Milk goat breeding chip and application thereof

Abstract

The invention provides a milk goat breeding chip and application thereof, and relates to the technical field of animal molecular breeding. The milk goat breeding chip disclosed by the invention comprises 81107 SNP (Single Nucleotide Polymorphism) loci which are positioned in a milk goat reference genome version of GCA015443085.1. The chip can be applied to milk goat genetic typing and population structure analysis, molecular marker-assisted breeding, character correlation analysis, genetic map construction and gene localization, genetic relationship identification, breeding resource gene analysis, genetic diversity evaluation, whole genome correlation analysis and whole genome linkage analysis. The method has the characteristics of large detection flux, high marker coverage rate, high genotype detection rate, high genotype accuracy and good stability.

Description

Technical Field

[0001] The invention belongs to the technical field of animal molecular breeding, and in particular relates to a dairy goat breeding chip and application thereof. Background Art

[0002] The current market demand for goat milk is strong, but the development of dairy goat farming is relatively slow, with low scale and facility levels. The fundamental problem restricting the development of the industry is low breeding efficiency, low breeding rate of qualified offspring, short average utilization period and high management difficulty. In addition, compared with other livestock, the genetic improvement of dairy goats is still in its early stages. Therefore, innovating molecular breeding technology, accelerating the progress of genetic improvement, and speeding up the breeding of fine breeds are of great significance to the sustainable development of dairy goat farming.

[0003] Analyzing the genetic variation of resource populations is the basic work for the protection, selection and breeding of livestock breeds. The analysis technology of genetic variation has gradually deepened from morphological, cytological and biochemical markers to the current DNA molecular markers based on DNA polymorphism. The most commonly used genetic marker at present is the SNP marker. A single nucleotide variation in the genome can cause polymorphism in the DNA sequence. In the mammalian genome, there is about 1 SNP in 500 to 1000 bases. The SNP chip is a biotechnology tool that can quickly and scalably screen genetic variation sites. It is a high-throughput gene detection platform that can efficiently detect thousands of SNPs in one experiment.

[0004] Nowadays, genomic selection (GS) and genome-wide association analysis (GWAS) have been widely used in the genetic improvement of important economic traits of livestock such as cattle and pigs. Among them, how to obtain the genotype data of SNP loci efficiently and conveniently at a low cost is the premise and basis for the effective implementation of genomic selection. Therefore, it is an urgent problem for those skilled in the art to develop a liquid-phase SNP chip for dairy goat genomic breeding to accelerate the progress of genetic improvement of dairy goats. Summary of the invention

[0005] In view of this, the object of the present invention is to provide a dairy goat breeding chip and its application, which has the characteristics of large detection flux, high marker coverage, high genotype detection rate, high genotype accuracy and good stability.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0007] The present invention provides a dairy goat breeding chip, which comprises 81107 SNP sites located in the dairy goat reference genome version GCA_015443085.1, and the position information of the SNP sites is shown in Table 5.

[0008] The invention also provides application of the breeding chip in dairy goat genotyping and population genetic structure analysis.

[0009] The present invention also provides application of the breeding chip in molecular marker-assisted breeding of dairy goats.

[0010] The present invention also provides application of the breeding chip in dairy goat trait association analysis.

[0011] The invention also provides the application of the breeding chip in the construction of dairy goat genetic map and gene positioning.

[0012] The invention also provides application of the breeding chip in identification of kinship of dairy goats.

[0013] The present invention also provides application of the breeding chip in gene analysis and / or screening of dairy goat breeding resources.

[0014] The present invention also provides application of the breeding chip in genetic diversity assessment of dairy goats.

[0015] The present invention also provides application of the breeding chip in whole genome association analysis of dairy goats.

[0016] The invention also provides application of the breeding chip in whole genome linkage analysis of dairy goats.

[0017] Compared with the prior art, the present invention has the following beneficial effects:

[0018] The present invention develops an 80K dairy goat liquid phase breeding chip for the first time, which has the characteristics of large detection flux, high marker coverage, genotype detection rate of more than 99%, high genotype accuracy and good stability, and can be used for different dairy goat breed detection. The chip is used for whole genome association analysis of phenotypic data and genotypic data of goat groups from different sample sources. The analysis results show that the chip can be applied to dairy goat genotyping and population genetic structure analysis, molecular marker-assisted breeding, trait association analysis, genetic map construction and gene positioning, kinship identification, breeding resource gene analysis, genetic diversity assessment, whole genome association analysis and whole genome linkage analysis; it is conducive to accelerating the progress of molecular genetic improvement of dairy goats. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 This is a schematic diagram of the process of constructing a library for the whole genome of the dairy goat population in Example 1.

[0020] Figure 2 This is the basic quality distribution diagram of the sequencing data generated by FastQC in Example 1.

[0021] Figure 3 This is the phylogenetic tree result diagram in Example 1.

[0022] Figure 4 This is the PCA cluster analysis diagram in Example 1.

[0023] Figure 5 It is the cross-validation error value result at different K value levels in Example 1.

[0024] Figure 6 This is a diagram of the genetic structure of the population in Example 1.

[0025] Figure 7 This is the Manhattan plot of the body height trait in Example 1.

[0026] Figure 8 This is the Manhattan plot of the body oblong trait in Example 1.

[0027] Fig. 9 This is the Manhattan plot of the chest trait in Example 1.

[0028] Fig.10 This is the Manhattan plot of the pipe circumference properties in Example 1.

[0029] Fig.11 This is the Manhattan plot of body weight in Example 1.

[0030] Fig.12 This is the Manhattan plot of the chest width trait in Example 1.

[0031] Fig.13 This is the Manhattan plot of the breast depth trait in Example 1.

[0032] Fig.14 This is the Manhattan plot of the waist width trait in Example 1.

[0033] Fig.15 This is the Manhattan plot of the cross-section high trait in Example 1.

[0034] Fig.16 This is the QQ diagram of the body height trait in Example 1.

[0035] Fig.17 This is the QQ diagram of the tube circumference properties in Example 1.

[0036] Fig.18 This is the QQ diagram of the body oblique elongation trait in Example 1.

[0037] Fig.19 This is the weight QQ diagram in Example 1.

[0038] Fig. 20 This is the QQ diagram of the chest circumference trait in Example 1.

[0039] Fig.21 This is the QQ diagram of the waist width trait in Example 1.

[0040] Fig. 22 This is the cross-section high trait QQ diagram in Example 1.

[0041] Fig.23 This is the QQ diagram of the breast depth trait in Example 1.

[0042] Fig.24 This is the QQ diagram of the chest width trait in Example 1.

[0043] Fig.25 A in FIG. 1 is a Manhattan plot of loci significantly associated with milk production phenotype in Example 1, Fig.25 B in FIG. 1 is a QQ plot significantly associated with the milk production phenotype in Example 1.

[0044] Fig.26 A in FIG. 1 is the Manhattan plot of the milk fat rate trait in Example 1, Fig.26 B in the figure is the QQ plot of the milk fat rate trait in Example 1.

[0045] Fig. 27 A in is the Manhattan plot of the milk protein rate trait in Example 1, Fig. 27 B in the figure is the QQ plot of the milk protein rate trait in Example 1.

[0046] Fig.28 A in is the Manhattan plot of the lactose rate trait in Example 1, Fig.28 B in FIG. 1 is a QQ plot of the lactose rate trait in Example 1.

[0047] Fig.29 A in is the Manhattan plot of the ash content property in Example 1, Fig.29 B in FIG. 1 is a QQ diagram of the ash content properties in Example 1.

[0048] Fig.30 A in is the Manhattan plot of the total dry matter ratio trait in Example 1, Fig.30 B in FIG. 1 is a QQ diagram of the total dry matter ratio trait in Example 1.

[0049] Fig.31 A in FIG. 1 is the Manhattan plot of the somatic cell number trait in Example 1, Fig.31 B in FIG. 1 is the QQ plot of the somatic cell number trait in Example 1.

[0050] Fig.32 Schematic diagram of the distribution of SNP sites in each chromosome of the genome in Example 1.

[0051] Fig.33 is the SNPs site data quality control result in Example 2; wherein, Fig.33 a in the figure is a statistical graph of the deletion rate of a single site. Fig.33 b in the figure is the statistical chart of missing rate of genotype individuals. Fig.33 The c in is the minimum allele frequency statistic, Fig.33 The d in the figure is the Haven-Berg P value histogram.

[0052] Fig.34 This is the sample PCA population structure diagram in Example 2.

[0053] Fig.35 This is a schematic diagram of the phylogenetic tree of 20 one-year-old dairy goat rams in Example 2.

[0054] Fig.36 This is the LD attenuation analysis result in Example 2.

[0055] Fig.37 This is a heat map of the kinship matrix of the sample group of 20 one-year-old dairy goats in Example 2.

[0056] Fig.38 This is the Manhattan plot of body weight of 20 one-year-old dairy goat ram samples from the whole genome association analysis in Example 2.

[0057] Fig.39 This is the body weight QQ plot of the whole genome association analysis of 20 one-year-old dairy goat ram samples in Example 2.

[0058] Fig.40 This is the Manhattan plot of the whole genome association analysis of 20 one-year-old dairy goat ram samples in Example 2.

[0059] Fig.41 This is the height QQ plot of the whole genome association analysis of 20 one-year-old dairy goat ram samples in Example 2.

[0060] Fig.42 This is the oblique long Manhattan plot of the whole genome association analysis of 20 one-year-old dairy goat ram samples in Example 2.

[0061] Fig.43 This is the body length QQ plot of the whole genome association analysis of 20 one-year-old dairy goat ram samples in Example 2.

[0062] Fig.44 This is the Manhattan plot of chest circumference of the whole genome association analysis of 20 one-year-old dairy goat ram samples in Example 2.

[0063] Fig.45 This is the chest circumference QQ plot of the whole genome association analysis of 20 one-year-old dairy goat ram samples in Example 2.

[0064] Fig.46 This is the Manhattan plot of chest depth of the whole genome association analysis of 20 one-year-old dairy goat ram samples in Example 2.

[0065] Fig.47 This is the chest depth QQ plot of the whole genome association analysis of 20 one-year-old dairy goat ram samples in Example 2.

[0066] Fig.48 This is the Manhattan plot of chest width of 20 one-year-old dairy goat ram samples in the whole genome association analysis in Example 2.

[0067] Fig.49 This is the chest width QQ plot of the whole genome association analysis of 20 one-year-old dairy goat ram samples in Example 2.

[0068] Fig.50 This is the Manhattan plot of rump width of the whole genome association analysis of 20 one-year-old dairy goat ram samples in Example 2.

[0069] Fig.51 This is the rump width QQ plot of the whole genome association analysis of 20 one-year-old dairy goat ram samples in Example 2.

[0070] Fig.52 This is the Manhattan plot of buttock length of 20 one-year-old dairy goat ram samples from the whole genome association analysis in Example 2.

[0071] Fig.53 This is the QQ plot of rump length from the whole genome association analysis of 20 one-year-old dairy goat ram samples in Example 2.

[0072] Fig.54 This is the Manhattan plot of waist circumference of the whole genome association analysis of 20 one-year-old dairy goat ram samples in Example 2.

[0073] Fig.55 This is the abdominal circumference QQ plot of the whole genome association analysis of 20 one-year-old dairy goat ram samples in Example 2.

[0074] Fig.56 This is the Manhattan plot of waist and horn width in the whole genome association analysis of 20 one-year-old dairy goat ram samples in Example 2.

[0075] Fig.57 This is the QQ plot of waist horn width in the whole genome association analysis of 20 one-year-old dairy goat ram samples in Example 2.

[0076] Fig.58 This is the Manhattan plot of head length of 20 one-year-old dairy goat ram samples in the whole genome association analysis in Example 2.

[0077] Fig.59 This is the head length QQ plot of the whole genome association analysis of 20 one-year-old dairy goat ram samples in Example 2.

[0078] Fig.60This is the Manhattan plot of forehead width of the whole genome association analysis of 20 one-year-old dairy goat ram samples in Example 2.

[0079] Fig.61 This is the forehead width QQ plot of the whole genome association analysis of 20 one-year-old dairy goat ram samples in Example 2.

[0080] Fig.62 This is the Manhattan plot of the whole genome association analysis of 20 one-year-old dairy goat ram samples in Example 2.

[0081] Fig.63 This is the tube circumference QQ plot of the whole genome association analysis of 20 one-year-old dairy goat ram samples in Example 2.

[0082] Fig.64 This is the Manhattan plot of live weight before slaughter of 20 one-year-old dairy goat ram samples in the whole genome association analysis in Example 2.

[0083] Fig.65 This is the QQ plot of live weight before slaughter of 20 one-year-old dairy goat ram samples from Example 2 based on the whole genome association analysis.

[0084] Fig.66 This is the Manhattan plot of carcass weight of 20 one-year-old dairy goat ram samples from the whole genome association analysis in Example 2.

[0085] Fig.67 This is the QQ plot of carcass weight of 20 one-year-old dairy goat ram samples from the whole genome association analysis in Example 2.

[0086] Fig.68 This is the Manhattan plot of net meat weight of 20 one-year-old dairy goat ram samples in the whole genome association analysis in Example 2.

[0087] Fig.69 This is the net meat weight QQ plot of the whole genome association analysis of 20 one-year-old dairy goat ram samples in Example 2.

[0088] Fig.70 This is the Manhattan plot of slaughter rate of 20 one-year-old dairy goat ram samples from the whole genome association analysis in Example 2.

[0089] Fig.71 This is the QQ plot of slaughter rate of 20 one-year-old dairy goat ram samples from the whole genome association analysis in Example 2.

[0090] Fig.72 This is the Manhattan plot of net meat rate of the whole genome association analysis of 20 one-year-old dairy goat ram samples in Example 2.

[0091] Fig.73This is the net meat rate QQ plot of the whole genome association analysis of 20 one-year-old dairy goat ram samples in Example 2.

[0092] Fig.74 This is the Manhattan plot of carcass net meat percentage of 20 one-year-old dairy goat ram samples from the whole genome association analysis in Example 2.

[0093] Fig.75 This is the QQ plot of carcass net meat rate of 20 one-year-old dairy goat ram samples from the whole genome association analysis in Example 2.

[0094] Fig.76 This is the Manhattan plot of eye muscle area of ​​the whole genome association analysis of 20 one-year-old dairy goat ram samples in Example 2.

[0095] Fig.77 This is the QQ plot of eye muscle area from the whole genome association analysis of 20 one-year-old dairy goat ram samples in Example 2.

[0096] Fig.78 This is the Manhattan plot of GR values ​​of the whole genome association analysis of 20 one-year-old dairy goat ram samples in Example 2.

[0097] Fig.79 This is the QQ plot of the GR value of the whole genome association analysis of 20 one-year-old dairy goat ram samples in Example 2.

[0098] Fig.80 This is the Manhattan plot of back fat thickness of 20 one-year-old dairy goat ram samples in the whole genome association analysis in Example 2.

[0099] Fig.81 This is the QQ plot of back fat thickness in the whole genome association analysis of 20 one-year-old dairy goat ram samples in Example 2.

[0100] Fig.82 This is the tail-weight Manhattan plot of the whole genome association analysis of 20 one-year-old dairy goat ram samples in Example 2.

[0101] Fig.83 This is the tail-weight QQ plot of the whole genome association analysis of 20 one-year-old dairy goat ram samples in Example 2.

[0102] Fig.84 This is the meat color L* Manhattan plot of the whole genome association analysis of 20 one-year-old dairy goat ram samples in Example 2.

[0103] Fig.85 This is the meat color L*QQ plot of the whole genome association analysis of 20 one-year-old dairy goat ram samples in Example 2.

[0104] Fig.86This is the meat color a* Manhattan plot of the whole genome association analysis of 20 one-year-old dairy goat ram samples in Example 2.

[0105] Fig.87 This is the meat color a*QQ plot of the whole genome association analysis of 20 one-year-old dairy goat ram samples in Example 2.

[0106] Fig.88 This is the meat color b* Manhattan plot of the whole genome association analysis of 20 one-year-old dairy goat ram samples in Example 2.

[0107] Fig.89 This is the meat color b*QQ plot of the whole genome association analysis of 20 one-year-old dairy goat ram samples in Example 2.

[0108] Fig.90 This is the pH Manhattan plot of the whole genome association analysis of 20 one-year-old dairy goat ram samples in Example 2.

[0109] Fig.91 This is the pH QQ diagram of the whole genome association analysis of 20 one-year-old dairy goat ram samples in Example 2.

[0110] Fig.92 This is the Manhattan plot of drip loss from the whole genome association analysis of 20 one-year-old dairy goat ram samples in Example 2.

[0111] Fig.93 This is the drip loss QQ plot of the whole genome association analysis of 20 one-year-old dairy goat ram samples in Example 2.

[0112] Fig.94 This is the Manhattan plot of cooked meat rate of 20 one-year-old dairy goat ram samples in the whole genome association analysis in Example 2.

[0113] Fig.95 This is the QQ graph of cooked meat rate of the whole genome association analysis of 20 one-year-old dairy goat ram samples in Example 2.

[0114] Fig.96 This is the shear force Manhattan plot of the whole genome association analysis of 20 one-year-old dairy goat ram samples in Example 2.

[0115] Fig.97 This is the shear force QQ plot of the whole genome association analysis of 20 one-year-old dairy goat ram samples in Example 2. DETAILED DESCRIPTION

[0116] The present invention provides a dairy goat breeding chip, which comprises 81107 SNP sites located in the dairy goat reference genome version GCA_015443085.1, and the position information of the SNP sites is shown in Table 5.

[0117] In the present invention, the GCA_015443085.1 is the NCBI sequence number of the reference genome. In the present invention, blood samples of 208 Guanzhong dairy goats in the lactation period were used to perform whole genome resequencing of the dairy goat population, and the resequencing data were screened for SNP sites based on the deletion rate and the retention of dimorphic sites. The site screening criteria include: MAF value ≥ 0.05; deletion rate < 0.15; heterozygosity rate ≤ 0.5; sequencing depth ≥ 5. In the present invention, as an implementable embodiment, the chip includes probes designed for the gene sequence covering the SNP site. The chip is made using an on-chip in-situ synthesis method, an off-chip synthesis method, or a microbead method.

[0118] In the present invention, as an implementation method, when the chip is made, each SNP probe sequence containing 20-50 deoxynucleotides is coupled to a specific microbead. The type of microbead is determined according to the number of SNPs carried, ranging from several thousand to more than tens of millions. Each type of microbead is encoded and detected by its specific address sequence and SNP probe sequence.

[0119] The invention also provides application of the breeding chip in dairy goat genotyping and population genetic structure analysis.

[0120] The present invention also provides application of the breeding chip in molecular marker-assisted breeding of dairy goats.

[0121] The present invention also provides application of the breeding chip in dairy goat trait association analysis.

[0122] The invention also provides the application of the breeding chip in the construction of dairy goat genetic map and gene positioning.

[0123] The invention also provides application of the breeding chip in identification of kinship of dairy goats.

[0124] The present invention also provides application of the breeding chip in gene analysis and / or screening of dairy goat breeding resources.

[0125] The present invention also provides application of the breeding chip in genetic diversity assessment of dairy goats.

[0126] The present invention also provides application of the breeding chip in whole genome association analysis of dairy goats.

[0127] The invention also provides application of the breeding chip in whole genome linkage analysis of dairy goats.

[0128] The present invention develops for the first time an 80K dairy goat liquid phase breeding chip, which has large detection throughput, high marker coverage, a genotype detection rate of more than 99%, high genotype accuracy and good stability, and can perform whole genome association analysis on the phenotypic data and genotypic data of goats.

[0129] Unless otherwise specified, the experimental methods used in the following examples are all conventional molecular biological methods; the materials and reagents used are reagents and materials that can be obtained from commercial channels unless otherwise specified.

[0130] Example 1 Acquisition of SNP sites in the whole genome of dairy goat population and development of breeding chips

[0131] 1. Whole genome resequencing of dairy goat population

[0132] 1. All steps of whole genome sequencing experiment

[0133] Blood samples were collected from 208 lactating Guanzhong dairy goats at ShenDa Ranch in Hohhot, Inner Mongolia, and the samples were labeled and recorded.

[0134] Construct a 300-500bp DNA small fragment library, and sequence it after the library passes the test.

[0135] The specific experimental process of constructing a small DNA fragment library is as follows: Figure 1 As shown, it mainly includes the following specific steps:

[0136] S1. DNA sample extraction and quality control: DNA was extracted from blood samples using magnetic beads. The concentration of DNA samples was detected using Qubit fluorescence quantification instrument; the integrity of DNA samples was detected using 1% agarose gel electrophoresis, and samples that passed quality control were used for library preparation.

[0137] S2. DNA fragmentation, end repair and quality control: Use dsDNA Fragmentase to fragment the DNA sample, repair the end of the enzyme cut, and add A base to the 3' end. Use 2% agarose gel electrophoresis to detect the effect of DNA fragmentation, and samples with obvious bright bands at 300-500bp are used for subsequent reactions.

[0138] S3. Adapter ligation and quality control: Use ligase to connect sequencing adapters and fragmented DNA, and ligation products are purified using magnetic beads. The concentration of the purified products is detected by Qubit fluorescence quantification instrument, and samples with qualified concentrations are subjected to subsequent reactions.

[0139] S4. Fragment amplification, selection and quality control: PCR was used to amplify the ligation products, and magnetic beads were used to screen the PCR products. The products after fragment screening were tested for library concentration using Qubit fluorescence quantification instrument, fragment size was tested using 2% agarose gel electrophoresis, and library fragment size was verified using Qsep400 bioanalyzer.

[0140] S5. Circularization and quality control: The linear library is denatured into single strands and then circularized. After digesting the uncircularized linear DNA molecules, a single-stranded circular library is obtained. The concentration of the single-stranded circular library is detected by Qubit fluorescence quantitative instrument, and the subsequent reaction is carried out if the concentration is qualified.

[0141] S6.DNB preparation and quality control: Single-stranded circular DNA molecules replicate through rolling circles to form a DNA nanoball (DNB) containing more than 300 copies. The DNB concentration is detected using a Qubit fluorescence quantifier, and subsequent reactions are performed when the concentration is qualified.

[0142] S7. Sequencing on the machine: Use the loading device MGIDL-T7 to load DNB into the sequencing chip, and sequence on the machine through the combined probe anchor polymerization technology to obtain the original sequencing sequence.

[0143] 2 Sequencing data quality control

[0144] There are some low-quality data in the original sequencing sequences obtained above, which will cause great errors in the sequencing results. It is necessary to use specific software FASTP v0.23.2 to filter the data to ensure the accuracy of the test results.

[0145] Results: After filtering by FASTP software, the sequencing output of 208 Guanzhong dairy goats was 6317.42Gb (clean data), with an average of 30.37Gb per sample and an average Q30 of 94.97% for all samples. The sequencing data output and quality of the first 10 samples are summarized in Table 1.

[0146] Table 1 Summary of data output quality

[0147] Samples Rawreads Rawbases Cleanreads Cleanbases Cleanrate CleanQ20 CleanQ30 Depth GC 00316 222,583,058 33,387,458,700 214,405,432 31,571,147,585 94.56％ 98.24％ 94.83％ 11.71 43.57％ 00285 250,559,938 37,583,990,700 241,261,420 35,528,603,786 94.53％ 98.48％ 95.47％ 13.18 43.45％ 00399 204,260,216 30,639,032,400 181,542,216 26,231,669,589 85.62％ 98.54％ 94.80％ 9.73 43.55％ 294 210,643,920 31,596,588,000 186,365,198 26,941,126,327 85.27％ 98.84％ 95.72％ 9.99 43.05％ 41975 237,208,928 35,581,339,200 203,490,206 29,045,543,220 81.63％ 98.98％ 96.16％ 10.77 44.82％ 42275 194,307,494 29,146,124,100 189,200,918 27,983,080,305 96.01％ 98.13％ 94.73％ 10.38 43.66％ 42386 411,508,918 61,726,337,700 347,306,908 49,473,822,798 80.15％ 98.98％ 96.16％ 18.35 44.47％ 42425 249,880,326 37,482,048,900 214,858,118 30,639,461,721 81.74％ 98.62％ 94.49％ 11.36 45.02％ 42427 235,794,162 35,369,124,300 199,859,108 28,546,684,392 80.71％ 98.32％ 95.24％ 10.59 44.09％ 42508 205,228,544 30,784,281,600 196,751,650 28,954,349,830 94.06％ 97.48％ 93.17％ 10.74 43.50％

[0148] Note: Samples: sample name; Rawreads: total number of raw reads; Rawbases: total number of raw bases; Cleanreads: total number of reads after filtering; Cleanbases: total number of bases after filtering; Cleanrate: ratio of clean bases / raw bases; CleanQ20&Q30: percentage of bases with quality values ​​≥20&30; Depth: sequencing depth, total clean base / genome size; GC: GC content.

[0149] 3. Sequencing data quality assessment

[0150] Whether the base can be correctly identified determines the quality of sequencing. The sequencer itself, sequencing reagents and samples affect the quality of the base. FastQC v0.11.9 is used to generate a basic quality distribution diagram of sequencing data to reflect the stability of sequencing quality during the sequencing process.

[0151] Results: Taking sample 000316 as an example, its basic mass distribution diagram is shown in Figure 2 The horizontal coordinate is the basic position of the filtered data, and the vertical coordinate is the average basic quality value. From the distribution diagram, the average basic quality value is greater than 30. This indicates that the sequencing data quality of sample 000316 is relatively high. After analysis, the basic quality of other samples has also reached an acceptable level, indicating that the sequencing data of this experiment is very successful.

[0152] 4 Genetic evolution analysis

[0153] (1) Phylogenetic tree construction

[0154] All the obtained SNP sites were first screened according to the missing rate ≤ 0.1 and MAF value ≥ 0.05, and the binary SNP sites located on the chromosome were retained. After quality control, 15,511,550 SNP sites were obtained.

[0155] The 15,511,550 SNP sites obtained after quality control were used to construct a phylogenetic tree using Plink 1.9.

[0156] Results: A phylogenetic tree was constructed using 15,511,550 SNP loci obtained after quality control. Figure 3 From the results of the evolutionary tree, the 208 Guanzhong dairy goat samples were closely related and had similar genetic characteristics.

[0157] (2) Principal Component Analysis (PCA)

[0158] The genetic distance between samples was calculated by the above evolutionary tree, and PCA cluster analysis was performed. The results are shown in Figure 4 .

[0159] from Figure 4 From the clustering results, the group samples are relatively clustered and there is no stratification phenomenon.

[0160] (3) Group structure analysis

[0161] The filtered SNP loci were transferred to Admixture v1.3.0 to obtain the population genetic structure of the research subjects and calculate the number of subgroups based on the flatness of the cross-validation error.

[0162] Results: The cross-validation error distribution at different K values ​​is shown in Figure 5 , the group structure diagram is as follows Figure 6 As shown in the figure, the approximate range of K value is between 1 and 9. After calculation one by one, it is found that when K = 2, the cross-validation error is the most gentle, so K = 2 is selected as the optimal number of subgroups.

[0163] 2. Genome-wide association analysis of body weight, body size and milk production traits in Guanzhong dairy goats

[0164] Using the above-mentioned group of 208 lactating ewes and 30 breeding rams from the Inner Mongolia ShenDa Ranch as materials, a detailed study was conducted through the data of weight, body size and milk production traits of these individuals, and molecular markers and functional genes related to the weight, body size and milk production traits of dairy goats were screened, thereby providing new candidate sites for the development of dairy goat breeding chips.

[0165] 1Genome-Wide Association Study (GWAS)

[0166] (1) Variant site filtering

[0167] The SNP sites of the above 238 samples were screened based on the deletion rate and retained dimorphic sites using VCF tools, and the obtained SNP sites were used for whole-genome association analysis.

[0168] The site screening criteria are shown in Table 2:

[0169] Table 2 Site screening criteria

[0170]

[0171]

[0172] Note: GWAS loci do not take population indicators into consideration.

[0173] (2) GWAS analysis

[0174] rMVP v1.1.0 was used to perform genome-wide association analysis on the traits of 238 dairy goats. A total of three computational models were used in the analysis: general linear model (GLM), mixed linear model (MLM) and FarmCPU model, and the principal component was added to the model as a covariate for correction, and genome-wide association analysis was performed on 16 phenotypic traits. The 16 phenotypic traits are: body height, body oblique length, chest circumference, tube circumference, weight, chest width, chest depth, waist angle width, cross height, milk production, milk fat rate, milk protein rate, lactose rate, ash rate, total dry matter ratio, and somatic cell number.

[0175] 2GWAS analysis results

[0176] (1) Results of genome-wide association analysis of body weight and size traits

[0177] Figures 7 to 15 This is the Manhattan plot of 9 traits including height and body length of 208 dairy goats. Figures 16 to 24 This is a QQ plot of 9 traits including height and oblique length of 208 dairy goats. Among them, height, oblique length, chest circumference, tube circumference, and weight are based on the data of 30 breeding rams and 238 dairy goats after merging. If found in the figure, it means that the site significantly associated with the phenotype can be located, otherwise, it cannot be located.

[0178] From the above GWAS results, we can know that:

[0179] Among the significant loci located for chest circumference, the GLM model has 1468 loci, the FarmCPU model has 197 loci, and the MLM model has 906 loci. The SNP loci are mainly distributed on chromosomes 1, 2, 6, 8, and 14.

[0180] Among the significant loci located for breast depth, the GLM model had 219 loci, the FarmCPU model had 248 loci, and the MLM model had 70 loci. The SNP loci were mainly distributed on chromosomes 1, 13, 17, 22, and 29.

[0181] Among the significant loci of chest width trait, GLM model has 173 loci, FarmCPU model has 298 loci, and MLM model has 49 loci. SNP loci are mainly distributed on chromosomes 1, 4, 11, 24, and 29.

[0182] Among the significant loci of body oblique length trait, GLM model has 730 loci, FarmCPU model has 1055 loci, and MLM model has 348 loci. SNP loci are mainly distributed on chromosomes 1, 9, 16, 18, and 20.

[0183] Among the significant loci of tube circumference traits, the GLM model has 2065 loci, the FarmCPU model has 92 loci, and the MLM model has 1688 loci. The SNP loci are mainly distributed on chromosomes 1, 5, 6, 8, 13, and 14.

[0184] Among the significant loci of height trait, GLM model has 1304 loci, FarmCPU model has 138 loci, and MLM model has 866 loci. SNP loci are mainly distributed on chromosomes 15, 13, 18, and 30.

[0185] Among the significant loci of body weight, the GLM model has 4479 loci, the FarmCPU model has 104 loci, and the MLM model has 2596 loci. The SNP loci are mainly distributed on chromosomes 1, 2, 3, 6, 8, and 10.

[0186] The significant loci of the cross-high trait are 107 in the GLM model, 87 in the FarmCPU model, and 23 in the MLM model. The SNP loci are mainly distributed on chromosomes 1, 5, 8, and 22.

[0187] Among the significant loci of waist width, the GLM model has 199 loci, the FarmCPU model has 210 loci, and the MLM model has 108 loci. The SNP loci are mainly distributed on chromosomes 3, 4, 16, 25, and 28.

[0188] (2) Results of genome-wide association analysis of milk production traits

[0189] Manhattan plots and QQ plots of loci significantly associated with milk production phenotypes are shown in Fig.25 The results of genome-wide analysis of the average milk production of dairy goats in 22 days showed that the average milk production in 22 days was located in multiple significant loci, including 231 in the GLM model, 231 in the FarmCPU model, and 23 in the MLM model. Candidate genes such as XXYLT1, GULP1, OLFM3, CD9, TTK, LHPP, and MYO10 were screened on chromosomes 2, 3, 9, 11, 12, and 16.

[0190] Manhattan plot and QQ plot based on milk fat percentage trait, see Fig.26 The three models located significant loci, 497 loci in the GLM model, 228 loci in the MLM model, and 111 loci in the FarmCPU model. Candidate genes such as OSGEPL1, HS2ST1, LDB2, and UHRF2 were screened on chromosomes 2, 4, 5, and 9.

[0191] Manhattan plot and QQ plot based on milk protein rate trait, see Fig. 27The three models located significant loci, including 384 loci in the GLM model, 168 loci in the MLM model, and 386 loci in the FarmCPU model. Candidate genes such as CDK14, PBX1, and CHN2 were screened on chromosomes 1, 7, 13, and 16.

[0192] Manhattan plot and QQ plot based on lactose rate trait, see Fig.28 The three models located significant loci, 231 loci in the GLM model, 80 loci in the MLM model, and 217 loci in the FarmCPU model. Candidate genes such as ST7L, PRKCB, BCAS2, and PHTF1 were screened on chromosomes 1 and 3.

[0193] Manhattan plot and QQ plot according to ash content characteristics, see Fig.29 The three models located significant loci, including 540 loci in the GLM model, 252 loci in the MLM model, and 120 loci in the FarmCPU model. Candidate genes such as IGSF21, FRZB, and ZPBP were screened on chromosomes 1, 2, and 7.

[0194] Manhattan plot and QQ plot based on total dry matter ratio trait, see Fig.30 The three models located significant loci, including 987 loci in the GLM model, 536 loci in the MLM model, and 106 loci in the FarmCPU model. Candidate genes such as TPK1, SYT1, and TRPM3 were screened on chromosomes 1, 6, and 9.

[0195] Manhattan plot and QQ plot based on somatic cell number traits, see Fig.31 The three models located significant loci, including 1970 loci in the GLM model, 1008 loci in the MLM model, and 110 loci in the FarmCPU model. Candidate genes such as PIK3CB, GLMN, CPM, and FOXN3 were screened on chromosomes 1, 3, 5, 12, and 14.

[0196] 3. Development and verification of 80K dairy goat breeding chip

[0197] 1. SNP locus type

[0198] A total of 81,107 SNP loci were finally screened out through genome-wide association analysis, and the types of SNP loci are shown in Table 3.

[0199] Table 3 SNP locus types

[0200] Site type Number of SNPs Resequencing data 74712 GWAS loci 1627 Variety-specific loci 1030 Literature collection functional genes 3738 total 81107

[0201] 2. SNP site information statistics

[0202] By statistically analyzing the SNP site information, the statistical table is shown in Table 4.

[0203] Table 4 SNP site information statistics

[0204]

[0205]

[0206] 3. Location information of SNP sites

[0207] The location information of the SNP sites is shown in Table 5.

[0208] Table 5 Location information of SNP sites

[0209]

[0210]

[0211]

[0212]

[0213]

[0214]

[0215]

[0216]

[0217]

[0218]

[0219]

[0220]

[0221]

[0222]

[0223]

[0224]

[0225]

[0226]

[0227]

[0228]

[0229]

[0230]

[0231]

[0232]

[0233]

[0234]

[0235]

[0236]

[0237]

[0238]

[0239]

[0240]

[0241]

[0242]

[0243]

[0244]

[0245]

[0246]

[0247]

[0248]

[0249]

[0250]

[0251]

[0252]

[0253]

[0254]

[0255]

[0256]

[0257]

[0258]

[0259]

[0260]

[0261]

[0262]

[0263]

[0264]

[0265]

[0266]

[0267]

[0268]

[0269]

[0270]

[0271]

[0272]

[0273]

[0274]

[0275]

[0276]

[0277]

[0278]

[0279]

[0280]

[0281]

[0282]

[0283]

[0284]

[0285]

[0286]

[0287]

[0288]

[0289]

[0290]

[0291]

[0292]

[0293]

[0294]

[0295]

[0296]

[0297]

[0298]

[0299]

[0300]

[0301]

[0302]

[0303]

[0304]

[0305]

[0306]

[0307]

[0308]

[0309]

[0310]

[0311]

[0312]

[0313]

[0314]

[0315]

[0316]

[0317]

[0318]

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[0320]

[0321]

[0322]

[0323]

[0324]

[0325]

[0326]

[0327]

[0328]

[0329]

[0330]

[0331]

[0332]

[0333]

[0334]

[0335]

[0336]

[0337]

[0338]

[0339]

[0340]

[0341]

[0342]

[0343]

[0344]

[0345]

[0346]

[0347]

[0348]

[0349]

[0350]

[0351]

[0352]

[0353]

[0354]

[0355] 4. Schematic diagram of the distribution of SNP sites in the genome

[0356] The schematic diagram of the distribution of SNP sites in each chromosome of the genome is shown in Fig.32 ,pass Fig.32 It can be seen that SNP sites are distributed on each chromosome and are distributed relatively evenly, thus showing their uniform distribution in the entire genome.

[0357] Using the obtained 81,107 SNP marker combinations as detection targets, highly specific probes were designed and handed over to Huazhi Biotechnology Co., Ltd. for probe synthesis and detection, and an 80K dairy goat breeding chip was obtained.

[0358] Example 2 Application of 80K Dairy Goat Breeding Chip in Dairy Goat Groups

[0359] The developed 80K dairy goat breeding chip was used to conduct genome-wide association analysis on weight, body size and slaughter traits of 20 one-year-old dairy goat rams.

[0360] 1. Data quality control results

[0361] The 81107 SNP loci obtained by 80K liquid phase chip sequencing of dairy goats were analyzed by Plink1.9 according to the conditions of missing rate ≤ 0.1, MAF value ≥ 0.05, minimum allele frequency ≥ 0.05 and P> 1×10 in Hardy-Weinberg equilibrium test. -6 After data quality control according to the standards, 72792 SNP loci of 20 individuals were obtained.

[0362] Visualize the deletion rate of a single site, the deletion rate of a genotype individual, the minimum allele frequency, and the Hardy-Weinberg P value, and draw a statistical histogram, such as Fig.33 Observation Fig.33 In the overall distribution of deletion rates in Figure a, the deletion rates of most sites are low (less than 0.05). Fig.33 Figure b shows the missing rate distribution of samples. The missing rate of most individuals is low (less than 0.05), and the data quality is good. Fig.33 All SNPs with a minimum allele frequency below 5% screened in Figure c need to be filtered out. Fig.33 Figure d shows the Hardy-Weinberg equilibrium test with P>1×10 -6 Samples with a detection rate of less than 95% were eliminated, and SNPs with unqualified quality were filtered out.

[0363] 2 Population structure analysis results

[0364] (1) Results of principal component analysis

[0365] The dairy goat population was subjected to principal component analysis using Plink1.9. The same steps as above showed that the sample population was relatively concentrated. According to the first and second principal components, it can be seen that the differences between individuals in the dairy goat population are small ( Fig.34 ).

[0366] (2) Phylogenetic tree construction results

[0367] The phylogenetic tree constructed using Plink1.9 based on the quality-controlled SNPs markers is shown in the following figure. Fig.35 As shown, it can be seen that there are certain similarities in the genetic characteristics of the sample groups, and the relationship is relatively close.

[0368] (3) LD attenuation analysis results

[0369] The results of LD decay analysis of dairy goat population using PopLDdecay v1.5 are as follows Fig.36 As shown in the figure, the difference in population diversity can be judged by the LD decay rate. Generally, the LD decay rate of wild populations is faster than that of domesticated populations. The comparison between the LD decay distance (0.1) and the average distance between markers can be used to judge whether the markers in the GWAS association analysis are sufficient. Fig.36 The medium LD decay distance (0.1) and the average distance between markers show that there are enough markers in the GWAS analysis.

[0370] (4) Results of kinship analysis

[0371] The heat map of the kinship matrix was obtained by analyzing the kinship of 20 dairy goat rams using GCTAv1.94.0. Fig.37 As shown in Figure 1, the heat map of the kinship matrix uses different shades of color to represent the kinship distance between dairy goat rams, with light colors representing more distant relationships. Fig.37 It can be seen that the samples are of the same species and have a close relationship.

[0372] Results of genome-wide association analysis of 3 goat traits

[0373] (1) Genome-wide association analysis of body weight

[0374] In the results of genome-wide association analysis of body weight (such as Fig.38 Manhattan plot of body weight, Fig.39 In the QQ graph of body weight, five SNP sites with significant correlation at the whole genome level were found, which were located on chromosomes 3, 4, 7, 10, and 16. In addition, Sahana et al. defined SNP sites with significant level at the chromosome range as the standard for significant correlation at the chromosome range, and the P value corresponding to such significant correlation SNP sites should be less than 10 -4(Sahana et al., 2010). Therefore, 15 SNP sites significantly associated at the chromosome level were obtained, located on chromosomes 2, 7, 8, 11, 15, 18, 20, 21, 24, and 28, respectively.

[0375] (2) Genome-wide association analysis of body height

[0376] The results of genome-wide association analysis of body height (such as Fig.40 Manhattan plot of body height, Fig.41 In the QQ plot, one SNP site with significant correlation at the whole genome level was found, which was located on chromosome 20. There were 12 SNP sites with significant correlation at the chromosome level, which were located on chromosomes 4, 6, 8, 14, and 20, respectively.

[0377] (3) Genome-wide association analysis of body length

[0378] The results of genome-wide association analysis of in vivo oblique length (such as Fig.42 The oblique long Manhattan plot, Fig.43 In the QQ plot, one SNP site with significant correlation at the whole genome level was found, which was located on chromosome 21. There were 8 SNP sites with significant correlation at the chromosome level, which were located on chromosomes 2, 3, 9, 12, 14, 21, and 27, respectively.

[0379] (4) Chest circumference genome-wide association analysis

[0380] No SNP loci significantly associated with chest circumference were found in the genome-wide association analysis (e.g. Fig.44 Bust Manhattan Chart, Fig.45 There are 14 SNP sites significantly associated with each other at the chromosome level, located on chromosomes 3, 4, 6, 14, 15, 19, 20, 24, and 29.

[0381] (5) Genome-wide association analysis of chest depth

[0382] The results of genome-wide association analysis of chest depth (such as Fig.46 chest-deep manhattan chart, Fig.47 In the QQ graph of chest depth, one SNP site with significant correlation at the whole genome level was found, which was located on chromosome 15. There were 13 SNP sites with significant correlation at the chromosome level, which were located on chromosomes 7, 8, 20, 21, and 29.

[0383] (6) Genome-wide association analysis of chest width

[0384] The results of genome-wide association analysis of chest width (such as Fig.48 Manhattan chart of chest width, Fig.49In the chest width QQ graph, one SNP site with significant correlation at the whole genome level was found, which was located on chromosome 26. There were 9 SNP sites with significant correlation at the chromosome level, which were located on chromosomes 3, 10, 13, 20, 25, 26, and 29.

[0385] (7) Genome-wide association analysis of Jiri Hiro

[0386] In the genome-wide association analysis results of Ji Hiro Fig.50 Butt width Manhattan map, Fig.51 In the QQ plot, one SNP site with significant correlation at the whole genome level was found, which was located on chromosome 13. There were 8 SNP sites with significant correlation at the chromosome level, which were located on chromosomes 3, 18, 19, and 25, respectively.

[0387] (8) Genome-wide association analysis of buttock length

[0388] No SNP loci significantly associated with natural length were found in the genome-wide association analysis (e.g. Fig.52 Butt long Manhattan map, Fig.53 There are 5 SNP loci that are significantly associated at the chromosome level, located on chromosomes 1, 5, and 19.

[0389] (9) Genome-wide association analysis of abdominal circumference

[0390] The results of genome-wide association analysis of abdominal circumference (such as Fig.54 Waist circumference Manhattan chart, Fig.55 In the data of the QQ graph of waist circumference, one SNP site with significant correlation at the whole genome level was found, which was located on chromosome 23. There were 8 SNP sites with significant correlation at the chromosome level, which were located on chromosomes 6, 8, 10, 13, 27, and 29, respectively.

[0391] (10) Genome-wide association analysis of waist width

[0392] The results of genome-wide association analysis of waist width (such as Fig.56 Manhattan map with waist width, Fig.57 In the QQ plot of waist width, one SNP site with significant correlation at the whole genome level was found, which was located on chromosome 21. There were 18 SNP sites with significant correlation at the chromosome level, which were located on chromosomes 3, 5, 6, 10, 11, 12, 13, 14, and 27.

[0393] (11) Genome-wide association analysis of head length

[0394] The results of genome-wide association analysis on head length (such as Fig.58 Head length Manhattan map, Fig.59In the head length QQ diagram), two SNP sites with significant correlation at the whole genome level were found, located on chromosome 17. There were 13 SNP sites with significant correlation at the chromosome level, located on chromosomes 8, 12, 15, 16, 19, 23, and 24.

[0395] (12) Genome-wide association analysis of forehead width

[0396] In the genome-wide association analysis results of forehead width (such as Fig.60 Forehead width Manhattan chart, Fig.61 In the QQ plot, two SNPs with significant correlation at the whole genome level were found, located on chromosomes 13 and 18. Three SNPs with significant correlation at the chromosome level were located on chromosomes 5 and 17.

[0397] (13) Genome-wide association analysis of vascular perivascular

[0398] No SNP loci significantly associated with tube circumference were found in the genome-wide association analysis (such as Fig.62 Manhattan map of the tube, Fig.63 There are 16 SNP loci that are significantly associated at the chromosome level, located on chromosomes 1, 3, 4, 7, 9, 10, 14, 20, 23, and 25.

[0399] (14) Genome-wide association analysis of live weight before slaughter

[0400] No SNP loci significantly associated with live weight before slaughter were found in the genome-wide association analysis (e.g. Fig.64 Manhattan chart of live weight before slaughter, Fig.65 There are 3 SNP loci significantly associated at the chromosome level, located on chromosomes 4, 5, and 10, respectively.

[0401] (15) Genome-wide association analysis of carcass weight

[0402] No SNP loci significantly associated with carcass weight were found in the genome-wide association analysis (e.g. Fig.66 Carcass weight Manhattan chart, Fig.67 There are 10 SNP loci significantly associated at the chromosome level, located on chromosomes 4, 5, 8, 10, 15, 19, 21, and 24.

[0403] (16) Genome-wide association analysis of net meat weight

[0404] No SNP loci significantly associated with net meat weight were found in the genome-wide association analysis (e.g. Fig.68 Net meat weight Manhattan chart, Fig.69There are 11 SNP sites significantly associated at the chromosome level, located on chromosomes 4, 5, 8, 10, 15, 19, 21, and 24.

[0405] (17) Genome-wide association analysis of slaughter rate

[0406] No SNP loci significantly associated with slaughter rate were found in the genome-wide association analysis (e.g. Fig.70 Manhattan chart of slaughter rate, Fig.71 There are 7 SNP loci significantly associated at the chromosome level, located on chromosomes 7 and 8 respectively.

[0407] (18) Genome-wide association analysis of net meat rate

[0408] The results of genome-wide association analysis of net meat rate (such as Fig.72 Manhattan chart of net meat rate, Fig.73 In the QQ graph of net meat rate, 4 SNP sites with significant correlation at the whole genome level were found, located on chromosome 8. There were 9 SNP sites with significant correlation at the chromosome level, located on chromosomes 1, 2, 6, 8, 9, and 19 respectively.

[0409] (19) Genome-wide association analysis of carcass net meat percentage

[0410] The results of genome-wide association analysis on carcass net meat percentage (such as Fig.74 Manhattan chart of carcass net meat percentage, Fig.75 In the carcass net meat rate QQ graph), one SNP site significantly associated at the whole genome level was found, located on chromosome 16. There were two SNP sites significantly associated at the chromosome level, located on chromosomes 5 and 9, respectively.

[0411] (20) Genome-wide association analysis of eye muscle area

[0412] No SNP loci significantly associated with eye muscle area were found in the genome-wide association analysis (e.g. Fig.76 Manhattan chart of eye muscle area, Fig.77 There are 5 SNP loci significantly associated at the chromosome level, located on chromosomes 4, 5, 6, 7, and 18.

[0413] (21) Genome-wide association analysis of GR values

[0414] No SNP loci significantly associated with GR values ​​were found in the genome-wide association analysis of GR values ​​(e.g. Fig.78 Manhattan plot of GR value, Fig.79 There is one SNP site that is significantly associated at the chromosome level, located on chromosome 2.

[0415] (22) Genome-wide association analysis of back fat thickness

[0416] In the genome-wide association analysis results of back fat thickness (such as Fig.80 Back fat thickness Manhattan chart, Fig.81 In the back fat thickness QQ diagram), one SNP site with significant correlation at the whole genome level was found, which was located on chromosome 14. There were 10 SNP sites with significant correlation at the chromosome level, which were located on chromosomes 5, 8, 12, 13, 18, 21, and 23 respectively.

[0417] (23) Genome-wide association analysis of tail weight

[0418] No SNP loci significantly associated with tail weight were found in the genome-wide association analysis of tail weight (e.g. Fig.82 Tail-heavy Manhattan plot, Fig.83 There are 9 SNP loci that are significantly associated at the chromosome level, located on chromosomes 10, 11, 22, and 26.

[0419] (24) Genome-wide association analysis of meat color L*

[0420] In the results of genome-wide association analysis of meat color L* (such as Fig.84 Flesh-colored L* Manhattan map, Fig.85 In the L*QQ graph, three SNP sites with significant correlation at the whole genome level were found, located on chromosomes 9, 11, and 25. There were 12 SNP sites with significant correlation at the chromosome level, located on chromosomes 4, 5, 7, 8, 9, 11, 24, 26, and 31.

[0421] (25) Meat color a* genome-wide association analysis

[0422] In the genome-wide association analysis results of meat color a* (such as Fig.86 Flesh-colored a* Manhattan map, Fig.87 In the a*QQ graph, one SNP site with significant correlation at the whole genome level was found, which was located on chromosome 10. There were 15 SNP sites with significant correlation at the chromosome level, which were located on chromosomes 1, 2, 4, 6, 10, 13, 17, and 21.

[0423] (26) Meat color b* genome-wide association analysis

[0424] No SNP loci significantly associated with meat color b* were found in the genome-wide association analysis (e.g. Fig.88 Flesh color b* Manhattan map, Fig.89There are 11 SNP sites that are significantly associated at the chromosome level, located on chromosomes 2, 4, 9, 13, 20, 25, 26, and 29.

[0425] (27) pH genome-wide association analysis

[0426] No SNP loci significantly associated with pH were found in the whole genome association analysis of pH (such as Fig.90 pH Manhattan plot, Fig.91 There are three SNP sites that are significantly associated at the chromosome level, located on chromosomes 11 and 12 respectively.

[0427] (28) Genome-wide association analysis of drip loss

[0428] No SNP loci significantly associated with drip loss were found in the genome-wide association analysis of drip loss (e.g. Fig.92 Drip loss Manhattan plot, Fig.93 There are 8 SNP loci significantly associated at the chromosome level, located on chromosomes 4, 9, 17, 21, and 25.

[0429] (29) Genome-wide association analysis of cooked meat rate

[0430] No SNP loci significantly associated with cooked meat rate were found in the genome-wide association analysis (e.g. Fig.94 Manhattan chart of cooked meat rate, Fig.95 There are 19 SNP loci significantly associated at the chromosome level, located on chromosomes 1, 3, 4, 7, 8, 9, 13, 19, 20, 25, and 27.

[0431] (30) Genome-wide association analysis of shear stress

[0432] In the genome-wide association analysis results of shear stress (such as Fig.96 Manhattan plot of shear force, Fig.97 In the shear force QQ diagram, 6 SNP sites with significant correlation at the whole genome level were found, which were located on chromosomes 8, 9, 16, 24, and 27. There were 12 SNP sites with significant correlation at the chromosome level, which were located on chromosomes 5, 8, 10, 15, 24, 27, 28, and 29.

[0433] 4 Positional candidate genes

[0434] To further explore the molecular markers and functional genes related to goat traits, 172 candidate genes were found near the interval of significant sites by combining the goat genome and public information on NCBI (see Table 6 for details).

[0435] Table 6 Candidate genes associated with traits

[0436]

[0437]

[0438]

[0439] The above results prove that the 80K dairy goat breeding chip can perform genome-wide association analysis on the phenotypic and genotypic data of goats.

[0440] Although the above embodiment describes the present invention in detail, it is only a part of the embodiments of the present invention, not all of the embodiments. People can also obtain other embodiments based on this embodiment without creativity, and these embodiments all fall within the protection scope of the present invention.

Claims

1. A dairy goat breeding chip, characterized in that: The dairy goat breeding chip contains 81,107 SNP sites located in the dairy goat reference genome version GCA_015443085.1, and the position information of the SNP sites is shown in Table 5.

2. Application of the breeding chip according to claim 1 in genotyping and population genetic structure analysis of dairy goats.

3. Application of the breeding chip according to claim 1 in molecular marker-assisted breeding of dairy goats.

4. Application of the breeding chip according to claim 1 in dairy goat trait association analysis.

5. Application of the breeding chip according to claim 1 in genetic map construction and gene localization of dairy goats.

6. Application of the breeding chip according to claim 1 in identification of genetic relationships in dairy goats.

7. Use of the breeding chip according to claim 1 in gene analysis and / or screening of dairy goat breeding resources.

8. Use of the breeding chip according to claim 1 in the genetic diversity assessment of dairy goats.

9. Use of the breeding chip according to claim 1 in genome-wide association analysis of dairy goats.

10. Use of the breeding chip according to claim 1 in whole genome linkage analysis of dairy goats.

Images