A high-density SNP chip in the whole genome of dairy cows and its application

Abstract

This invention provides a medium-to-high density 126K SNP chip for the whole genome of dairy cattle, containing reagents for detecting 120,155 SNP loci. These SNP loci are derived from three categories: first, 5,363 SNP loci identified and mined by the inventors' team in previous research that are significantly associated with important economic traits in dairy cattle; second, 223 SNP loci from major genetic defect loci and paternity testing loci in dairy cattle; and third, 114,569 SNP loci selected from existing commercial dairy cattle genome chips based on detection rate, gene frequency, location uniqueness, genotype filling principles, and genomic distribution in China's large-scale dairy cattle population. This chip can be used for genome-wide association studies, genome-wide selection breeding, paternity testing, and identification of common bovine genetic defects in Chinese Holstein cattle.

Description

Technical Field

[0001] This invention relates to the fields of molecular biology and biochips. Specifically, this application provides a medium-to-high density 126K SNP chip for the whole genome of dairy cows and its applications. Background Technology

[0002] SNP (Single Nucleotide Polymorphism) analysis is currently an important analytical method for discovering and identifying key genes for important complex traits and for genomic selection (GS). Chip-based SNP genotyping technology can simultaneously detect a large number of SNP loci, offering advantages such as speed and high throughput, significantly improving detection efficiency. As information carriers for genomic selection, there are currently many commercially available chips for dairy cattle, most of which are solid-phase chips based on microbead arrays. Since 2009, Illumina has developed two bovine genome chips: the Bovine SNP 50K chip (containing 54,000 SNPs) and the Bovine HD chip (containing 777,962 SNPs); since 2014, Neocate has developed the GeneSeek 80K, GeneSeek 150K, GeneSeek 100K, and GeneSeek 9K bovine genome SNP chips. Currently, all chips used in dairy cattle breeding in my country are imported. These chips are widely used in domestic and international dairy cow genome assessment and gene mining, but they have the following shortcomings: (1) Chip development is based on Holstein dairy cow populations in Europe and the United States (Holstein dairy cows dominate dairy production in most countries of the world), and SNP effect sites related to important traits of Holstein dairy cow populations in my country are not included in the chips (Holstein cattle account for 90% of my country's dairy cow population). Therefore, the efficiency of genome assessment and gene mining for the genetic background of Holstein dairy cow populations in my country is low; (2) Chip detection technology is limited by human resources, and dairy cow biological samples need to be sent abroad to detect gene information, which makes it impossible to guarantee the security of genetic resource information of individual dairy cows in my country; (3) The current internationally used dairy cow chips are solid-phase chips. This technology is based on the detection of quantified fluorescent labels by microbead arrays to detect genotypes. Its probe molecules are fixed on the support to form a regularly arranged array of dots, and the site update and upgrade cycle is long and costly.

[0003] Therefore, it is essential to design and develop a medium-to-high density SNP breeding chip for Holstein dairy cattle in my country for whole-genome selection. This would not only enable my country to reduce its reliance on imports for key dairy cattle breeding technologies, improve its independent breeding capacity for superior breeders, and accelerate breeding efficiency, but also ensure the security of genetic information of my country's dairy cattle genetic resources. Furthermore, it would reduce detection costs and facilitate the addition of new important trait-related SNP loci to maintain continuous chip iteration and upgrades. Taking all these factors into consideration, the inventors propose a medium-to-high density 126K SNP chip for the entire dairy cattle genome based on liquid-phase capture of target region genome sequences and high-depth sequencing genotyping technology. Summary of the Invention

[0004] This application provides a medium-to-high density 126K SNP chip for the whole genome of dairy cows, wherein the 126K SNP chip contains reagents for detecting SNP loci associated with dairy cow milk production, body size and health traits.

[0005] Furthermore, the 126K SNP chip contains reagents for detecting the SNP sites shown in Table 2 of the instruction manual.

[0006] Furthermore, the reagent is a probe.

[0007] Furthermore, the 126K SNP chip is a liquid-phase probe hybridization chip.

[0008] Furthermore, the 126K SNP chip contains probes with sequences as shown in SEQ ID NO.1-12.

[0009] On the other hand, this application provides the application of the aforementioned 126K SNP chip in dairy cow breeding.

[0010] Furthermore, the breeding is molecular marker-assisted breeding, specifically referring to genome selection breeding.

[0011] On the other hand, this application provides the application of the above-mentioned 126K SNP chip in dairy cow genotyping detection.

[0012] On the other hand, this application provides the application of the above-mentioned 126K SNP chip in the identification of kinship in dairy cows.

[0013] On the other hand, this application provides the application of the above-mentioned 126K SNP chip in the diagnosis of genetic defects in dairy cows.

[0014] Furthermore, the cow is a Holstein cow.

[0015] The SNP molecular markers are mainly derived from three types of SNP loci: The first type comprises 5,363 SNP loci significantly associated with important economic traits in dairy cows, identified and mined by the inventors' team in previous research; the second type comprises 223 SNP loci related to major genetic defects and paternity testing in dairy cows; and the third type comprises 114,569 SNP loci selected from existing commercial dairy cow genome chips based on detection rate, gene frequency, location uniqueness, genotype filling principles, and genomic distribution within China's large-scale dairy cow population. These three types together contain a total of 120,155 SNP loci.

[0016] Due to space limitations, only a portion of the probe sequences are listed in this application. Given the known SNP sites, those skilled in the art can routinely design corresponding detection probes based on chip type, detection method, and other requirements, and verify their accuracy.

[0017] The 126K SNP chip for dairy cows in this application has three advantages: (1) It adds genetic variation sites of functional genes related to important traits in the genetic background of Holstein dairy cows in my country. The inventors' team previously mined and identified a large number of functional gene SNP effect sites that are significantly related to milk production, body size and health traits through multi-omics research, making this chip more suitable for genome selection and genetic analysis of Holstein dairy cows in my country; (2) The SNP sites in Chinese dairy cows have high polymorphism and are evenly distributed throughout the genome, ensuring the accuracy of genome selection; (3) Compared with the internationally used 50K, 80K, 100K and 150K chips for dairy cows, its detection cost is reduced to a certain extent, making it more suitable for large-scale genome reference group expansion and evaluation of breeding value of breeding cattle. In order to improve the efficiency of breeding of excellent breeding cattle, considering the cost of typing and the accuracy of genome selection, this patent designs a 126K SNP (120, 155 SNPs) chip. This is of great significance for my country to break free from its dependence on imported dairy cow bio-breeding chips, rapidly expand the size of the genome reference population, and cultivate excellent breeding bulls and cows to accelerate population genetic improvement. Attached Figure Description

[0018] Figure 1 This is a flowchart illustrating the chip design and fabrication process of the present invention.

[0019] Figure 2 This invention provides the distribution of SNP sites across the entire genome.

[0020] Figure 3 This is a physical image of the SNP chip of this invention. Detailed Implementation

[0021] The present invention will now be described in further detail with reference to specific embodiments. The examples given are merely illustrative of the invention and are not intended to limit its scope. The embodiments provided below can serve as a guide for further improvements by those skilled in the art and do not constitute a limitation on the invention in any way.

[0022] Chip design process as Figure 1 As shown.

[0023] Example 1: Obtaining the first type of SNP site

[0024] Studies have shown that adding SNP loci significantly associated with target traits to SNP chips can improve the accuracy of genomic selection. Therefore, it is necessary to add loci representing the genetic background of Chinese Holstein dairy cattle populations to the chips. The inventors' team previously used multi-omics technologies, including genome-wide association studies, resequencing, transcriptomics, and metabolomics, to mine and identify 5,363 SNP loci significantly associated with important traits (including milk production, body size, and health) in Chinese Holstein dairy cattle.

[0025] The first category of loci is located at SNP loci numbered 1-5,363 in Table 2. The detailed process for obtaining each trait-related SNP locus is as follows:

[0026] 1. Milk production characteristics

[0027] Milk production traits are the most important economic traits of dairy cows, including milk yield, milk fat content, milk protein content, milk fat percentage, milk protein percentage, and milk fatty acid content.

[0028] (1) Analysis of the genetic effects of candidate genes

[0029] Current research has identified DGAT1, GHR, ABCG2, and EEF1D as major genes influencing milk production traits in dairy cows. Meanwhile, the inventors' team previously conducted transcriptomic and proteomic analyses of liver tissues from Chinese Holstein cattle at different lactation stages (dry period, early lactation, and peak lactation) using iTRAQ technology, identifying a total of 3252 proteins (FDR≤0.01), of which 905 were differentially expressed (P ≤ 0.05, FCE ≤ 1.2). In the three comparison groups (dry period vs. early lactation, dry period vs. peak lactation, and early lactation vs. peak lactation), the numbers were 523, 337, and 458, respectively. Subsequently, through functional enrichment analysis, and by comparing the physical locations of SNPs significantly associated with milk production traits in OTL and previous GWAS results, 41 key functional genes among the 73 differentially expressed proteins were found to be associated with milk production traits.

[0030] The SNPs that are significantly associated with milk production traits in the OTL peak and GWAS results are located close to each other, including AKT3, PKLR, SEC13, ACAT1, ACOX2, ADIPOQ, ALDH18A1, AMDHD2, APOA2, APOB, APOC4, CLINT1, CYP2C18, CYP3A5, CYP7A1, DH1, EHHADH, ETFA, FBP2, GLUL, HADH, HADHB, HSD17B2, IDH2, LDHA, MAT2A, NPL, PCK1, PM20D1, PP4C, PRKACA, SUCLA2, APOP4, APOP5, GYS2, LDHB, NGFR, NR0B2, PC, PPP2R2B, and SLC22A7.

[0031] Further genetic effect analysis was conducted on a population of 947 Chinese Holstein cows from 45 bull families across 22 cattle farms at Beijing Shou Nong Livestock Co., Ltd. Through pooled sequencing of frozen semen DNA, 135 SNP loci were identified, and individual genotyping was performed using genotyping by target sequencing (GBTS). Single-label and haplotype association analyses were conducted on the SNP loci with five milk production traits (milk yield, milk fat content, milk fat percentage, milk protein content, and milk protein percentage) using the SAS software MIXED procedure and an animal model (model shown below).

[0032] y = μ + hys + b × M + G + a + e

[0033] Where y is the phenotypic value of milk production traits (individual milk yield, milk fat content, milk protein content, milk fat percentage, and milk protein percentage over 305 days), μ is the population mean, hys is the year-season utility, b is the regression coefficient of covariate M, M is the calving age effect, G is the genotype / haplotype combination effect, a is the individual random additive genetic effect, and e is the random residual effect.

[0034] According to the results of single marker and haplotype association analysis, all 135 SNP loci were significantly associated with one or more milk production traits (P<0.05, P<0.01), allele substitution effect or additive inheritance effect (P<0.05, P<0.01).

[0035] The inventors' team previously identified 14 candidate genes for milk production traits—DDIT3, RPL23A, SESN2, and NR4A—through transcriptome studies of mammary epithelial tissue from lactating cows with extremely high and low milk fat / milk protein ratios. Based on a Holstein cow population from Beijing Shou Nong Livestock, 1093 cows with complete pedigrees and standardized DHI records were selected as the experimental group. First, using genomic DNA from 40 Holstein bulls (the fathers of the 1093 cows), 3000 bp segments of the coding regions and upstream and downstream regulatory regions of the above four candidate genes were segmented by PCR amplification and sequencing using a pooled genomic DNA sequencing method. Based on the pooled DNA sequencing results, a total of 35 SNP loci were identified. Further individual genotyping was performed using time-of-flight mass spectrometry. Single-marker association analysis showed that all 35 SNP loci were significantly associated with at least one milk production trait (P≤0.0001~0.0493), indicating allele substitution or additive inheritance effects (P<0.05, P<0.01).

[0036] (2) Genome-wide association analysis

[0037] The experimental population consisted of 2,093 cows from 14 bull families at Beijing Shou Nong Livestock Co., Ltd. Estimated breeding value (EBV) was used as the phenotypic value for analysis. Genotyping was performed using the Bovine SNP50K chip. After rigorous quality control, two statistical analysis methods—label-by-label transmission disequilibrium test (L1-TDT) and label-by-label regression analysis (MMRA)—were used to perform association analyses on five milk production traits: milk yield, milk fat content, milk protein content, milk fat percentage, and milk protein percentage. The analysis identified 105 SNP loci at a genomically significant level (P<1.27E-06). Of these, 38 SNP loci were detected by both methods simultaneously, while the remaining 4 and 63 SNP loci were detected individually by L1-TDT and MMRA methods, respectively. L1-TDT and MMRA methods located 20, 9, 21, 65, and 28 SNP loci, respectively.

[0038] A significant SNP locus affects milk yield, milk fat content, milk protein content, milk fat percentage, and milk protein percentage traits.

[0039] The study group consisted of 784 Chinese Holstein cows from 21 bull families across 18 cattle farms at Beijing Shou Nong Livestock Farm.

[0040] Genotyping data was obtained using the Bovine SNP50 chip, and the content of milk fatty acids in milk samples was detected by gas chromatography, yielding phenotypes for 22 milk fatty acid content traits, including lauric acid, myristic acid, palmitic acid, and linoleic acid. First, fixed-effects analysis was performed using the SAS 9.1 generalized linear model procedure to correct for the milk fatty acid phenotypic values. Then, genome-wide association analysis was conducted using the quantitative trait additive effects model in PLINK software (v1.07), identifying 83 SNP loci significantly associated with the 22 milk fatty acid content traits at the genome-wide level (P < 1.23E-06).

[0041] (3) Genome resequencing

[0042] Whole-genome resequencing (10×) was performed on eight Holstein bulls (from four full-sib / half-sib groups: high / low) with extremely high / low EBV (protein and fat percentages) using the Illumina next-generation sequencing platform. A total of 10,961,243 SNP loci were detected by alignment with the bovine reference genome (UMD3.1), of which 57,451 SNP loci shared the same allele direction across the four high / low groups (based on the uniform distribution described later, 4,840 SNP loci were included in the microarray).

[0043] 2. Body type

[0044] Genome-wide genotyping was performed on 1,314 Chinese Holstein cattle from Beijing Shou Nong Livestock using the Bovine SNP50 bovine whole-genome SNP chip. A LASSO model (details below) was used to perform genome-wide association analysis on 29 physical traits (including size, height, forequarters, chest width, body depth, loin strength, rump width, rump angle, bone quality, hoof angle, hind limb lateral view, udder depth, udder quality, central suspensory ligament, anterior chamber attachment, anterior teat position, teat length, posterior attachment height, posterior attachment width, posterior teat position, and angularity), and 8 functional scoring traits (including total score, volume, milk characteristics, rump, limbs and hooves, anterior udder, posterior udder, and lactation system).

[0045] First, the effect size of each SNP was estimated using single-trait mixed model analysis (SMMA). Then, the 500 SNPs with the lowest p-values ​​were selected for LASSO analysis. The SMMA model was y = 1 / μ + x. j β j +Zg+e, where y is the phenotypic value of each body type trait, l is the unit vector, μ is the population mean, and x j It is the genotype of the j-th marker, β j This represents the SNP effect, g is the minor polygenic effect, Z is the correlation matrix of g, and e is the random residual. g ~ N(0, Aσ) g2 ), e~N(0,Iσ e 2 ), where A is an additive genetic correlation matrix based on pedigree, σ g 2 It is the residual variance.

[0046] Secondly, the effect size of SNPs detected by the SMMA model was estimated using the improved LASSO model, which is y = 1μ + Xβ + Zg + e, where X is the genotype covariate matrix of 500 SNPs and β is the SNP effect vector.

[0047] Genome-wide association analysis detected 59 significant SNP loci at the genome level that were significantly associated with 26 physical traits (P<0.01).

[0048] 3. Health characteristics

[0049] (1) Immunoglobulins

[0050] This study used 588 Chinese Holstein cattle from Beijing Shou Nong Livestock Farm as the research subjects. Colostrum, serum, and hair samples were collected within 24 hours after calving. The concentrations of immunoglobulins and albumin in colostrum and serum were detected using ELISA kits, and genotyping was performed using the GeneSeek 150K chip. Based on GCTA software, the heritability estimates of colostrum and serum IgG, IgA, IgM, and albumin concentrations ranged from 0.08 to 0.48. Except for serum IgG1 and IgG2 concentrations, most immunoglobulin concentrations showed medium to high heritability (0.12–0.48). Genome-wide association analysis (GWA) based on a mixed linear model using GCTA software revealed 36 SNP loci at the genome-wide level that were significantly associated with colostrum and serum IgG, IgG2, and IgM concentrations (P < 3.08E-6).

[0051] (2) Susceptibility / resistance to paratuberculosis

[0052] A study population of 945 dairy cows from Beijing Shou Nong Livestock Co., Ltd. was selected. A case-control strategy was used to perform genome-wide association analysis (GWAS) on paratuberculosis susceptibility / resistance. Individual genotyping was performed using the Bovine 50K and GeneSeek 150K microarrays. Phenotypic results were based on serum paratuberculosis antibody OD values ​​(185 positive, 760 negative). GWAS was performed on two datasets (intersection of 50K and 150K microarray data vs. dataset with low-density padding to high-density data) using GRAMMAR-GC and ROADTRIPS software. Both methods detected 14 and 18 significant SNP loci at the genome-wide level, respectively, that were significantly associated with paratuberculosis susceptibility / resistance in Holstein dairy cows (P < 5 × 10⁻⁶). -5 ).

[0053] Example 2: Obtaining the second type of SNP site

[0054] 1. Genetic defects

[0055] Genetic defects in dairy cows mainly include spinal deformity syndrome (CVM), leukocyte adhesion deficiency (BLAD), uridine monophosphate synthase deficiency, and citrullinemia, which can lead to premature abortion in cows, calf mortality, and reduced survival rates, causing significant economic losses to the dairy farming industry. By adding genetic defect gene loci to SNP chips, it is possible to easily, quickly, and accurately identify and screen individual dairy cows for recessive harmful genes, thereby reducing the frequency of harmful genes in the population through early culling or scientific selective breeding, and improving the quality of my country's dairy cow population. Therefore, this chip includes 25 SNP loci, representing 19 common bovine genetic defect causative mutation sites, with locus information sourced from the OMIA database of major bovine genetic defect sites.

[0056] Table 1 Common mutation sites causing bovine genetic defects

[0057]

[0058]

[0059] 2. Paternity test

[0060] In dairy cattle breeding, accurate pedigree records are a crucial foundation for accurately estimating individual breeding values ​​and accelerating population genetic progress. Pedigree errors are common in dairy farms, making paternity testing and pedigree correction using genetic markers of great significance. The International Society for Animal Genetics (ISAG) has researched and recommended 198 SNP loci for bovine paternity testing (to facilitate research and exchange among laboratories worldwide), and therefore these have been added to this microarray.

[0061] The second type of loci are located at SNP loci numbered 5,364–5,586 in Table 2.

[0062] Example 3: Obtaining the third type of SNP site

[0063] A large-scale, high-quality reference population is a crucial foundation and essential step in genomic selection. Since 2008, the inventors' team has constructed my country's only reference population for dairy cow genomic selection, currently numbering 23,000 head. The SNP chips used for genotyping of individuals in this reference population are all imported, including 50K (5,811 heads), 80K (1,535 heads), 100K (5,000 heads), and 150K (10,591 heads). Therefore, this chip needs to have high genotyping accuracy with the chip data of this reference population to accurately assess the genomic composition of the dairy cow population.

[0064] The premise of genomic selection is the tight linkage between markers and QTLs. Therefore, uniform marker distribution across the genome maximizes the capture of QTLs influencing traits, thereby improving the accuracy of genotype inference and genomic assessment. Genotype imputation methods are based on inferred haplotypes (allele combinations linked between adjacent markers on the same chromosome) to fill in missing genotypes. The basic process is as follows: First, haplotypes are constructed using individual, family, and population linkage disequilibrium information with high-density markers; then, the markers in the microarray to be imputed are matched with the markers in the haplotypes to fill the microarray.

[0065] Factors affecting the accuracy of microarray genotype filling include minimum allele frequency, marker density, reference population size, and filling method. Given that the genotype microarray data for the aforementioned reference population includes 50K, 80K, 100K, and 150K SNP microarrays, the inventors, based on my country's large-scale dairy cattle population, selected a subset of SNP loci from existing commercially available 50K, 80K, 100K, and 150K bovine genome microarrays, considering detection rate, gene frequency, location uniqueness, genotype filling principles, and genomic distribution.

[0066] 1. Quality Control

[0067] Based on genotypic data from 23,000 Holstein cattle in China, effective SNP loci suitable for the Chinese Holstein dairy cattle population were selected according to detection rate, minimum allele frequency, and Hardy-Weinberg equilibrium test. Quality control was performed using PLINK software: (1) SNP loci with a detection rate less than 0.95 were removed; (2) SNP loci with a minimum allele frequency less than 0.01 were removed; (3) SNP loci that did not conform to Hardy-Weinberg equilibrium (P-value < 1 × 10⁻⁶) were removed. -6 After quality control, 154,363 SNP sites were obtained after removing duplicates from the common sites of the four groups.

[0068] 2. Distribution of SNP sites

[0069] To ensure that SNP loci uniformly cover the entire bovine genome and capture important trait genetic variations across the entire genome for accurate genomic assessment, a 24Kb sliding window was set on the chromosome to screen the aforementioned locus set. Ultimately, 114,569 type III SNP loci were identified.

[0070] The third type of loci are located at SNPs 5,587-120,155 in Table 2.

[0071] Table 2 List of SNP sites used in the chip

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[0678] The variation information of the SNP loci is represented in the form of chromosome number_physical location reference genotype>allele. In the example, the SNP loci in Table 2 are numbered sequentially by row, i.e., Chr1_1277227A>C is the first, Chr1_3249057G>T is the second, Chr1_8437530T>G is the third, and so on.

[0679] Example 4: Chip Fabrication

[0680] The sets of the three types of SNP loci mentioned above are shown in Table 2, and their distribution on the chromosome is as follows: Figure 2 As shown.

[0681] To ensure that the physical location of SNP sites is unique across the entire genome, the above three types of SNPs are checked and evaluated.

[0682] First, based on the two currently used bovine reference genome sequences (UMD3.1.1 and ARS-UCD1.2), the flanking sequences of 111,991 SNP sites were aligned using the blastn software with the default parameters -evalue 1e-5, -dust yes, and identical>=98%. SNP sites that met the requirements were retained, resulting in a total of 111,991 SNP sites.

[0683] Then, based on the physical location and flanking sequences of the SNP sites on the bovine reference genome, probe design was successfully performed (some examples are shown in SEQ ID NO. 1-13). [Image of physical probes follows.] Figure 3 As shown.

[0684] Table 3 Partial Probe Sequence List

[0685] Target SNP chromosome number physical location Mutation point probe sequence Chr1:41768691G>T Chr1 41768691 G>T SEQ ID NO.1 Chr1:109550832G>A Chr1 109550832 G>A SEQ ID NO.2 Chr1:127318739C>A Chr1 127318739 C>A SEQ ID NO.3 Chr1:144659141A>C Chr1 144659141 A>C SEQ ID NO.4 Chr1:144762379T>G Chr1 144762379 T>G SEQ ID NO.5 Chr1:144833882A>G Chr1 144833882 A>G SEQ ID NO.6 Chr10:49904259G>A Chr10 49904259 G>A SEQ ID NO.7 Chr10:53840143T>A Chr10 53840143 T>A SEQ ID NO.8 Chr10:6988001T>C Chr10 6988001 T>C SEQ ID NO.9 Chr11:103963998C>T Chr11 103963998 C>T SEQ ID NO.10 Chr11:14189221A>G Chr11 14189221 A>G SEQ ID NO.11 Chr11:4671286C>T Chr11 4671286 C>T SEQ ID NO.12 Chr11:506778G>A Chr11 506778 G>A SEQ ID NO.13

[0686] Due to the limitations of sequence listing format, SEQ ID NO.1-13 only represent the original sequences that have not changed. Those skilled in the art can obtain the mutated sequences from the mutation sites and mutation modes recorded in Table 2.

[0687] Example 5: Practical Effects of the Chip in this Application

[0688] To verify the practical effect of this chip, the inventors designed (1) Experiment 1: nearly 5,000 healthy Holstein cows were selected from the dairy farm of Beijing Shou Nong Livestock to test the SNP site detection rate and polymorphism of the 126K chip; (2) Experiment 2: 33 Holstein cows were selected from the dairy farm of Beijing Shou Nong Livestock to test the 126K and 50K chips at the same time, 23 Holstein cows were tested the 126K and GGP 85K chips at the same time, 93 Holstein cows were tested the 126K and GGP 100K chips at the same time, and 93 Holstein cows were tested the 126K and GGP 150K chips at the same time to conduct individual genotype detection rate and comparative verification analysis.

[0689] First, the quality analysis of the microarray genotype data of all dairy cows was performed using PLINK software: (1) Experiment 1: The 126K microarray detection results of 5000 dairy cows showed an SNP detection rate of 98.12% to 99.86%, with an average detection rate of 99.54% and an average locus polymorphism information content of 0.401; (2) Experiment 2: The genotyping consistency of 126K with 50K, 85K, 100K, and 150K microarrays was 98.46%, 98.89%, 99.25%, and 99.39%, respectively. The results show that the 126K microarray has the same detection performance as existing commercial dairy cow microarrays, and exhibits better locus polymorphism, which is more consistent with the genetic background of dairy cows in my country.

[0690] Then, for the validation population in Experiment 2, based on the genome selection reference population (23,000 head) described in Example 3, the genotype data of 126K, 50K, 85K, 100K, and 150K were sequentially filled to the 50K, 85K, 100K, and 150K levels using Beagle 5.2 software, and the filling accuracy of different types of chips was calculated. The results showed that the filling accuracy of the 126K chip was 98.53%, consistent with the performance of existing dairy cow chips (96.11%–99.81%), meeting the needs of genome assessment and gene mining.

Claims

1. A medium-to-high density 126K SNP chip for the whole genome of dairy cows, characterized in that, The bovine whole-genome 126K SNP chip is a liquid-phase probe hybridization chip, which contains probes for detecting the following SNP loci associated with bovine milk production, body size, reproduction, and health traits: The location information of the SNP sites was determined by alignment with the bovine reference genome UMD3.1.

1.

2. The application of the 126K SNP chip of the whole genome of dairy cows according to claim 1 in Holstein dairy cow breeding.

3. The application of the 126K SNP chip of the whole bovine genome as described in claim 1 in the genotyping detection of Holstein bovines.

4. The application of the 126K SNP chip of the whole bovine genome as described in claim 1 in the identification of kinship in Holstein cows.

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