A whole-genome low-density chip for selecting high-yield and longevity traits in dairy cows and its application

Abstract

The present invention discloses a whole-genome low-density chip for the selection of high-yield and long-lived traits in dairy cows and its application. The present invention first discloses a whole-genome low-density chip for the selection of high-yield and long-lived traits in dairy cows, comprising 10,000 SNP sites, with ARS-UCD1.2 as the reference genome, and the information of the 10,000 SNP sites is shown in Table 6. The present invention further discloses the application of the above-mentioned whole-genome low-density chip. The 10,000 SNP sites contained in the whole-genome low-density chip of the present invention are relatively evenly distributed on the chromosomes, covering the whole genome and important trait sites, which can effectively improve the accuracy of longevity and milk production selection, and can be applied to gene screening, gene positioning, marker-assisted breeding and other directions. In addition, the detection cost of the low-density chip of the present invention is greatly reduced, and it is more suitable for use in commercial dairy farms, which is of great significance to dairy cow breeding and dairy cow population improvement.

Description

Technical Field

[0001] The present invention relates to the fields of molecular biology and biochips, and more specifically to a whole-genome low-density chip for selecting high-yield and longevity traits in dairy cows and its application. Background Art

[0002] Since the concept of genomic selection was proposed in 2001, it has become a routine breeding tool in dairy cattle. Genomic selection utilizes genome-wide markers to identify linkage disequilibrium between markers and trait-related QTLs, enabling early selection of offspring.

[0003] Implementation of genomic selection requires access to a genomic breeding array for the corresponding variety. To identify genetic markers within the array, it is crucial to first identify genetic loci associated with the target trait. Common methods for uncovering trait-associated loci include genome-wide association studies (GWAS) and the fixation index (Fst).

[0004] Genome-wide association analysis (GSA) involves conducting large, repeatedly validated studies of gene-phenotype associations at the genome-wide level. By genotyping large population DNA samples with high-density genetic markers (such as SNPs or CNVs) across the genome, the study aims to identify genetic factors associated with complex traits, comprehensively revealing the genetic genes associated with phenotypes. Single nucleotide polymorphisms (SNPs) primarily refer to DNA sequence polymorphisms caused by variations in a single nucleotide at the genomic level.

[0005] The interpopulation genetic differentiation index is a measure of population differentiation and genetic distance. A higher differentiation index indicates greater dissimilarity. It is suitable for comparing diversity between subpopulations. It measures the degree of population differentiation, ranging from 0 to 1. A value of 0 indicates random mating and complete genotypic similarity between the two populations, while a value of 1 indicates complete isolation and complete dissimilarity. It is often estimated from genetic diversity, such as single-nucleotide polymorphisms (SNPs). It is a statistical method in population genetics based on the Harwin-Wenzhou equilibrium.

[0006] Longevity refers to the overall productive lifespan of a dairy cow and is used to quantify its potential to generate profit. Longevity is often measured using various indicators, such as productive lifespan, which refers to the time from first calving to death or culling. While productive lifespan does not affect yield per unit, increasing it can help reduce passive culling and cow purchase costs, and can also increase lifetime milk production, making it a key economic indicator for ranch production and management. For example, parity is used to indicate the length of time a cow spends in the herd and the duration of its milk production. Production performance primarily includes milk yield, milk fat percentage, and milk protein percentage. This trait is controlled by a large number of polygenic genes with minimal effects and is influenced by multiple genetic and environmental factors, making it a quantitatively inherited trait. Traditional phenotypic-based breeding methods struggle to achieve significant results quickly. Furthermore, due to the presence of polygenic genes with minimal effects, breeding methods using molecular-assisted selection markers have also yielded less than ideal results.

[0007] The longevity and milk production of dairy cows are usually formed by the superposition of genetic and environmental factors. Therefore, under the condition that environmental factors are relatively fixed, it is necessary to analyze and explore the genetic information such as genetic loci and genes that affect high-yield and long-lived dairy cows. Developing breeding chips to identify high-yield and long-lived dairy cows is of great significance for dairy cow breeding. Summary of the Invention

[0008] One purpose of the present invention is to provide a whole genome low-density chip for the selection of high-yield and long-lived traits in dairy cows, so as to improve the accuracy of evaluating traits such as longevity and milk production of the herd and increase efficiency.

[0009] Another object of the present invention is to provide an application of the above-mentioned whole genome low-density chip.

[0010] In order to achieve the above object, the present invention adopts the following technical solutions:

[0011] The present invention first provides a whole-genome low-density chip for selecting high-yield and longevity traits in dairy cows. The whole-genome low-density chip contains 10,000 SNP sites, using ARS-UCD1.2 as the reference genome. The information of the 10,000 SNP sites is shown in Table 6.

[0012] Furthermore, the SNP site is associated with the longevity and milk production traits of dairy cows.

[0013] The present invention further provides the use of the above-mentioned cow whole genome low-density chip in any of the following applications:

[0014] 1) Application in dairy cattle breeding;

[0015] 2) Application in genome-wide association analysis of dairy cows;

[0016] 3) Application in the selection of longevity traits in dairy cows;

[0017] 4) Application in the breeding of milk production traits in dairy cows.

[0018] In the present invention, the longevity is measured by the number of parities. Individuals with more than 5 parities are defined as long-lived or high-reproduction individuals, and individuals with less than 3 parities are defined as non-long-lived or low-reproduction individuals. The milk production is measured by the mean of the DHI data, with an average milk production of 40 kg as the threshold. Cows with a milk production above 40 kg are defined as high-producing cows, and cows with a milk production below 40 kg are defined as low-producing cows.

[0019] The present invention further provides a method for preparing a whole-genome low-density chip for selecting high-yield and long-lived traits in dairy cows. The preparation method comprises forming the above-mentioned 10,000 SNP sites into a low-density chip.

[0020] In the present invention, the dairy cow is a Holstein cow.

[0021] The beneficial effects of the present invention are as follows:

[0022] The present invention's genome-wide low-density microarray for high-yield and longevity selection in dairy cows contains 10,000 SNPs, which are evenly distributed across chromosomes, covering the entire genome and encompassing key trait loci. This effectively improves the accuracy of selection for longevity and milk production, and is applicable to gene screening, gene mapping, marker-assisted breeding, and other areas. Furthermore, the present invention's low-density microarray significantly reduces detection costs, making it more suitable for use in commercial dairy farms, and has significant implications for dairy cow breeding and herd improvement. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] The specific embodiments of the present invention will be further described in detail below with reference to the accompanying drawings.

[0024] Figure 1 Customize the flow chart for the probe.

[0025] Figure 2 is the site-chromosome distribution map, where the horizontal axis is the chromosome and the vertical axis is the number of sites.

[0026] Figure 3 The density map of marker distribution on chromosomes.

[0027] Figure 4 The MAF distribution diagram shows that the horizontal axis MAF represents the different ranges of MAF values, and the vertical axis Number represents the number of core sites within the MAF range. The higher the MAF value of the target site, the better the polymorphism of the site.

[0028] Figure 5 This is a statistical chart of SNP variation types.

[0029] Figure 6It is a functional site distribution map, where the horizontal axis Type is the gene structure type and the vertical axis Number is the number of target sites under the relevant type.

[0030] Figure 7 The PCA visualization results are shown in Figure 2. The upper figure is the PCA obtained based on the original genotype of the population, and the lower figure is the PCA obtained based on the chip SNP sites.

[0031] Figure 8 A graph showing the predictive effects of different machine learning methods on milk production (A) and longevity (B) for the 10K chip. DETAILED DESCRIPTION

[0032] In order to more clearly illustrate the present invention, the present invention is further described below in conjunction with preferred embodiments and accompanying drawings. Similar components in the accompanying drawings are represented by the same reference numerals. It should be understood by those skilled in the art that the following detailed description is illustrative rather than restrictive and should not be used to limit the scope of protection of the present invention.

[0033] Saikexing currently has 6,932 long-lived cattle with ≥5 parities in its herd, accounting for 4.54% of the herd size. This study selected 600 of 963 cattle (from Saikexing's eight ranches) born after August 7, 2004, with clear three-generation pedigrees and 5 to 11 parities, which are representative. In addition, the DHI of the above cattle began to be measured on July 13, 2016, which was a relatively early measurement time. After data cleaning, the cumulative number of tested cattle was 59,300, and the large amount of data ensured the accuracy of phenotypic data and genomic selection. Therefore, Saikexing has the conditions to carry out genome-wide association analysis on dairy cows, explore functional sites, develop breeding chips, and improve the accuracy of longevity and milk production traits in breeding to improve efficiency.

[0034] Example 1 Design and preparation of a low-density whole-genome microarray for dairy cows

[0035] 1. Sample Collection

[0036] Samples were collected from 600 Chinese Holstein cows across eight Saikexing dairy farms, with both phenotypic and genotypic data available for all individuals. The phenotypic data were divided into DHI data and dairy farm management data.

[0037] 2. Genome Resequencing

[0038] 1. Genomic DNA extraction (magnetic bead method)

[0039] Preparation for DNA extraction: Dissolve Proteinase K in Proteinase K Storage Buffer to prepare 20 mg / ml Proteinase K solution and store at -20°C until use.

[0040] Extraction steps:

[0041] (1) Add the corresponding reagents to the 96DW deep-well plate according to Table 1:

[0042] Table 1 DNA pre-reagent addition position

[0043]

[0044] (2) Add blood samples to the corresponding wells of Plate 1, with a sample volume of 300 μL / sample.

[0045] (3) Insert the spin tips pack into the 96DW deep well plate.

[0046] (4) Run the blood DNA extraction program. According to the instrument program flow, place the deep-well plate with the magnetic rod cover and the deep-well plate with reagents into the designated positions of the instrument.

[0047] (5) Obtain genomic DNA (gDNA).

[0048] 2. Genomic DNA quality inspection

[0049] (1) DNA integrity detection: Take the gDNA to be detected, spot it on 1.5% agarose gel, and perform electrophoresis at 150V for 25 minutes.

[0050] (2) DNA purity detection: The purity of DNA was detected using NanoDrop 2000 nucleic acid protein analyzer. The A260 / A280 ratio was in the range of 1.7-2.1, and the A260 / A230 ratio was in the range of 1.8-2.2, which met the requirements for library construction.

[0051] (3) Quantification of DNA concentration: gDNA was quantified using the dsDNA HS Assay Kit for Qubit.

[0052] 3. Library construction

[0053] The specific steps of library construction are as follows: Detect qualified genomic DNA and use a universal library construction kit (forMGI) To build the library, DNA was crushed by Covaris TM Randomly interrupted, end-repaired, A-tailed, and connected to sequencing adapters, SP Beads screened out fragments of about 300-350 bp, amplified them by PCR, and used SPBeads were used to purify the PCR products and finally obtain the sequencing library.

[0054] 4. Hybridization capture

[0055] After quantification by Qubit2.0 and (Agilent) detects the insert fragments of the sequencing library. Library hybridization: After the library is qualified, a biotin-labeled probe is specifically bound to the DNA library that has been attached with an adapter sequence. The hybridization system consists of a probe, hybridization buffer, blocking reagent, and DNA library. The entire hybridization reaction is performed on a PCR instrument, requiring precise temperature control to prevent evaporation of the system. Hybridization capture: After the hybridization reaction is completed, streptavidin magnetic beads are added to the reaction system for hybridization capture. The streptavidin magnetic beads adsorb the double-stranded complex formed by the biotin-labeled probe and DNA, and the magnetic beads are adsorbed using a magnetic stand to obtain a hybridization capture library. Library product elution: After capture is completed, the hybridization capture library is eluted with an elution buffer to remove non-specific hybridization. High temperature, low salt environment treatment: In a high temperature, low salt environment, a large number of non-specific hybridization complexes cannot exist stably and can be removed through multiple washing. Capture product enrichment: The eluted product is amplified and released from the library through post-PCR. The number of amplification cycles is reasonably adjusted according to the size of the liquid phase probe.

[0056] 5. Sequencing

[0057] After the library is qualified, pooling is performed based on the effective concentration of the library and the target data volume. Sequencing is performed on the DNBSEQ-T7 sequencer with the PE150 sequencing strategy.

[0058] The sequencing process utilizes DNA nanoball (DNB) core sequencing technology. First, DNA anchors and fluorescent probes are polymerized on the nanoballs. A high-resolution imaging system then collects the light signal, which is then digitized to produce the sequence to be tested. The raw image data generated by sequencing is converted into raw sequence data (raw reads) using base calling software and stored in the FASTQ file format.

[0059] 3. Trait-associated site mining

[0060] 1. Low-depth sequencing fill

[0061] The low-depth sequencing population was filled to a high-depth level. Specifically, 1) a filling reference panel was constructed. Cattle sequencing data was downloaded from a public database. At the same time, the cattle in this population were sequenced at a high depth. The genotype data obtained after SNP calling was used to construct a filling reference panel, resulting in a total of 45,008,371 SNPs; 2) the sequencing data of the low-depth sequencing population was aligned and quality controlled to obtain a reads file (bam) format, and the low-depth sequencing population was filled to a high-depth level using GLIMPSE2 software. 3) The filled population was quality controlled, and sites with MAF < 0.01 and significant deviation from the Hartmann-Wenriched equilibrium were eliminated, leaving 9,927,289 SNPs for subsequent analysis.

[0062] 2. Site screening

[0063] The traits tested in this study are divided into two categories: longevity and milk production. Regarding the definition of traits, longevity is measured by parity. Cows with more than five parities are considered long-lived or high-fertility, while those with fewer than three parities are considered non-long-lived or low-fertility. Milk production is measured by the mean DHI data, with an average milk production of 40kg as the threshold. Cows with a milk production above 40kg are considered high-yielding, while cows with a milk production below 40kg are considered low-yielding.

[0064] 2.1. GWAS analysis of binary traits

[0065] GEMMA software was used to perform genome-wide association analysis based on a logit regression model for binary traits. The phenotype yi of individual i was defined as yi = 1 if individual i was a case, and yi = 1 if individual i was a control. The model for the kth variant was constructed as follows:

[0066] Logit(π)=Xα+G k β+g,

[0067] Where, π=P(y=1|X,G k , g) is a given covariate (X) and genotype (G k ) and the random effect g, the probability vector of an individual being defined as high-yielding (i.e., average milk production greater than 40 kg) or long-lived (more than 5 parities) is used.

[0068] 2.2. Mining of population differentiation sites

[0069] In population differentiation, the Fst method is often used to identify regions or loci of population divergence. This method uses the Weir and Cockerham method in vcftools to estimate the Fst score of genetic markers and calculate the fixation index (Fst) to prioritize SNPs. Fst values ​​range from 0 to 1, with larger values ​​indicating greater differentiation between populations. An Fst index of 1 indicates that the genetic variant is fully differentiated within each population; an Fst index less than 0 has no specific biological explanation and can be ignored; an F-index of 0 to 0.05 indicates minimal differentiation between populations; an F-index of 0.05 to 0.25 indicates moderate differentiation between populations; and an F-index greater than 0.25 indicates significant differentiation between populations.

[0070] Based on the above GWAS and population differentiation analysis, 1495 loci and 2889 SNPs related to milk production and longevity were obtained.

[0071] 4. Chip Design

[0072] Based on the trait-associated loci obtained from the above loci mining, genetic loci in different regions of the genome (see 1 to 7 below) were further supplemented according to the following genetic locus selection principles to achieve the 10K chip locus level. The detailed technical points are as follows:

[0073] 1. Site screening principles

[0074] For candidate sites to be added to the chip, that is, the genetic sites on the genome that need to be added to the 10K chip, screening is performed considering their genomic characteristics:

[0075] (1) Covering relevant sites, such as QTL, GWAS, domestication-related genes, interspecific and intraspecific subpopulation differences, excellent traits, terminators, alternative splicing, non-synonymous mutations, etc.

[0076] (2) High polymorphism (MAF ≥ 0.05 by default).

[0077] (3) Uniform coverage of the entire genome.

[0078] (4) Multiple site selection: Generally, 2-4 times the target site is selected as the candidate site set.

[0079] (5) The design system scores to determine the final chip customization site.

[0080] 2. Site selection priority

[0081] Based on the key points in "1. Site Screening Principles," we preliminarily screened the genetic loci on the genome for use in the 10K array. Because multiple candidate loci may exist in a given region, we prioritized their functional importance, ranking the candidate loci based on the previous screening principles. We then selected the loci to ensure a uniform distribution across the genome for the 10K array:

[0082] (1) Priority 1 = VIP loci, QTL loci, GWAS loci, important genes, common genes, etc.;

[0083] (2) Priority 2 = terminators on annotated genes (select all, but refer to MAF values) / variable splicing (select all, but refer to MAF values) / non-synonymous mutation sites (select all, but refer to MAF values), etc.;

[0084] (3) Priority 3 = whole genome coverage sites.

[0085] Based on the above importance ranking principles, sites in a region are selected to determine candidate sites in the chip.

[0086] 3. Probe customization process

[0087] Probe customization flow chart as follows Figure 1 shown.

[0088] 4. Chip customization information is shown in Table 2.

[0089] Table 2 Chip customization information

[0090]

[0091] 5. Site quality control

[0092] Based on the strategy described in steps 1-4, genomic variants in the low-depth sequencing population were typed and verified, and data quality control was performed to obtain accurate and high-quality variant sites. The quality control details are shown in Table 3 (i.e., the 9,927,289 SNP sites after filling and quality control in step 3-1 of low-depth sequencing), including read coverage depth, deletion rate, kinship detection, minimum allele frequency (MAF), Hardy-Weinberg equilibrium, and outlier samples. After removing low-quality samples and sites, 370 samples remained (as shown in Table 4), and the 9,927,289 SNP sites in the genome served as candidate sites for subsequent array selection.

[0093] Table 3 Quality control parameters and thresholds

[0094]

[0095]

[0096] Table 4 Sample details

[0097] Varieties and strains Original number of samples Number of remaining samples Group 1 600 370 total 600 370

[0098] 6. Site Scoring

[0099] The candidate loci are submitted to a dedicated probe design system for scoring. The optimal probe is determined based on the evaluation of upstream and downstream sequences of the target locus. The designed probe is then used for subsequent sequencing, targeting the 10K microarray loci for the target sequenced individuals. The system primarily assesses the specificity, complexity, and GC content of the upstream and downstream sequences of the target locus. Priority is given to placing the target locus in the middle of the probe, and the designed probe is 120 bp in length.

[0100] Based on the scoring results, a total of 20K high-quality custom-made microarray sites were selected as the final target microarray site set, known as the 1x site set. From this 1x site set (20K), sites were selected through site evaluation and repeated testing to obtain the optimal 10K site set, known as the 10K microarray.

[0101] 7. Site Assessment

[0102] From the 1x locus set (20K), a set of loci (10K loci) was selected for the proposed microarray. This set of loci (10K loci) was then evaluated, focusing on metrics such as locus set ranking, chromosome distribution, locus density, MAF distribution, mutation spectrum analysis, marker functional locus distribution statistics, and PCA population structure for the final microarray product. The locus evaluation results demonstrated uniform genome-wide coverage of loci, encompassing loci for important traits, and are therefore suitable for applications in gene screening, gene mapping, and marker-assisted breeding.

[0103] The specific method is as follows:

[0104] 7.1 Site set ranking

[0105] Based on the distribution of the functional regions of the screened loci across the genome, the loci were ranked and counted according to their importance or priority. See Table 5 for details.

[0106] Table 5 Site set ranking table

[0107] Tiling order Number of sites Proportion 1 2272 22.72％ 2 3897 38.97％ 3 3831 38.31％ SUM 10000 100％

[0108] Tiling order 1 = VIP, QTL loci, GWAS loci, important genes, common genes, etc.;

[0109] Tiling order 2 = annotation of promoter, terminator, alternative splicing, etc.

[0110] Tiling order 3 = whole genome coverage of sites.

[0111] 7.2 Locus chromosome distribution

[0112] The selected points were plotted using the R package, and the results are as follows Figure 2 From the figure, we can see the priority distribution of mutation sites on each chromosome. Therefore, the distribution of priority sites on the chromosome of this chip is relatively uniform, and the coverage is good.

[0113] 7.3 Site density distribution

[0114] Figure 3 This is the distribution density map of the chip loci set on each chromosome. From the figure, we can see that the loci of each chromosome are distributed relatively evenly, indicating that the loci contained in the chip can well cover each chromosome.

[0115] 7.4 MAF Distribution

[0116] MAF is the minimum allele frequency, which usually refers to the frequency of uncommon alleles in a given population. For example, for the three genotypes TT, TC, and CC, the frequency of C in the population is 0.36, and the frequency of T is 0.64. Then allele C is the minimum allele, and the minimum allele frequency MAF is 0.36. Figure 4 This is the MAF distribution diagram. It can be seen from the figure that the sites with the minimum allele frequency > 0.1 account for 99.99%.

[0117] 7.5 Mutation Spectrum Analysis

[0118] Base transition refers to the replacement between purine and purine, or between pyrimidine and pyrimidine; base transversion refers to the replacement between purine and pyrimidine.

[0119] Due to base structure, the probability of transitions is generally higher than that of transversions. After detecting and filtering high-confidence SNPs, we perform statistical analysis on the SNP mutation spectrum. Point mutations include six types: A / T, A / C, A / G, G / C, G / T, and G / A. For example, A / C indicates a mutation from A to C on a single strand. By classifying and statistically analyzing the mutation results of all samples, we can see the percentage of each mutation type in each sample. Figure 5 This is a statistical chart of SNP variation types. It can be seen from the chart that A / G and C / T mutation types account for a high proportion, and other variation types are similar.

[0120] 7.6 Distribution statistics of marker functional sites

[0121] ANNOVAR software was used to annotate the site set (10K sites) for the planned custom chip, and the 10K sites were aligned to the corresponding regions on the genome. Figure 6 This is a functional site distribution map. It can be seen from the map that the screened sites have more non-synonymous mutations, SNPs in intergenic regions and introns.

[0122] 7.7PCA Population Structure

[0123] PCA analysis graphically displays the clustering of individuals within a population, providing a general understanding of the population's genetic structure. On a PCA graph, individuals with closer linear distances generally have closer genetic relationships, while individuals with greater linear distances generally have more distant genetic relationships. This approach is particularly effective for identifying varieties using microarrays containing varietal identification loci. Figure 7 The PCA diagram shows that the sample clustering of the PCA obtained from the original genotype of the population and the PCA obtained from the chip SNP sites are consistent, indicating that the chip can better represent the original status of the population.

[0124] 8. Chip design results

[0125] Based on the 600 samples provided, a total of 10K SNP loci were finally obtained through chip design condition screening and uniform distribution considerations (specific SNP loci information is shown in Table 6). The 10K chip was successfully designed for the selection of longevity and milk production traits in dairy cows.

[0126] Table 6 SNP loci in the microarray for selecting longevity and milk production traits in dairy cows

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[0165] Example 2 Chip Effect Verification Test

[0166] A total of 600 cattle were tested to verify the effectiveness of the 10K chip obtained in Example 1. By repeating the 5-fold cross-validation strategy 20 times, the phenotype was predicted using a machine learning method to verify the application effect of the 10K chip.

[0167] This test uses four machine learning methods: support vector machine (SVM), random forest (RF), k-nearest neighbor (KNN) and naive Bayes (NB) machine learning methods to predict milk production and longevity traits. Longevity is measured by parity. Those with more than 5 parities are defined as long-lived, and those with less than 3 parities are defined as non-long-lived. Milk production is measured by the mean of DHI data, with an average milk production of 40kg as the threshold. Those with more than 40kg are defined as high-yielding cows, and those with less than 40kg are defined as low-yielding cows. Therefore, milk production and longevity are predicted as binary traits, and the prediction accuracy measurement indicator is expressed as the proportion of cows that are accurately judged as high-yielding or low-yielding, long-lived or non-long-lived, that is, TP / (TP+FP). TP is the number of accurately classified cows, and FP is the number of incorrectly classified cows. The results are shown in Tables 7 and Figure 8 , indicating that the 10K chip obtained in Example 1 has a good predictive effect on milk production and longevity traits.

[0168] Table 7 Prediction effect of 10K chip on milk production and longevity

[0169]

[0170] Obviously, the above embodiments of the present invention are merely examples for clearly illustrating the present invention, and are not limitations on the implementation methods of the present invention. For ordinary technicians in the relevant field, other different forms of changes or modifications can be made based on the above description. It is impossible to list all the implementation methods here. All obvious changes or modifications derived from the technical solution of the present invention are still within the scope of protection of the present invention.

Claims

1. A whole-genome low-density chip for the selection of longevity and milk production traits in dairy cows, characterized by: The whole genome low-density chip consists of 10,000 SNP sites, with ARS-UCD1.2 as the reference genome. The information of the 10,000 SNP sites is as follows: The longevity is measured by parity, with parity exceeding 5 defined as longevity and parity below 3 defined as non-longevity. The milk production is measured by the mean of DHI data, with an average milk production of 40 kg as the threshold. Cows with an average milk production of more than 40 kg are defined as high-producing cows, and cows with an average milk production of less than 40 kg are defined as low-producing cows; The dairy cows are Holstein cows.

2. The whole genome low-density chip according to claim 1, characterized in that The SNP site is associated with the longevity and milk production traits of dairy cows.

3. Use of the whole genome low-density chip according to any one of claims 1-2 in the selection of longevity traits in dairy cows, characterized in that: The longevity is measured by parity, with parity exceeding 5 defined as longevity and parity below 3 defined as non-longevity. The dairy cows are Holstein cows.

4. Use of the whole genome low-density chip according to any one of claims 1-2 in the selection of milk production traits in dairy cows, characterized in that: The milk production is measured by the mean of DHI data, with an average milk production of 40 kg as the threshold. Cows with an average milk production of more than 40 kg are defined as high-producing cows, and cows with an average milk production of less than 40 kg are defined as low-producing cows; The dairy cows are Holstein cows.

5. The method for preparing a whole genome low-density chip according to any one of claims 1-2, characterized in that: The preparation method is to form a low-density chip with the 10,000 SNP sites described in any one of claims 1-2, and the dairy cow is a Holstein cow.

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