SNP molecular marker combination for identifying the relationship of sunite mongolian cattle and application thereof

Abstract

This invention belongs to the field of animal molecular genetics and breeding technology, specifically relating to SNP molecular marker combinations for identifying kinship in Sunite Mongolian cattle and their applications. The marker combination consists of 141 highly polymorphic SNP loci distributed on bovine autosomes 1-29, with an average physical distance of 19.93 Mb between loci, exhibiting good genome coverage and locus independence. The selected SNP loci show high minimum allele frequency (MAF), expected heterozygosity (He), and polymorphism information content (PIC) in the Sunite Mongolian cattle population, with average values ​​of 0.4843, 0.4990, and 0.3745, respectively, and a cumulative exclusion probability of no less than 0.9999 under unknown maternal genotype conditions. The SNP molecular marker combination and its application method of this invention can provide a reliable molecular tool for the protection of Sunite Mongolian cattle germplasm resources, the construction of conservation populations, the selection of breeding bulls and cows, and the development of the Sunite Mongolian beef cattle industry, offering advantages such as ease of operation, moderate cost, and high accuracy.

Description

Technical Field

[0001] This invention belongs to the field of animal molecular genetics and breeding technology, specifically relating to SNP molecular marker combinations for identifying kinship in Sunite Mongolian cattle and their applications. Background Technology

[0002] Sunite Mongolian cattle are an important local breed of Mongolian cattle, mainly distributed in Sunite Left Banner, northern Xilin Gol League, Inner Mongolia. They are a representative cattle resource in the local traditional grassland animal husbandry. Their core production area is concentrated in sumu towns such as Honggeer, Saihangobi, Dalai, Bayanwula, and Chagan Aobao, covering a continuous area of ​​approximately 17,000 km². 2 It accounts for about 50% of the total land area of ​​the banner. Due to long-term selection in the arid, cold, and wind-blown desert grassland ecological environment, Sunite Mongolian cattle have developed characteristics such as cold and drought resistance, tolerance to roughage, strong disease resistance, and adaptability to long-distance grazing, making them a valuable local beef cattle genetic resource in my country. Currently, there are approximately 18,000 Sunite Mongolian cattle, of which about 12,000 are breeding cows, occupying an important position in the overall Mongolian cattle resource.

[0003] Historically, with the introduction and promotion of improved beef cattle breeds such as Simmental and crossbreeding, the number of purebred Mongolian cattle in my country declined significantly. In contrast, Sunite Mongolian cattle have generally retained a high degree of local bloodline purity and typical traits, which is of great significance for maintaining the genetic diversity of Mongolian cattle and conducting research on adaptive genetics. However, from a germplasm perspective, the Sunite Mongolian cattle population is limited in size and the frequency of inter-herd interaction is low. Under the production mode that is mainly based on free grazing and free mating, the risk of inbreeding is gradually accumulating. If systematic conservation and rational utilization measures are lacking, there is a risk of further reduction in their effective genetic variation.

[0004] In the traditional production system of Sunite Mongolian cattle, herders often graze bulls and cows of different sizes in mixed herds. During breeding, free mating, repeated mating, and ineffective mating frequently occur. Furthermore, the dispersed grazing areas and insufficient allocation of breeding bulls limit familial exchange between small groups, leading to an increased probability of inbreeding over generations. Phenotypically, this may manifest as decreased reproductive performance, increased differences between calf birth weight and body condition, and greater body size dispersion, consistent with the risks of inbreeding and decreased genetic diversity.

[0005] Numerous studies have shown that pedigree information in livestock and poultry populations contains varying degrees of errors and omissions. The average error rate in cattle pedigrees can reach around 10%, and is even higher in some groups. Pedigree errors reduce the accuracy of breeding value estimation, weaken selection and mating efficiency, and thus affect the speed of genetic progress. Correcting pedigrees using DNA molecular markers and identifying kinship between offspring and candidate parents have become important technical means in modern livestock and poultry breeding systems. Compared with traditional microsatellite markers, single nucleotide polymorphism (SNP) markers have significant advantages in terms of high genome-wide distribution density, good genetic stability, and mature automated detection and data analysis processes, making them the mainstream tool for current kinship identification and genome selection.

[0006] Existing SNP panels for kinship identification, constructed for other cattle breeds, are often optimized based on the breed's own genetic structure, allele frequency distribution, and linkage disequilibrium (LD) patterns. Directly applying these panels to the Sunite Mongolian cattle population may result in decreased kinship identification ability or shifts in the determination threshold. Therefore, it is necessary to establish a set of SNP molecular marker combinations with high polymorphism, balanced genome coverage, and low linkage disequilibrium among the Sunite Mongolian cattle population, and construct a matching kinship determination system to achieve accurate kinship identification, pedigree information verification, and rapid parentage identification in the Sunite Mongolian cattle population. Summary of the Invention

[0007] The purpose of this invention is to provide a combination of SNP molecular markers for identifying kinship in Sunite Mongolian cattle.

[0008] This invention analyzes whole-genome resequencing data from 285 Sunite Mongolian cattle to identify 141 SNP loci on 29 autosomes, which can be used for kinship identification in the Sunite Mongolian cattle population. These SNPs contain 141 markers and their base mutations and location information. These SNP marker combinations are distributed on 29 autosomes, and the average distance between adjacent SNPs on the same chromosome is 19.93 Mb.

[0009] The SNP molecular marker combination provided by this invention for identifying kinship in Sunite Mongolian cattle includes the following 141 SNP molecular markers:

[0010]

[0011]

[0012]

[0013] Furthermore, this invention provides additional information on the aforementioned 141 SNP molecular markers (based on gene annotation and sequence alignment using the ARS-UCD 2.0 reference genome). The average minimum allele frequency (MAF), expected heterozygosity (He), and polymorphism information content (PIC) of this marker combination are 0.4843, 0.4990, and 0.3745, respectively; the cumulative exclusion probability exceeds 99.99%. Details are as follows:

[0014]

[0015]

[0016]

[0017] This invention provides the application of the aforementioned 141 molecular marker combinations in identifying kinship in Sunite Mongolian cattle. In this invention, by simultaneously extracting high-quality genomic DNA and performing parallel genotyping on 141 SNP loci, hundreds of samples can be processed in a single run. This solves the problems of existing methods being complex, time-consuming (several days), costly, and unable to meet the needs of rapid screening of large-scale populations. Kinship identification for a single sample is time-saving and cost-effective, and is stable and feasible under basic laboratory conditions, providing the necessary high-quality input data for subsequent Kinship and PI_HAT calculations.

[0018] This invention provides the application of the aforementioned 141 molecular marker combinations in the genetic improvement breeding of Sunite Mongolian cattle. By utilizing the SNP molecular marker combinations of this invention, a genome-wide relationship matrix (GRM) is constructed to replace the traditional pedigree matrix, solving the problems of traditional phenotypic selection being susceptible to environmental interference and unable to avoid homozygous recessive harmful alleles. This provides support for the scientific formulation of mating programs to delay inbreeding depression, maximize the preservation of effective population size and genetic diversity in breed conservation, and help improve problems such as declining reproductive performance, increased differences in calf birth weight and body condition, and increased body size dispersion in Sunite Mongolian cattle.

[0019] This invention provides a method for identifying the kinship of Sunite Mongolian cattle, comprising the following steps: Genomic DNA was extracted from the cattle to be tested, and genotyping was performed on a marker combination containing 141 highly polymorphic SNP loci using high-throughput sequencing technology. The SNP marker combination was distributed across 29 autosomes, with an average distance of 19.93M between adjacent SNPs on the same chromosome. Linkage-disequilibrium pruning (LD pruning, parameter: --indep-pairwise 50 10 0.1) was performed to ensure locus independence. The genotypic data of 141 SNPs of the individuals to be tested are input into King or Plink software to calculate the kinship coefficient and IBD sharing value (PI_HAT) between individuals, and the kinship between individuals is inferred based on the results.

[0020] Kinship represents the probability that two individuals share alleles due to a common ancestor, reflecting the closeness of their kinship. It is calculated using King software based on the allele sharing pattern of 141 SNP loci. The core logic is: Kinship = 1 / 2 × (expected proportion of shared alleles).

[0021] Specifically, the allele matching status (homozygous matching, heterozygous crossover matching, etc.) of each SNP locus is weighted and summed to obtain the Kinship value between individuals.

[0022] According to internationally accepted standards and the verification results of this invention, the kinship level corresponding to the Kinship value is: 0.354: Identical twins (completely share genes); (0.177, 0.354): Within one generation, kinship (e.g., parent-child, siblings); (0.0884, 0.177): Family relationships within two generations (e.g., grandparents and grandchildren, half-siblings); (0.0442, 0.0884): Kinship within three generations (e.g., great-grandparents and grandchildren); ≤0.0442: No significant kinship.

[0023] IBS (Status Homologous) refers to two individuals sharing the same allele sequence; IBD (Disease Homologous) refers to two individuals sharing alleles originating from a common ancestor.

[0024] IBS analysis allows us to calculate the specific IBS values ​​between two samples, but it doesn't reveal the specific kinship relationship. It only shows an increasing relationship, meaning the larger the IBS value, the closer the kinship. A matrix was constructed based on the IBS values.

[0025] The Plink software uses the PI_HAT value (parameter: --genome) to estimate the IBD value. This method is based on a Hidden Markov Model and uses moment estimation to calculate the probability that IBD = 1, 2, or 0.

[0026] PI_HAT: is the IBD ratio, i.e., P(IBD=2) + 0.5*P(IBD=1).

[0027] The kinship corresponding to the PI_HAT value is: within one generation ≈ 0.375; within two generations ≈ 0.1875; within three generations ≈ 0.09375.

[0028] Based on the joint analysis of Kinship and PI_HAT values, the criteria for determining the kinship of Sunite Mongolian cattle in this invention are as follows: If the Kinship value of the tested individuals is between (0.177, 0.354) and PI_HAT ≈ 0.375, then they are considered to be related within one generation. If the Kinship value is between (0.0884, 0.177) and PI_HAT≈0.1875, then the relationship is determined to be within two generations (e.g., grandparent-grandchild). If the Kinship value is between (0.0442, 0.0884) and PI_HAT≈0.09375, then the relationship is determined to be within three generations. If the Kinship value is ≤0.0442 and PI_HAT<0.05, then it is determined that there is no significant kinship. When multiple candidate parents meet the same kinship level, the individual whose Kinship and PI_HAT values ​​are closest to the theoretical values ​​is selected as the most similar parent.

[0029] Validation on two known families (Family_1 and Family_2) showed that the Kinship and PI_HAT values ​​calculated using the 141 SNP marker combinations of this invention were 100% consistent with the kinship inferred from whole-genome SNP data. In the top 100% (141 SNPs) group, the family misclassification rate was the lowest (0.0567 for all groups), verifying the stability and high accuracy of the method of this invention in the Sunite Mongolian cattle population.

[0030] The beneficial effects of this invention: The SNP molecular marker combination for identifying kinship in Sunite Mongolian cattle provided by this invention contains 141 SNP marker combinations distributed across 29 autosomes, with an average distance of 19.93 Mb between adjacent SNP markers on the same chromosome. The average minimum allele frequency (MAF), expected heterozygosity (He), and polymorphism information content (PIC) of the marker combinations of this invention are 0.4843, 0.4990, and 0.3745, respectively; the cumulative exclusion probability exceeds 0.9999 when the maternal genotype is unknown.

[0031] This invention employs high-throughput SNP genotyping technology to obtain genotypic information of individual Sunite Mongolian cattle at the aforementioned 141 SNP loci. King software is used to calculate the kinship coefficient between individuals, and Plink software is combined to calculate the IBD shared value (PI_HAT). This constructs a threshold for determining kinship within the Sunite Mongolian cattle population, enabling accurate identification of kinship within one, two, and three generations. Validation results show that the SNP marker combinations constructed in this invention have kinship identification capabilities in the Sunite Mongolian cattle population comparable to or higher than those of whole-genome SNP data, making them suitable for pedigree correction and parental screening in large-scale populations.

[0032] The SNP molecular marker combination of the present invention can be effectively applied to pedigree correction, germplasm resource management and genetic improvement breeding of Sunite Mongolian cattle. It has the characteristics of simple operation, low cost and high accuracy, and has good application prospects and economic benefits. Detailed Implementation

[0033] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be described in detail below. Obviously, the described embodiments are merely some embodiments of this invention, and not all embodiments. Based on the embodiments of this invention, all other implementation methods obtained by those skilled in the art without creative effort are within the scope of protection of this invention.

[0034] All chemical reagents used in the examples are commercially available.

[0035] The 285 Sunite Mongolian cattle sample used in this invention came from farmers in Sunite Left Banner, Inner Mongolia, including 45 breeding bulls, 120 cows, and 120 offspring.

[0036] Example 1: Screening of 141 SNP molecular markers for kinship identification in Sunite Mongolian cattle Blood or tissue samples were collected from 285 individuals of Sunite Mongolian cattle from the sumu towns of Honggeer, Saihangobi, Dalai, Bayanwula, and Chagan Aobao in Sunite Left Banner. These included 45 breeding bulls, 120 breeding cows, and 120 of their offspring. Ear tags, sex, age, origin of the herder, and preliminary pedigree information were recorded during sample collection.

[0037] Genotyping of the above samples was performed using the Illumina NovaSeq high-throughput sequencing platform or a high-density SNP array to obtain the whole genome SNP dataset of Sunite Mongolian cattle. Quality control of the sequencing data was performed, including removal of adapter contamination sequences and filtering of low-quality reads, using FastP software. Subsequently, the filtered reads were aligned to the bovine reference genome ARS-UCD2.0 using bwa, PCR repeats were removed using Picard, and SNP detection was performed using GATK to obtain the original variant site information.

[0038] Considering the pedigree error rate, the following steps are used to screen and validate SNPs in order to obtain SNPs with identification capabilities: 1. Filter SNPs; 2. SNP polymorphism is divided into 10 levels (PIC value); 3. Calculate the misclassification rate for the two families (Family_1 and Family_2); 4. Based on the SNP datasets corresponding to different levels, kinship and IBS analyses were performed, and the kinship relationships obtained from whole-genome SNP identification were compared and verified.

[0039] SNPs are filtered based on the following criteria: 1) Preserve sites with MAF > 0.4 (Software: vcftools; Parameter: -maf 0.4); 2) Retain loci with a 100% success rate in individual genotyping (Software: vcftools; Parameter: -max-missing 1); 3) HWE test (software: vcftools; parameter: --hwe 0.0001); 4) Preserve biallelic loci (Software: vcftools; Parameters: --min-alleles 2 --max-alleles 2); 5) Retain loci with heterozygosity above the mean (mean=0.3853) (Software: plink; Parameters: --allow-extra-chr --chr-set 29 --hardy); 6) Retain sites with a PIC value higher than the mean (mean=0.3716); 7) Perform LD pruning (software: plink; parameters: --indep-pairwise 50 10 0.1, representing pruning method: window size of 50 SNPs, window step size of 10 SNPs, and linkage imbalance coefficient r).2 (Threshold is 0.1) 8) Only select loci on autosomes; SNP density filtering involves dividing the filtered SNP dataset into 20M windows and taking the first site within each window.

[0040] Based on all the above filtering conditions, a SNP dataset containing 141 sites was finally obtained.

[0041] SNP Dataset Construction and Classification: A high-quality SNP dataset of 141 loci was obtained after screening using the above process. The loci on each chromosome were further classified according to SNP polymorphism into ten subsets with retention rates of 100%, 90%, 80%, 70%, 60%, 50%, 40%, 30%, 20%, and 10%. The results are shown in Table 1. Table 1. Number of SNP molecular markers at each level

[0042] Error rate and kinship analysis: Using the following two known parent-offspring triplets (Table 2), a comparative analysis of the misclassification rate and kinship discrimination ability of the above-mentioned datasets at different levels was conducted.

[0043] Table 2. Samples of known parent-offspring triplets obtained through paternity testing.

[0044] The differences in misclassification rates between the two families under different classifications are as follows: Table 3. Misclassification rates of two families under different classification levels

[0045] Meanwhile, based on all SNP molecular markers before filtering, the misclassification rates of the two families were calculated, and the results are shown in Table 4: Table 4. Misclassification rates of all SNPs in the two families.

[0046] To achieve the most accurate identification efficiency, this study ultimately selected 141 SNP molecular markers as the SNP molecular marker combination for identifying kinship in Sunite Mongolian cattle (see Table 3). The 141 SNP molecular markers are distributed across 29 autosomes, with an average of 5 SNP molecular markers per chromosome. The average distance between adjacent SNPs on the same chromosome is 19.93M, avoiding linkage disequilibrium between SNPs.

[0047] The SNP molecular marker combinations of this invention exhibit high polymorphism: the minimum allele frequency (MAF) ranges from 0.47 to 0.5, with an average of 0.4843; the expected heterozygosity (He) ranges from 0.49 to 0.5, with an average of 0.4990; the average polymorphism information content (PIC) is 0.3745; and the cumulative exclusion probability exceeds 99.99%.

[0048] Table 5. Combinations of 141 SNP markers SNP Name chromosome Location Base mutation Minimum allele frequency Expected heterozygosity Polymorphic information content Excluding probability Chr1:169469 chr1 169469 T / C 0.4811 0.4993 0.3746 0.2014 Chr1:20461348 chr1 20461348 G / A 0.4623 0.4972 0.3736 0.4062 Chr1:40046199 chr1 40046199 G / C 0.4906 0.4998 0.3749 0.1951 Chr1:61565419 chr1 61565419 C / T 0.4623 0.4972 0.3736 0.325 Chr1:80039640 chr1 80039640 A / G 0.5 0.5 0.375 0.4354 Chr1:101025426 chr1 101025426 A / G 0.4811 0.4993 0.3746 0.4007 Chr1:120157263 chr1 120157263 C / A 0.4434 0.4936 0.3718 0.375 Chr1:140006014 chr1 140006014 C / T 0.4906 0.4998 0.3749 0.3028 Chr2:584323 chr2 584323 A / G 0.5 0.5 0.375 0.3396 Chr2:20531199 chr2 20531199 C / T 0.4906 0.4998 0.3749 0.3236 Chr2:40353284 chr2 40353284 A / G 0.4906 0.4998 0.3749 0.4017 Chr2:60571511 chr2 60571511 C / T 0.4717 0.4984 0.3742 0.3156 Chr2:80135909 chr2 80135909 G / A 0.4906 0.4998 0.3749 0.3146 Chr2:100127162 chr2 100127162 C / A 0.4906 0.4998 0.3749 0.3139 Chr2:120115615 chr2 120115615 A / G 0.4906 0.4998 0.3749 0.3917 Chr3:559182 chr3 559182 A / G 0.4906 0.4998 0.3749 0.3309 Chr3:20474447 chr3 20474447 C / T 0.5 0.5 0.375 0.4333 Chr3:40419611 chr3 40419611 G / A 0.4811 0.4993 0.3746 0.4344 Chr3:60063632 chr3 60063632 C / T 0.5 0.5 0.375 0.4333 Chr3:80277343 chr3 80277343 G / A 0.4811 0.4993 0.3746 0.1753 Chr3:100078143 chr3 100078143 G / A 0.4717 0.4984 0.3742 0.3951 Chr3:120664384 chr3 120664384 C / T 0.4811 0.4993 0.3746 0.275 Chr4:215640 chr4 215640 G / C 0.4717 0.4984 0.3742 0.1 Chr4:20049251 chr4 20049251 C / T 0.4717 0.4984 0.3742 0.3705 Chr4:40539179 chr4 40539179 C / T 0.5 0.5 0.375 0.3806 Chr4:60092795 chr4 60092795 G / A 0.4811 0.4993 0.3746 0.1729 Chr4:81045890 chr4 81045890 C / T 0.5 0.5 0.375 0.3031 Chr4:100060437 chr4 100060437 C / T 0.5 0.5 0.375 0.3674 Chr5:103745 chr5 103745 A / G 0.4811 0.4993 0.3746 0.3917 Chr5:21243204 chr5 21243204 A / G 0.5 0.5 0.375 0.3389 Chr5:40201549 chr5 40201549 T / A 0.5 0.5 0.375 0.3788 Chr5:60199933 chr5 60199933 G / A 0.4811 0.4993 0.3746 0.375 Chr5:80042873 chr5 80042873 C / T 0.4623 0.4972 0.3736 0.3528 Chr5:101065637 chr5 101065637 T / C 0.5 0.5 0.375 0.2816 Chr6:577748 chr6 577748 C / T 0.5 0.5 0.375 0.276 Chr6:21225352 chr6 21225352 T / C 0.4717 0.4984 0.3742 0.1472 Chr6:40346112 chr6 40346112 A / G 0.4717 0.4984 0.3742 0.3389 Chr6:60037073 chr6 60037073 C / T 0.4623 0.4972 0.3736 0.3757 Chr6:80745782 chr6 80745782 T / C 0.4623 0.4972 0.3736 0.3 Chr6:100510572 chr6 100510572 G / A 0.5 0.5 0.375 0.2177 Chr7:957025 chr7 957025 C / A 0.4528 0.4956 0.3728 0.341 Chr7:20675954 chr7 20675954 G / A 0.4906 0.4998 0.3749 0.4625 Chr7:40159584 chr7 40159584 A / T 0.4906 0.4998 0.3749 0.3104 Chr7:60200262 chr7 60200262 C / T 0.5 0.5 0.375 0.3882 Chr7:80094212 chr7 80094212 C / T 0.5 0.5 0.375 0.45 Chr7:100079208 chr7 100079208 T / A 0.4717 0.4984 0.3742 0.2566 Chr8:235274 chr8 235274 A / G 0.4717 0.4984 0.3742 0.2517 Chr8:20439700 chr8 20439700 C / T 0.4811 0.4993 0.3746 0.4208 Chr8:40805438 chr8 40805438 C / T 0.4906 0.4998 0.3749 0.3347 Chr8:60041414 chr8 60041414 C / T 0.5 0.5 0.375 0.3781 Chr8:80031687 chr8 80031687 T / C 0.4906 0.4998 0.3749 0.2437 Chr8:100308590 chr8 100308590 G / T 0.4906 0.4998 0.3749 0.384 Chr9:1021733 chr9 1021733 C / T 0.4811 0.4993 0.3746 0.2215 Chr9:20776229 chr9 20776229 G / A 0.5 0.5 0.375 0.2514 Chr9:40187718 chr9 40187718 G / A 0.5 0.5 0.375 0.3187 Chr9:60323041 chr9 60323041 A / G 0.4811 0.4993 0.3746 0.3806 Chr9:80291663 chr9 80291663 T / G 0.4811 0.4993 0.3746 0.3333 Chr9:100096490 chr9 100096490 C / T 0.4906 0.4998 0.3749 0.3333 Chr10:329228 chr10 329228 A / G 0.4528 0.4956 0.3728 0.2604 Chr10:20160055 chr10 20160055 G / A 0.5 0.5 0.375 0.2559 Chr10:40359373 chr10 40359373 T / A 0.4906 0.4998 0.3749 0.3278 Chr10:60943857 chr10 60943857 A / G 0.4906 0.4998 0.3749 0.2344 Chr10:80501254 chr10 80501254 C / T 0.5 0.5 0.375 0.4052 Chr10:100161280 chr10 100161280 A / C 0.4811 0.4993 0.3746 0.3201 Chr11:272636 chr11 272636 A / G 0.4906 0.4998 0.3749 0.3667 Chr11:20878348 chr11 20878348 G / T 0.4717 0.4984 0.3742 0.4125 Chr11:40215530 chr11 40215530 T / C 0.4623 0.4972 0.3736 0.3865 Chr11:60492478 chr11 60492478 C / T 0.4906 0.4998 0.3749 0.4292 Chr11:80325947 chr11 80325947 A / G 0.4906 0.4998 0.3749 0.2458 Chr11:100316333 chr11 100316333 C / T 0.5 0.5 0.375 0.3215 Chr12:363733 chr12 363733 C / T 0.5 0.5 0.375 0.3837 Chr12:20055123 chr12 20055123 G / A 0.4906 0.4998 0.3749 0.3889 Chr12:41299901 chr12 41299901 G / A 0.4811 0.4993 0.3746 0.4326 Chr12:60090907 chr12 60090907 A / G 0.4906 0.4998 0.3749 0.3479 Chr12:80726761 chr12 80726761 C / T 0.5 0.5 0.375 0.4531 Chr13:568474 chr13 568474 G / T 0.5 0.5 0.375 0.3781 Chr13:20860940 chr13 20860940 A / C 0.4623 0.4972 0.3736 0.325 Chr13:41119324 chr13 41119324 A / G 0.5 0.5 0.375 0.358 Chr13:60967466 chr13 60967466 C / T 0.5 0.5 0.375 0.3802 Chr13:80014261 chr13 80014261 C / T 0.4623 0.4972 0.3736 0.4361 Chr14:173414 chr14 173414 C / A 0.4623 0.4972 0.3736 0.2722 Chr14:20103544 chr14 20103544 A / G 0.4623 0.4972 0.3736 0.4045 Chr14:40569949 chr14 40569949 T / C 0.4623 0.4972 0.3736 0.2639 Chr14:61360558 chr14 61360558 C / T 0.5 0.5 0.375 0.1979 Chr14:80090322 chr14 80090322 G / A 0.4528 0.4956 0.3728 0.3243 Chr15:95457 chr15 95457 C / T 0.4906 0.4998 0.3749 0.3847 Chr15:22050379 chr15 22050379 T / C 0.4906 0.4998 0.3749 0.3 Chr15:40511804 chr15 40511804 A / G 0.4811 0.4993 0.3746 0.4062 Chr15:60522535 chr15 60522535 C / T 0.5 0.5 0.375 0.333 Chr15:80289235 chr15 80289235 T / C 0.4811 0.4993 0.3746 0.3427 Chr16:388813 chr16 388813 T / C 0.5 0.5 0.375 0.3479 Chr16:20297086 chr16 20297086 C / A 0.4623 0.4972 0.3736 0.3319 Chr16:40141373 chr16 40141373 T / C 0.5 0.5 0.375 0.3014 Chr16:60180840 chr16 60180840 G / A 0.4811 0.4993 0.3746 0.3875 Chr16:80009775 chr16 80009775 C / A 0.4906 0.4998 0.3749 0.4 Chr17:22732 chr17 22732 T / C 0.4528 0.4956 0.3728 0.1615 Chr17:20012014 chr17 20012014 C / T 0.4906 0.4998 0.3749 0.2125 Chr17:40349047 chr17 40349047 T / G 0.4717 0.4984 0.3742 0.2403 Chr17:60072349 chr17 60072349 C / T 0.4811 0.4993 0.3746 0.3194 Chr18:1052076 chr18 1052076 C / T 0.4717 0.4984 0.3742 0.3312 Chr18:20075983 chr18 20075983 T / C 0.4434 0.4936 0.3718 0.3312 Chr18:40134627 chr18 40134627 A / G 0.4906 0.4998 0.3749 0.4375 Chr18:60143101 chr18 60143101 C / T 0.4906 0.4998 0.3749 0.3222 Chr19:896693 chr19 896693 G / A 0.4906 0.4998 0.3749 0.3066 Chr19:20011175 chr19 20011175 G / C 0.4906 0.4998 0.3749 0.2677 Chr19:40278340 chr19 40278340 C / T 0.4906 0.4998 0.3749 0.4726 Chr19:60573059 chr19 60573059 T / C 0.4906 0.4998 0.3749 0.3618 Chr20:152404 chr20 152404 A / G 0.4717 0.4984 0.3742 0.1351 Chr20:20318650 chr20 20318650 T / C 0.5 0.5 0.375 0.4514 Chr20:40072524 chr20 40072524 C / G 0.4906 0.4998 0.3749 0.3455 Chr20:60245114 chr20 60245114 T / G 0.4906 0.4998 0.3749 0.3285 Chr21:62466 chr21 62466 A / G 0.4906 0.4998 0.3749 0.2712 Chr21:20078175 chr21 20078175 A / G 0.5 0.5 0.375 0.1615 Chr21:41338577 chr21 41338577 A / C 0.4906 0.4998 0.3749 0.4747 Chr21:60129810 chr21 60129810 C / T 0.4906 0.4998 0.3749 0.3653 Chr22:199977 chr22 199977 T / C 0.5 0.5 0.375 0.4531 Chr22:20427889 chr22 20427889 C / T 0.4811 0.4993 0.3746 0.3854 Chr22:40090384 chr22 40090384 A / G 0.4434 0.4936 0.3718 0.2177 Chr22:60024824 chr22 60024824 G / A 0.5 0.5 0.375 0.3563 Chr23:876875 chr23 876875 G / C 0.5 0.5 0.375 0.3125 Chr23:20132680 chr23 20132680 G / A 0.4434 0.4936 0.3718 0.4108 Chr23:40120365 chr23 40120365 G / T 0.4906 0.4998 0.3749 0.3031 Chr24:402581 chr24 402581 A / G 0.5 0.5 0.375 0.3785 Chr24:20395361 chr24 20395361 A / G 0.4906 0.4998 0.3749 0.2882 Chr24:40249348 chr24 40249348 G / A 0.4906 0.4998 0.3749 0.2153 Chr24:60088738 chr24 60088738 G / C 0.5 0.5 0.375 0.3983 Chr25:760037 chr25 760037 G / A 0.5 0.5 0.375 0.4021 Chr25:20336470 chr25 20336470 T / G 0.5 0.5 0.375 0.3833 Chr25:40436063 chr25 40436063 G / T 0.4623 0.4972 0.3736 0.4153 Chr26:1200931 chr26 1200931 A / G 0.5 0.5 0.375 0.3278 Chr26:20490728 chr26 20490728 T / C 0.4906 0.4998 0.3749 0.4243 Chr26:40074346 chr26 40074346 C / G 0.4434 0.4936 0.3718 0.4378 Chr27:757022 chr27 757022 T / A 0.5 0.5 0.375 0.3194 Chr27:20379873 chr27 20379873 C / T 0.4811 0.4993 0.3746 0.3802 Chr27:40141167 chr27 40141167 G / A 0.4906 0.4998 0.3749 0.3566 Chr28:810217 chr28 810217 G / A 0.4717 0.4984 0.3742 0.3028 Chr28:20483273 chr28 20483273 C / G 0.4623 0.4972 0.3736 0.4306 Chr28:40063096 chr28 40063096 A / G 0.4717 0.4984 0.3742 0.2743 Chr29:702099 chr29 702099 G / A 0.4528 0.4956 0.3728 0.3622 Chr29:20360832 chr29 20360832 A / C 0.4906 0.4998 0.3749 0.3656 Chr29:40093537 chr29 40093537 C / T 0.5 0.5 0.375 0.4188

[0049] Example 2: Application of 141 SNP molecular marker combinations in identifying Sunite kinship To verify the accuracy and feasibility of using the 141 selected SNP molecular marker combinations for kinship identification in an actual population of Sunite Mongolian cattle, this example selected 8 pairs of combinations with clearly known parentage from a Sunite Mongolian cattle population that had never participated in the high-polymorphic SNP screening (see Table 6). Based on the 141 high-polymorphic SNP molecular marker combinations screened in Example 1, the kinship between samples was inferred by calculating Kinship and PI_HAT values ​​using King and Plink software. The relevant results are shown in Table 8. Taking F1 individuals as an example, Kinship and PI_HAT values ​​were calculated for their candidate parents.

[0050] The Kinship value is a kinship coefficient; the larger the coefficient, the closer the kinship. According to international standards, a Kinship value greater than 0.354 indicates identical twins; (0.177, 0.354) indicates a kinship within one generation; (0.0884, 0.177) indicates a kinship within two generations; and (0.0442, 0.0884) indicates a kinship within three generations.

[0051] The Plink software calculates the PI_HAT value using the `--genome` parameter to infer the proportion of IBD (identical by descent) sharing among individuals. The PI_HAT value is P(IBD=2) + 0.5 × P(IBD=1), where P(IBD=n) is the probability that individuals share n alleles. Different PI_HAT values ​​correspond to different levels of kinship: approximately 0.375 within one generation, approximately 0.1875 within two generations, and approximately 0.09375 within three generations.

[0052] Table 6. Samples with intra-generational kinship inferred from genome-wide SNP-calculated kinship coefficients and IBD shared values ​​(PI_HAT).

[0053] Table 7. Samples with intra-generational kinship inferred from kinship coefficients (Kinship) and IBD share values ​​(PI_HAT) calculated based on 141 SNP molecular marker combinations.

[0054] Table 8. Verification Tests for Paternity Testing

[0055]

[0056]

[0057] Note: * indicates a very significant parent-child relationship.

[0058] As shown in Table 8, the true parent of individual F1 is P6, with a Kinship value of 0.2054 and a PI_HAT value of 0.4741. The remaining samples are all outside the first-generation Kinship and IBD shared value (PI_HAT) thresholds and cannot be the true parents of F1. Based on these results and the previously recorded sex, it can be inferred that the true father of individual F2 is P2 and the mother is P3.

[0059] The most probable parentage results obtained by using the 141 SNP molecular marker combinations provided by this invention are highly consistent with the kinship inference results based on whole-genome SNP molecular markers (Tables 6 and 7), further verifying the accuracy and feasibility of the SNP molecular marker combinations provided by this invention in the parentage identification of Sunite Mongolian cattle.

[0060] In summary, the kinship inference system constructed by combining highly polymorphic SNP molecular markers not only reduces detection costs but also achieves kinship identification results in Sunite Mongolian cattle that are equivalent to whole-genome typing. It is suitable for pedigree correction and parent identification of large-scale populations in actual Sunite Mongolian cattle production.

[0061] Although the present invention has described in detail, through general description and specific embodiments, the SNP molecular marker combinations used for kinship identification in Sunite Mongolian cattle, those skilled in the art can still make various modifications and improvements based on the present invention without departing from its spirit and essence. All such modifications and equivalent variations should be considered to fall within the protection scope of the present invention.

[0062] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A combination of SNP molecular markers for identifying kinship in Sunite Mongolian cattle, characterized in that... The SNP molecular marker combination consists of the following SNP molecular markers:

2. The application of the SNP molecular marker combination as described in claim 1 in identifying the kinship of Sunite Mongolian cattle.

3. The application of the SNP molecular marker combination as described in claim 1 in the genetic improvement breeding of Sunite Mongolian cattle.

4. A method for identifying the kinship of Sunite Mongolian cattle, characterized in that... The method includes the steps of extracting genomic DNA from the cattle to be tested and performing genotyping analysis using the SNP molecular marker combination as described in claim 1.

5. The method according to claim 4, characterized in that... The method further includes the following steps: calculating the Kinship value and PI_HAT value among the tested cattle individuals, and inferring the kinship relationship between the individuals based on the obtained results.

6. The method according to claim 4, characterized in that... , If the Kinship value between the tested cattle individuals is within the range of (0.177, 0.354) and PI_HAT ≈ 0.375, then they are considered to be related within one generation. If the Kinship value between the tested cattle individuals is within the range of (0.0884, 0.177) and PI_HAT≈0.1875, then they are determined to be related within two generations. If the Kinship value between the tested cattle individuals is within the range of (0.0442, 0.0884) and PI_HAT≈0.09375, then they are determined to be related within three generations. If the Kinship value between the tested cattle individuals is ≤0.0442 and PI_HAT<0.05, then they are judged to have no significant kinship.

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