SNP (Single Nucleotide Polymorphism) molecular marker combination for genetic relationship identification of wuzhu cattle and application of SNP molecular marker combination

Abstract

The invention belongs to the field of molecular genetics, and particularly relates to an SNP (Single Nucleotide Polymorphism) molecular marker combination for genetic relationship identification of wuzhu cattle and application of the SNP molecular marker combination. The SNP marker combination is composed of 140 high-polymorphism SNP sites distributed on a plurality of chromosomes of a bovine whole genome, the average distance between the sites is reasonable, and the SNP marker combination has good genome coverage. The selected SNP site has high allele frequency (MAF), expected heterozygosity (He) and polymorphic information content (PIC), and the cumulative exclusion probability is 0.9999. King and Plink software are combined to deduce a genetic relationship coefficient (Kinship) and an IBD shared value (PIHAT) between samples, and a result shows that the marker combination can realize a genetic relationship recognition effect equivalent to that of whole genome SNP data in Wuzhu white cattle (grassland white cattle), and the scientificity and practicability of the marker combination are verified. The SNP molecular marker combination can be widely applied to the processes of germplasm resource management, pedigree correction and cattle breeding, and has good popularization prospects and economic benefits.

Description

Technical Field

[0001] The invention belongs to the field of molecular genetics, and in particular relates to a SNP molecular marker combination for identifying the kinship of Ujumqin white cattle and an application thereof. Background Art

[0002] Complete pedigree information in the livestock industry is crucial for genetic evaluation and selection planning. However, multiple studies have found significant error rates in cattle pedigree records: for example, the average error rate in European and American cattle pedigrees is approximately 10% (Visscher PM et al., 2002), while some Chinese Holstein herds have pedigree error rates reaching 20% ​​(Zhang Z et al., 2014), and swine pedigree error rates can reach as high as 37% (Zhang J et al., 2020). Pedigree errors reduce the accuracy of breeding evaluations and hinder genetic progress. It is estimated that when the pedigree error rate in dairy cattle is 10%, the response rate to selection can decrease by approximately 2–3% (Visscher PM et al., 2002). Weller et al. (2004) noted that reducing the pedigree error rate in cattle from approximately 11.7% to 8% could increase annual genetic progress by at least 1%. Therefore, to ensure an annual genetic progress rate exceeding 1%, the pedigree error rate must be kept around 8%. Therefore, correcting genealogical errors and adopting accurate kinship identification technologies (such as DNA-based identification) have become urgent tasks (Zhou Y et al., 2024).

[0003] The Ujumqin White Cattle (Grassland White Cattle) is an important local high-quality beef cattle breed in China, primarily distributed in West Ujumqin Banner, Inner Mongolia Autonomous Region. The breed originated in the early 1970s, when French Charolais cattle were introduced to China as a symbol of friendly exchange between China and foreign countries. Based on geographical and ecological conditions, they were bred in West Ujumqin Banner. Initially introduced through registration and breeding, performance testing, and long-term localized breeding, a herd of beef cattle with a large physique, rapid weight gain, strong reproductive capacity, and excellent stress tolerance, adapted to the alpine and arid grassland environment, was established. This herd was designated the "Ujumqin White Cattle (Grassland White Cattle)." Currently, the year-end stock of Ujumqin White Cattle (Grassland White Cattle) in the region exceeds 58,000, making them a significant local beef cattle resource in Inner Mongolia.

[0004] In the production and management of Ujumqin White Cattle (Grassland White Cattle), due to their mostly free-range grazing, breeding methods including natural mating and artificial insemination, and extensive management, pedigree records often contain omissions or errors. Under similar management conditions, the pedigree error rate for beef cattle herds can be as high as 30%. Errors in pedigree records not only affect the accuracy of individual genetic assessments but also reduce the efficiency of breeding and mating, affecting the rate of genetic progress. Therefore, correcting kinship and verifying pedigree information for the Ujumqin White Cattle (Grassland White Cattle) population is key to ensuring breeding reliability and promoting the sustainable development of the industry.

[0005] With the development of molecular technology, DNA-based kinship identification has been widely used, among which single nucleotide polymorphism (SNP) markers have become the mainstream tool due to their inherent advantages. SNPs are distributed throughout the genome and are extremely numerous, with an extremely low mutation rate of approximately 1×10 -9 to 5×10 -9 (Martinez-Arias et al., 2001), with significantly greater genetic stability than microsatellite markers; most SNPs are biallelic, making them suitable for high-throughput automated detection and reducing human error. Compared with previous reliance on microsatellite markers, SNP sequencing offers significant advantages in cost and efficiency. However, due to the low polymorphism of individual loci, identification capabilities are limited.

[0006] Currently, there is a lack of systematic molecular tools to verify and calibrate the pedigree of the Ujumqin White Cattle (Grassland White Cattle). Although my country has a SNP marker combination for kinship identification of Chinese Simmental cattle, it cannot be used for kinship identification of the Ujumqin White Cattle (Grassland White Cattle) due to their unique genetic background. Summary of the Invention

[0007] The present invention establishes a SNP molecular marker combination suitable for the Ujumqin White Cattle (Grassland White Cattle) population, which can not only effectively identify pedigree record errors and improve the accuracy of breeding and selection, but also provide important technical support for the protection of Ujumqin White Cattle (Grassland White Cattle) germplasm resources, genetic improvement and the construction of beef product quality traceability system.

[0008] The purpose of the present invention is to provide a SNP molecular marker combination for identifying the kinship of Ujumqin white cattle (grassland white cattle).

[0009] The present invention analyzed the whole-genome resequencing data of 424 Ujumqin white cattle (Grassland white cattle) and screened out 140 SNP sites on 29 autosomes, which can be used to identify the kinship of the Ujumqin white cattle (Grassland white cattle) population. It contains 140 markers and their base mutation and position information. These SNP marker combinations are distributed on 29 autosomes, and the average distance between adjacent SNPs on the same chromosome is 20.15M.

[0010] The SNP molecular marker combination provided by the present invention for identifying the kinship of Ujumqin white cattle (grassland white cattle) includes the following 140 SNP molecular markers:

[0011]

[0012]

[0013]

[0014]

[0015] In addition, the present invention provides other information on the above-mentioned 140 SNP molecular markers. The average values ​​of the minimum allele frequency (MAF), expected heterozygosity (He), and polymorphic information content (PIC) of this marker combination are 0.491245, 0.499519, and 0.374759, respectively; the cumulative exclusion probability exceeds 99.99%.

[0016] The details are as follows:

[0017]

[0018]

[0019]

[0020]

[0021] The present invention provides the application of the above 140 molecular marker combinations in identifying the kinship of Ujumqin white cattle (grassland white cattle).

[0022] The present invention provides the application of the above 140 molecular marker combinations in the genetic improvement and breeding of Ujumqin white cattle (grassland white cattle).

[0023] The invention relates to the use of a kit containing the detection kit for the above-mentioned 140 SNP molecular marker combinations in the genetic improvement and breeding of Zhumuqin white cattle (grassland white cattle).

[0024] The present invention provides a method for identifying the kinship of Ujumqin white cattle (grassland white cattle), comprising the following steps:

[0025] Genomic DNA was extracted from the cattle to be tested, and the marker combination of the above 140 highly polymorphic SNP loci was genotyped using high-throughput sequencing technology. The SNP marker combination was distributed on 29 autosomes, with an average distance of 20.15M between adjacent SNPs on the same chromosome. Linkage disequilibrium pruning (LD pruning, parameter: --indep-pairwise50100.1) was used to ensure site independence.

[0026] The genotype data of 140 SNPs of the individuals to be tested were input into King software and Plink software. King is a tool for family relationship inference and population genetic structure analysis; Plink is a genetic data analysis software that supports quality control, association analysis and population structure assessment of large-scale SNP data, and is often used in studies such as population genetics, GWAS and kinship inference.

[0027] The kinship coefficient (Kinship) and IBD sharing value (PI_HAT) between individuals were calculated, and the kinship between individuals was inferred based on the results.

[0028] The Kinship value represents the probability that two individuals share alleles due to a common ancestor, reflecting the closeness of their kinship relationship. It is calculated using King software based on the allele sharing patterns of 140 SNP loci. The core logic is: Kinship = 1 / 2 × (expected proportion of shared alleles). Specifically, the software performs a weighted summation of the allele matching conditions (homozygous matching, heterozygous cross matching, etc.) at each SNP locus to ultimately determine the Kinship value between individuals.

[0029] According to international standards and the verification results of this invention, the kinship level corresponding to the Kinship value is:

[0030] 0.354: identical twins (complete gene sharing); (0.177, 0.354]: relatedness within one generation (e.g., parent-child, full siblings); (0.0884, 0.177]: relatedness within two generations (e.g., grandparent-grandchild, half siblings); (0.0442, 0.0884]: relatedness within three generations (e.g., great-grandparent-grandchild); ≤0.0442: no significant relatedness.

[0031] IBS status homology refers to the fact that the allele sequences shared by two individuals are identical; IBD consanguinity refers to the fact that the alleles shared by two individuals are derived from a common ancestor.

[0032] King software performs IBS analysis (parameter: --ibs), which can calculate the specific IBS value between two samples. However, it does not provide a specific relationship between the two samples. Instead, it can only show an increasing relationship between the two samples, i.e., the larger the IBS value, the closer the relationship. The present invention constructs a matrix based on the IBS values.

[0033] 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 of IBD = 1, 2, or 0.

[0034] PI_HAT: IBD ratio, that is, P(IBD=2)+0.5*P(IBD=1).

[0035] The kinship values ​​corresponding to the PI_HAT values ​​are: kinship within the first generation ≈ 0.375; kinship within the second generation ≈ 0.1875; kinship within the third generation ≈ 0.09375.

[0036] Based on the combined analysis of Kinship value and PI_HAT value, the criteria for judging the kinship of Ujumqin White Cattle (Grassland White Cattle) in the present invention are as follows:

[0037] If the Kinship value of the pair of individuals to be tested is in (0.177, 0.354] and PI_HAT is approximately 0.375, it is determined to be within one generation of kinship;

[0038] If the Kinship value is in (0.0884, 0.177] and PI_HAT is approximately 0.1875, it is determined to be within two generations of kinship (such as grandchild and grandparent);

[0039] If the Kinship value is in (0.0442, 0.0884] and PI_HAT is approximately 0.09375, it is determined to be within three generations of kinship;

[0040] If the Kinship value is less than or equal to 0.0442 and PI_HAT is less than 0.05, it is determined to be no significant kinship;

[0041] When multiple candidate parents have the same kinship level, the individual with the Kinship and PI_HAT values closest to the theoretical values is selected as the most likely parent.

[0042] Verification on two known families (Family_1 and Family_2) shows that the Kinship value and PI_HAT value calculated using the 140 SNP marker combination of the present application are consistent with the kinship relationship results inferred from whole genome SNP data, with a consistency of 100%;

[0043] In the top 100% (140 SNP) grouping, the family mis-typing rate is the lowest (Family_1 is 0.0571, Family_2 is 0.0500), which verifies the stability and high accuracy of the method in the Wuzhumuqin White Cow (Grassland White Cow) population.

[0044] The present application has the following advantages:

[0045] The SNP molecular marker combination for identifying the kinship of Wuzhumuqin White Cows (Grassland White Cows) provided by the present application comprises 140 SNP markers distributed on 29 autosomes, the average distance between adjacent SNP markers on the same chromosome is 20.15M, and the average values of the minimum allele frequency (MAF), expected heterozygosity (He), and polymorphic information content (PIC) of the marker combination are 0.491245, 0.499519, and 0.374759, respectively. The cumulative exclusion probability is greater than 0.9999 when the genotype of the unknown mother is not known. The SNP molecular marker combination of the present application can be effectively applied to pedigree correction, germplasm resource management, and genetic improvement breeding of Wuzhumuqin White Cows (Grassland White Cows), and has the characteristics of simple operation, low cost, and high accuracy. It has good application prospect and economic benefit. DETAILED DESCRIPTION

[0046] In order to make the objectives, technical solutions and advantages of the present application clearer, the technical solutions of the present application will be described in detail below. Obviously, the described embodiments are only some of the embodiments of the present application, but not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative labor fall within the scope of protection of the present application.

[0047] If not specifically stated, the technical means used in the embodiments are conventional means known to those skilled in the art.

[0048] The chemical reagents used in the embodiments are all commercially available.

[0049] The 424 samples of Xiwuzhumuqin White Cattle (Grassland White Cattle) used in the present application were collected from farmers in Xiwuzhumuqin Banner, Inner Mongolia, including 40 breeding bulls, 192 cows and 192 offspring.

[0050] Screening of 140 SNP molecular markers for identifying the genetic relationship of Xiwuzhumuqin White Cattle (Grassland White Cattle) in Example 1

[0051] Forty breeding bulls, 192 cows and 192 offspring samples of different bloodlines were collected from herdsmen in Xiwuzhumuqin Banner, Inner Mongolia, a total of 424 blood or tissue samples. Illumina NovaSeq 6000 platform was used for sequencing processing. The raw sequencing data was filtered and de-redundant using fastp software, and the filtered sequencing data was aligned to the bovine reference genome ARS-UCD2.0 using bwa (0.7.12-r1039). Based on the sequence alignment results, PCR duplicates were removed using Picard software, and data with alignment quality lower than 20 were removed. Finally, GATK software was used for population detection to obtain the original variant detection information, including SNP and InDel.

[0052] Considering the pedigree error rate, in order to obtain SNP sites with identification ability, the following steps were adopted for SNP screening and verification in this embodiment:

[0053] 1. Filtering of SNPs;

[0054] 2. SNPs were divided into 10 grades according to SNP polymorphism (PIC value);

[0055] 3. The error typing rate of two families (known parent-offspring triad samples (Table 2)) was calculated;

[0056] 4. Based on the SNP data sets corresponding to different grades, kinship and IBS analysis were performed, and the genetic relationship obtained by whole genome SNP identification was compared and verified.

[0057] In step 1, SNPs are filtered based on the following criteria:

[0058] (1) Retain sites with MAF > 0.4 (software: vcftools; parameter: --maf0.4);

[0059] (2) retaining sites with 100% individual typing success rate (software: vcftools; parameter: --max-missing1);

[0060] (3) HWE test (software: vcftools; parameter: --hwe 0.0001);

[0061] (4) retaining biallelic sites (software: vcftools; parameters: --min-alleles 2 --max-alleles2);

[0062] (5) retain sites with heterozygosity higher than the mean (mean = 0.455007) (software: plink; parameters: --allow-extra-chr --chr-set 29 --hardy);

[0063] (6) retaining sites with PIC higher than the mean (mean = 0.371658);

[0064] (7) LD pruning was performed (software: plink; parameter: --indep-pairwise 50100.1, representing the pruning method: window size is 50 SNPs, window step is 10 SNPs, linkage disequilibrium coefficient r^2 threshold is 0.1);

[0065] (8) Screening only loci on autosomes;

[0066] (9) SNP density filtering: based on the filtered SNP dataset, the data was divided into 20M windows and the first site in each window was taken.

[0067] Based on all the above filtering conditions, a SNP dataset containing 140 loci was finally obtained.

[0068] SNP dataset construction and classification:

[0069] The high-quality SNP dataset of 140 loci obtained through the above screening process was further classified according to SNP polymorphism at the loci of each chromosome and divided into ten categories with retention ratios of 100%, 90%, 80%, 70%, 60%, 50%, 40%, 30%, 20%, and 10%. The results are shown in Table 1:

[0070] Table 1 Number of SNP molecular markers in each level

[0071]

[0072] Error rate and kinship discrimination analysis:

[0073] Using the following two known parent-offspring triplet samples (Table 2), a comparative analysis of the error typing rate and kinship discrimination ability of the above-mentioned data sets of different levels was performed.

[0074] Table 2 Parent-offspring triad samples known by paternity testing

[0075]

[0076] The differences in the mistyping rates of the two families at different levels are as follows:

[0077] Table 3 Mistyping rates of two families at different classification levels

[0078]

[0079] The results in Table 3 show that the mistyping rates for both families gradually decreased as the SNP stratification increased from the top 10% to the top 100%. In the top 100% group (i.e., using all 140 SNPs), the mistyping rates for Family_1 and Family_2 were 0.0571 and 0.0500, respectively, indicating that the stability and accuracy of kinship determination significantly improved with an increase in the number of markers. Comprehensive analysis indicates that the lowest mistyping rate and optimal kinship identification were achieved when all 140 SNP markers were used for kinship identification.

[0080] At the same time, based on all SNP molecular markers before filtering, the error typing rates of the two families were calculated. The results are shown in Table 4:

[0081] Table 4 Mistyping rates of all SNPs in two families

[0082]

[0083] The results in Table 4 show that although the number of SNPs in the whole genome is as high as about 25 million, its error typing rate is only slightly lower than that of the 140 efficient SNP marker combinations (Family_1 is 0.0338, Family_2 is 0.0297, compared with the error rate of about 0.05 for 140 SNPs), but the difference between the two is not large. This conclusion shows that under the premise of significantly reducing the data scale, detection cost and analysis time, the 140 SNP combination can still achieve a kinship determination effect close to the whole genome level, reflecting its efficiency and practicality in practical applications.

[0084] In order to obtain the most accurate identification efficiency, the present invention finally screened 140 SNP molecular markers as the SNP molecular marker combination for the kinship identification of Ujumqin white cattle (grassland white cattle) (see Table 5).

[0085] Table 5 Basic overview of 140 SNP markers

[0086]

[0087]

[0088]

[0089] The 140 SNP markers are distributed across 29 autosomes, with an average of five SNP markers per chromosome. The average distance between adjacent SNPs on the same chromosome is 20.15 M, avoiding linkage disequilibrium between SNPs. This SNP marker panel exhibits high polymorphism: the minimum allele frequency (MAF) ranges from 0.47 to 0.5, with an average of 0.491245; the expected heterozygosity (He) ranges from 0.49 to 0.5, with an average of 0.499519; the average polymorphism information content (PIC) is 0.374759; and the cumulative exclusion probability exceeds 99.99%.

[0090] Example 2 Application of 140 SNP Molecular Marker Combinations in Identifying the Genealogy of Ujumqin White Cattle (Grassland White Cattle)

[0091] To verify the accuracy and feasibility of the 140 SNP molecular marker combinations screened for kinship identification in an actual population of Ujumqin white cattle (Grassland White Cattle), this example selected 10 pairs of pairs with clear, known parent-offspring relationships from the Ujumqin white cattle population (see Table 6). Based on the 140 highly polymorphic SNP molecular marker combinations screened by this invention, King and Plink software were used to infer kinship between samples by calculating Kinship and PI_HAT values. The relevant results are shown in Table 6. Taking F1 individuals as an example, their candidate parents were ranked from high to low according to Kinship and PI_HAT values.

[0092] The Kinship value is the kinship coefficient, with a larger coefficient indicating closer kinship. According to international standards, a Kinship value greater than 0.354 indicates identical twins; (0.177, 0.354) indicates kinship within one generation; (0.0884, 0.177) indicates kinship within two generations; and (0.0442, 0.0884) indicates kinship within three generations.

[0093] The Plink software calculates the PI_HAT value with the --genome parameter, which is used to infer the proportion of IBD (identical by descent) sharing between individuals. The PI_HAT value is P(IBD=2) + 0.5 x P(IBD=1), where P(IBD=n) is the probability of sharing n alleles between individuals. Different PI_HAT values can correspond to different levels of kinship: approximately 0.375 for first-degree kinship, approximately 0.1875 for second-degree kinship, and approximately 0.09375 for third-degree kinship.

[0094] Table 6 Samples with first-degree kinship inferred based on the kinship coefficient (Kinship) and IBD sharing value (PI_HAT) calculated based on whole genome SNPs

[0095]

[0096]

[0097] Table 7 Samples with first-degree kinship inferred based on the kinship coefficient (Kinship) and IBD sharing value (PI_HAT) calculated based on 140 SNP molecular marker combinations

[0098]

[0099] The Plink software calculates the PI_HAT value with the --genome parameter, which is used to infer the proportion of IBD (identical by descent) sharing between individuals. The PI_HAT value is P(IBD=2) + 0.5 x P(IBD=1), where P(IBD=n) is the probability of sharing n alleles between individuals. Different PI_HAT values can correspond to different levels of kinship: approximately 0.375 for first-degree kinship, approximately 0.1875 for second-degree kinship, and approximately 0.09375 for third-degree kinship.

[0100] Table 8 Paternity identification verification test

[0101]

[0102]

[0103] Note: * indicates a highly significant parent-child relationship.

[0104] As shown in Table 8, the true parent of the F1 individual is P1, with a Kinship value of 0.2247 and a PI_HAT value of 0.3885. The remaining samples fall outside the first-generation kinship thresholds for Kinship and IBD sharing values ​​(PI_HAT), and therefore cannot be the true parent of the F1. Based on these results and the previously recorded sex, we can infer that the true paternal parent of the F2 individual is P2, and the dam is P3.

[0105] The most likely parent results inferred using the 140 SNP molecular marker combination provided by the present invention are highly consistent with the results of kinship inference based on whole-genome SNP molecular markers (Tables 6 and 7), further verifying the accuracy and feasibility of the SNP molecular marker combination provided by the present invention in paternity testing of Ujumqin White Cattle (Grassland White Cattle).

[0106] In summary, the kinship inference system constructed through the combination of highly polymorphic SNP molecular markers not only reduces the detection cost, but also achieves the kinship identification effect of Ujumqin White Cattle (Grassland White Cattle) equivalent to whole genome typing. It is suitable for pedigree correction and parent identification of large-scale populations in the actual production of Ujumqin White Cattle (Grassland White Cattle).

[0107] Although the present invention has described in detail, through general descriptions and specific embodiments, a SNP molecular marker combination for identifying the kinship of Ujumqin white cattle (Grassland white cattle), and the embodiments of the present invention use King software and Plink software, 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 person skilled in the art can easily conceive of changes or substitutions within the technical scope disclosed in the present invention, and all such changes or substitutions should be included in the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be based on the scope of protection of the claims.

Claims

1. A SNP molecular marker combination for identifying the kinship of Ujumqin white cattle, characterized in that: The SNP molecular marker combination consists of 140 SNP molecular markers:

2. Use of the SNP molecular marker combination according to claim 1 in identifying the kinship of Ujumqin white cattle.

3. Use of the SNP molecular marker combination according to claim 1 in genetic improvement and breeding of Ujumqin white cattle.

4. A method for identifying the kinship of Ujumqin white cattle (grassland white cattle), characterized by: The following steps are involved: Extract genomic DNA from the cattle to be tested, perform high-throughput SNP site sequencing, and use the SNP molecular marker combination described in claim 1 to perform genotyping analysis.

5. The method according to claim 4, characterized in that The genotyping analysis is to calculate the Kinship value and PI_HAT value between the tested cattle individuals, and infer the kinship relationship between the individuals based on the obtained results.

6. The method according to claim 5, characterized in that The method for determining kinship is as follows: If the Kinship value of the tested cattle pair is (0.177, 0.354], and PI_HAT≈0.375, it is determined to be a relationship within one generation; If the Kinship value of the tested cattle pair is (0.0884, 0.177], and PI_HAT≈0.1875, then it is determined to be a two-generation kinship relationship (such as grandparents and grandchildren); If the Kinship value of the tested cattle pair is (0.0442, 0.0884], and PI_HAT≈0.09375, then it is determined to be a three-generation kinship relationship; If the Kinship value of the tested cattle pair is ≤0.0442 and PI_HAT<0.05, it is judged that there is no significant kinship.

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