The method according to claim 7, characterized in that, Genotyping methods include sequencing and microarray methods.

9. The method according to claim 7, characterized in that, Genotyping methods include KASP genotyping and MassARRAY genotyping.

10. The method according to claim 7, characterized in that, The genotyping method used was the TaqMan probe method.

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