A breeding chip for dairy goats and its application

Abstract

This invention provides a dairy goat breeding chip and its applications, relating to the field of animal molecular breeding technology. The dairy goat breeding chip of this invention contains 81,107 SNP loci located in the dairy goat reference genome version GCA_015443085.1. The chip can be applied to dairy goat genotyping and population structure analysis, molecular marker-assisted breeding, trait association analysis, genetic map construction and gene localization, kinship identification, breeding resource gene analysis, genetic diversity assessment, genome-wide association analysis, and genome-wide linkage analysis. It features high throughput, high marker coverage, high genotype detection rate, high genotype accuracy, and good stability.

Description

Technical Field

[0001] This invention belongs to the field of animal molecular breeding technology, specifically relating to a dairy goat breeding chip and its application. Background Technology

[0002] The current market demand for goat milk is strong, but the development of dairy goat farming is relatively slow, with low levels of scale and facility-based operations. The fundamental problem hindering the industry's development is low farming efficiency, with key factors including low rates of qualified offspring reproduction, short average lifespan, and high management difficulty. Furthermore, compared to other livestock, genetic improvement of dairy goats is still in its early stages. Therefore, innovating molecular breeding techniques, accelerating genetic improvement, and speeding up the development of superior breeds are of great significance for the sustainable development of dairy goat farming.

[0003] Analyzing genetic variation in livestock populations is fundamental for livestock breed conservation, selection, and breeding. Techniques for analyzing genetic variation have evolved from morphological, cytological, and biochemical markers to the current DNA molecular markers based on DNA polymorphism. Currently, the most commonly used genetic marker is the SNP marker, where a single nucleotide variation in the genome can cause DNA sequence polymorphism. In mammalian genomes, there is approximately one SNP per 500–1000 base pairs. SNP microarrays are a biotechnological tool for rapid, large-scale screening of genetic variation sites. It is a high-throughput gene detection platform capable of efficiently detecting tens of thousands of SNPs in a single experiment.

[0004] Currently, genomic selection (GS) and genome-wide association analysis (GWAS) are widely used for the genetic improvement of important economic traits in livestock such as cattle and pigs. Among these methods, obtaining genotype data of SNP loci efficiently and conveniently at a low cost is a prerequisite and foundation for the effective implementation of genomic selection. Therefore, developing a liquid-phase SNP chip for dairy goat genomic breeding to accelerate the progress of genetic improvement in dairy goats is a problem that urgently needs to be solved by those skilled in the art. Summary of the Invention

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

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solutions:

[0007] This invention provides a dairy goat breeding chip, which contains 81,107 SNP sites located in dairy goat reference genome version GCA_015443085.1. The location information of the SNP sites is shown in Table 5.

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

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

[0010] This invention also provides the application of the breeding chip in trait association analysis of dairy goats.

[0011] This invention also provides the application of the breeding chip in the construction of genetic maps and gene localization in dairy goats.

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

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

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

[0015] This invention also provides the application of the breeding chip in genome-wide association analysis of dairy goats.

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

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

[0018] This invention is the first to develop an 80K liquid phase breeding chip for dairy goats, characterized by high detection throughput, high marker coverage, genotype detection rate exceeding 99%, high genotype accuracy, and good stability. It can be used for the detection of different dairy goat breeds. Genome-wide association analysis (GWAS) was performed on phenotypic and genotypic data from goat populations of different sample sources. The analysis results show that the chip can be applied to genotyping and population genetic structure analysis in dairy goats, marker-assisted breeding, trait association analysis, genetic map construction and gene localization, kinship identification, breeding resource gene analysis, genetic diversity assessment, GWAS, and GWAS linkage analysis; thus, it is beneficial to accelerate the progress of molecular genetic improvement in dairy goats. Attached Figure Description

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

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

[0021] Figure 3 This is a phylogenetic tree diagram from Example 1.

[0022] Figure 4 This is a PCA clustering analysis diagram from Example 1.

[0023] Figure 5 The results show the cross-validation error values ​​at different K value levels in Example 1.

[0024] Figure 6 This is a population genetic structure diagram from Example 1.

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

[0026] Figure 8 The image shows a Manhattan diagram of the oblique elongation morphology in Example 1.

[0027] Figure 9 This is a Manhattan diagram of the chest circumference characteristics in Example 1.

[0028] Figure 10 This is a Manhattan diagram of the pipe enclosure characteristics in Example 1.

[0029] Figure 11 This is a Manhattan diagram of body weight from Example 1.

[0030] Figure 12 This is a Manhattan diagram of chest width characteristics in Example 1.

[0031] Figure 13 This is a Manhattan diagram of chest depth characteristics in Example 1.

[0032] Figure 14 The Manhattan diagram shows the waist angle width characteristic in Example 1.

[0033] Figure 15 The Manhattan diagram shows the high-characteristic cross section in Example 1.

[0034] Figure 16 The figure shows the body height characteristics (QQ diagram) in Example 1.

[0035] Figure 17 The diagram shows the QQ shape of the tube casing in Example 1.

[0036] Figure 18 The image shows the QQ diagram of the body oblique elongation morphology in Example 1.

[0037] Figure 19 This is a weight QQ chart from Example 1.

[0038] Figure 20 The image shown is a QQ diagram illustrating the chest circumference characteristics in Example 1.

[0039] Figure 21 The image shown is a QQ diagram illustrating the waist angle width characteristic in Example 1.

[0040] Figure 22 The image shows the QQ pattern of the cross section in Example 1.

[0041] Figure 23 This is a QQ diagram of chest depth characteristics in Example 1.

[0042] Figure 24 This is a QQ diagram showing the chest width characteristics in Example 1.

[0043] Figure 25 In the diagram, A represents the Manhattan plot of the loci that were significantly associated with the milk production phenotype in Example 1. Figure 25 B in the figure represents the QQ plot that shows a significant correlation with the milk production phenotype in Example 1.

[0044] Figure 26 In the figure, A is the Manhattan plot of the milk fat percentage trait in Example 1. Figure 26 B in the figure is the QQ graph of the milk fat percentage property in Example 1.

[0045] Figure 27 In the diagram, A represents the Manhattan plot of the milk protein percentage trait in Example 1. Figure 27 B in the figure represents the QQ graph of the milk protein percentage trait in Example 1.

[0046] Figure 28 In the diagram, A represents the Manhattan plot of the lactose content trait in Example 1. Figure 28 B in the figure represents the QQ graph of lactose content in Example 1.

[0047] Figure 29 In the diagram, A represents the Manhattan plot of the ash content characteristics in Example 1. Figure 29 B in the figure represents the QQ graph of the ash content characteristics in Example 1.

[0048] Figure 30 In the figure, A is the Manhattan plot of the total dry matter ratio in Example 1. Figure 30 B in the figure represents the QQ graph of the total dry matter ratio in Example 1.

[0049] Figure 31 In the diagram, A represents the Manhattan plot of the somatic cell number trait in Example 1. Figure 31 B in the figure represents the QQ graph of somatic cell number traits in Example 1.

[0050] Figure 32 This is a schematic diagram showing the distribution of SNP sites in each chromosome of the genome in Example 1.

[0051] Figure 33 The quality control results for the SNPs site data in Example 2; wherein, Figure 33 In the figure, 'a' represents the statistical graph of the deletion rate at a single locus. Figure 33 In the figure, b represents the statistical graph of the individual genotype deletion rate. Figure 33 In this context, 'c' represents the minimum allele frequency. Figure 33 In the diagram, d represents the Hubble-Bergberg p-value histogram.

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

[0053] Figure 35 This is a schematic diagram of the phylogenetic tree of 20 one-year-old male dairy goats in Example 2.

[0054] Figure 36 The results are the LD attenuation analysis results from Example 2.

[0055] Figure 37 This is a heatmap of the kinship matrix of 20 one-year-old male dairy goats in Example 2.

[0056] Figure 38 The image shows the Manhattan plot of body weight from a genome-wide association analysis of 20 one-year-old male dairy goats in Example 2.

[0057] Figure 39 The image shows the QQ plot of body weight from 20 one-year-old male dairy goats in Example 2, based on genome-wide association analysis.

[0058] Figure 40 The image shows the high Manhattan plot of genome-wide association analysis of 20 one-year-old male dairy goats in Example 2.

[0059] Figure 41 The high QQ plot is a genome-wide association analysis of 20 one-year-old male dairy goats in Example 2.

[0060] Figure 42 The image shows a slanted Manhattan plot of genome-wide association analysis of 20 one-year-old male dairy goats from Example 2.

[0061] Figure 43 The oblique QQ plot of genome-wide association analysis of 20 one-year-old male dairy goats in Example 2.

[0062] Figure 44 Manhattan plot of chest circumference from 20 one-year-old male dairy goats in Example 2, based on genome-wide association analysis.

[0063] Figure 45 The chest circumference QQ graph is a genome-wide association analysis of 20 one-year-old male dairy goats in Example 2.

[0064] Figure 46 The deep Manhattan plot of the chest for genome-wide association analysis of 20 one-year-old male dairy goats in Example 2.

[0065] Figure 47 The image shows the chest depth QQ plot of a genome-wide association analysis of 20 one-year-old male dairy goats in Example 2.

[0066] Figure 48 Manhattan plot of chest width from 20 one-year-old male dairy goats in Example 2, based on genome-wide association analysis.

[0067] Figure 49 The chest width QQ diagram is shown in the genome-wide association analysis of 20 one-year-old male dairy goats in Example 2.

[0068] Figure 50 The Manhattan plot of the buttock width for genome-wide association analysis of 20 one-year-old male dairy goats in Example 2.

[0069] Figure 51 The image shows the QQ plot of the buttock width from the genome-wide association analysis of 20 one-year-old male dairy goats in Example 2.

[0070] Figure 52 Manhattan plot of buttock length from genome-wide association analysis of 20 one-year-old male dairy goats in Example 2.

[0071] Figure 53 The image shows the q-plot of the buttock length from 20 one-year-old male dairy goats in Example 2, based on a genome-wide association analysis.

[0072] Figure 54 Manhattan plot of abdominal circumference from 20 one-year-old male dairy goats in Example 2, based on genome-wide association analysis.

[0073] Figure 55 The image shows the q-shaped abdominal circumference of 20 one-year-old male dairy goats from Example 2, based on a genome-wide association analysis.

[0074] Figure 56 The Manhattan plot of waist angle width in genome-wide association analysis of 20 one-year-old male dairy goats in Example 2.

[0075] Figure 57 The image shows the waist angle width (QQ) plot of the genome-wide association analysis of 20 one-year-old male dairy goats in Example 2.

[0076] Figure 58 The Manhattan plot of head length from 20 one-year-old male dairy goats in Example 2 is shown in the genome-wide association analysis.

[0077] Figure 59 The head length QQ plot is shown in the genome-wide association analysis of 20 one-year-old male dairy goats in Example 2.

[0078] Figure 60The Manhattan plot of frontal width from genome-wide association analysis of 20 one-year-old male dairy goats in Example 2.

[0079] Figure 61 The frontal width QQ plot is shown in the genome-wide association analysis of 20 one-year-old male dairy goats in Example 2.

[0080] Figure 62 The Manhattan plot is shown for genome-wide association analysis of 20 one-year-old male dairy goats in Example 2.

[0081] Figure 63 The diagram shows the QQ diagram for genome-wide association analysis of 20 one-year-old male dairy goats in Example 2.

[0082] Figure 64 Manhattan plot of pre-slaughter live weight from 20 one-year-old male dairy goats in Example 2, based on genome-wide association analysis.

[0083] Figure 65 The image shows the pre-slaughter live weight QQ plot of 20 one-year-old male dairy goats from Example 2, based on a genome-wide association analysis.

[0084] Figure 66 Manhattan plot of carcass weight from 20 one-year-old male dairy goats in Example 2, based on genome-wide association analysis.

[0085] Figure 67 The carcass weight QQ plot is a genome-wide association analysis of 20 one-year-old male dairy goats in Example 2.

[0086] Figure 68 The Manhattan plot of net meat weight from a genome-wide association analysis of 20 one-year-old male dairy goats in Example 2.

[0087] Figure 69 The image shows the net meat weight QQ graph of 20 one-year-old male dairy goats from Example 2, based on genome-wide association analysis.

[0088] Figure 70 Manhattan plot of dressing percentage from genome-wide association analysis of 20 one-year-old male dairy goats in Example 2.

[0089] Figure 71 The QQ plot shows the dressing percentage of 20 one-year-old male dairy goats in Example 2, based on genome-wide association analysis.

[0090] Figure 72 Manhattan plot of net meat yield from genome-wide association analysis of 20 one-year-old male dairy goats in Example 2.

[0091] Figure 73The image shows the net meat yield QQ plot of a genome-wide association analysis of 20 one-year-old male dairy goats in Example 2.

[0092] Figure 74 Manhattan plot of carcass net meat percentage from genome-wide association analysis of 20 one-year-old male dairy goats in Example 2.

[0093] Figure 75 The image shows the QQ graph of carcass net meat yield from a genome-wide association analysis of 20 one-year-old male dairy goats in Example 2.

[0094] Figure 76 Manhattan plot of ocular muscle area from genome-wide association analysis of 20 one-year-old male dairy goats in Example 2.

[0095] Figure 77 The QQ plot of eye muscle area is a genome-wide association analysis of 20 one-year-old male dairy goats in Example 2.

[0096] Figure 78 Manhattan plot of GR values ​​from genome-wide association analysis of 20 one-year-old male dairy goats in Example 2.

[0097] Figure 79 The GR value QQ plot is shown for genome-wide association analysis of 20 one-year-old male dairy goats in Example 2.

[0098] Figure 80 Manhattan plot of back fat thickness from 20 one-year-old male dairy goats in Example 2, based on genome-wide association analysis.

[0099] Figure 81 The image shows the back fat thickness QQ plot of 20 one-year-old male dairy goats from Example 2, based on a genome-wide association analysis.

[0100] Figure 82 The Manhattan plot of tail weight from a genome-wide association analysis of 20 one-year-old male dairy goats in Example 2.

[0101] Figure 83 The tail weight QQ plot is a genome-wide association analysis of 20 one-year-old male dairy goats in Example 2.

[0102] Figure 84 The image shows the meat color L* Manhattan plot of the genome-wide association analysis of 20 one-year-old male dairy goats in Example 2.

[0103] Figure 85 The image shows the meat color L*QQ plot of the genome-wide association analysis of 20 one-year-old male dairy goats in Example 2.

[0104] Figure 86The image shows a Manhattan plot of flesh color (a*) from a genome-wide association analysis of 20 one-year-old male dairy goats in Example 2.

[0105] Figure 87 The image shows the meat color a*QQ plot of genome-wide association analysis of 20 one-year-old male dairy goats in Example 2.

[0106] Figure 88 The image shows a meat-colored b* Manhattan plot of genome-wide association analysis of 20 one-year-old male dairy goats from Example 2.

[0107] Figure 89 The image shows a b*QQ plot of meat color from a genome-wide association analysis of 20 one-year-old male dairy goats in Example 2.

[0108] Figure 90 The pH Manhattan plot is a genome-wide association analysis of 20 one-year-old male dairy goats in Example 2.

[0109] Figure 91 The pH-QQ plot is a genome-wide association analysis of 20 one-year-old male dairy goats from Example 2.

[0110] Figure 92 The Manhattan plot of drip loss from genome-wide association analysis of 20 one-year-old male dairy goats in Example 2.

[0111] Figure 93 The droplet loss QQ plot is shown in the genome-wide association analysis of 20 one-year-old male dairy goats in Example 2.

[0112] Figure 94 Manhattan plot of cooked meat percentage from genome-wide association analysis of 20 one-year-old male dairy goats in Example 2.

[0113] Figure 95 The QQ plot shows the cooked meat percentage from a genome-wide association analysis of 20 one-year-old male dairy goats in Example 2.

[0114] Figure 96 The shear force Manhattan plot is shown in the genome-wide association analysis of 20 one-year-old male dairy goats in Example 2.

[0115] Figure 97 The shear force QQ plot is shown in the genome-wide association analysis of 20 one-year-old male dairy goats in Example 2. Detailed Implementation

[0116] This invention provides a dairy goat breeding chip, which contains 81,107 SNP sites located in dairy goat reference genome version GCA_015443085.1. The location information of the SNP sites is shown in Table 5.

[0117] In this invention, GCA_015443085.1 is the NCBI sequence number of the reference genome. In this invention, blood samples from 208 Guanzhong dairy goats in their lactating period were used for whole-genome resequencing of the dairy goat population. The resequencing data were then used to screen for SNP sites based on deletion rate and retained dimorphic sites. The site screening criteria included: MAF value ≥ 0.05; deletion rate < 0.15; heterozygosity ≤ 0.5; sequencing depth ≥ 5. In one possible implementation, the chip contains probes designed with gene sequences covering the SNP sites. The chip is fabricated using in-situ synthesis, off-chip synthesis, or microbead method.

[0118] In this invention, as one possible implementation, during chip fabrication, each SNP containing 20-50 deoxynucleotides is coupled with a probe sequence to a specific microbead. The type of microbead is determined by the number of SNPs it carries, ranging from several thousand to tens of millions. Each type of microbead is encoded and detected by its specific address sequence and SNP probe sequence.

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

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

[0121] This invention also provides the application of the breeding chip in trait association analysis of dairy goats.

[0122] This invention also provides the application of the breeding chip in the construction of genetic maps and gene localization in dairy goats.

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

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

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

[0126] This invention also provides the application of the breeding chip in genome-wide association analysis of dairy goats.

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

[0128] This invention is the first to develop an 80K liquid phase breeding chip for dairy goats, which features high detection throughput, high marker coverage, genotype detection rate of over 99%, high genotype accuracy, and good stability. It can perform genome-wide association analysis on goat phenotypic and genotype data.

[0129] Unless otherwise specified, the experimental methods used in the following examples are all conventional molecular biology methods; the materials and reagents used are commercially available unless otherwise specified.

[0130] Example 1: Acquisition of whole-genome SNP loci and development of breeding chips in dairy goat populations

[0131] I. Whole-genome resequencing of a dairy goat population

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

[0133] Blood samples were collected and labeled from 208 Guanzhong dairy goats in their lactation period at the Shenda Ranch in Hohhot, Inner Mongolia.

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

[0135] The specific experimental procedure for constructing a DNA fragment library is as follows: Figure 1 As shown, the main steps include the following:

[0136] S1. DNA Sample Extraction and Quality Control: DNA was extracted from blood samples using a magnetic bead method. The concentration of DNA samples was detected using a Qubit quantitative PCR instrument; the integrity of DNA samples was detected by 1% agarose gel electrophoresis. Samples that passed quality control were used for library preparation.

[0137] S2. DNA Fragmentation, End Repair, and Quality Control: DNA samples were fragmented using dsDNA fragmentase, the cleavage ends were repaired, and an A base was added to the 3' end. The effectiveness of DNA fragmentation was assessed by 2% agarose gel electrophoresis; samples with a clear bright band at 300-500 bp were used for subsequent reactions.

[0138] S3. Adapter Ligation and Quality Control: Sequencing adapters were ligated to fragmented DNA using ligase, and the ligation products were purified using magnetic beads. The concentration of the purified products was detected using a Qubit real-time fluorescence analyzer, and samples with acceptable concentrations were used for subsequent reactions.

[0139] S4. Fragment Amplification, Selection, and Quality Control: The ligation products were amplified using PCR, and fragments were screened using magnetic beads. The fragment concentrations of the screened products were detected using a Qubit quantitative PCR instrument, fragment sizes were determined by 2% agarose gel electrophoresis, and the fragment sizes were verified using a Qsep400 bioanalyzer.

[0140] S5. Circularization and Quality Control: After denaturing the linear library into single strands, circularization is performed. Uncircularized linear DNA molecules are digested to obtain a single-stranded circular library. The concentration of the single-stranded circular library is detected using a Qubit quantitative PCR instrument. If the concentration is within acceptable limits, subsequent reactions can proceed.

[0141] S6. DNB Preparation and Quality Control: Single-stranded circular DNA molecules replicate via rolling circle to form a DNA nanosphere (DNB) containing more than 300 copies. The DNB concentration is detected using a Qubit fluorescence quantitative quantification system, and subsequent reactions proceed if the concentration is within acceptable limits.

[0142] S7. Sequencing: DNB is loaded into the sequencing chip using the MGIDL-T7 loading device, and the raw sequencing sequence is obtained by combining probe anchoring polymerization technology.

[0143] 2 Sequencing data quality control

[0144] The raw sequencing sequences obtained above contain some low-quality data, which can cause significant errors in the sequencing results. It is necessary to use specific software, FASTP v0.23.2, to filter the data and ensure the accuracy of the experimental results.

[0145] Results: After filtering with FASTP software, the sequencing data (clean data) from 208 Guanzhong dairy goats totaled 6317.42 Gb, with an average of 30.37 Gb per sample. The average Q30 of all samples was 94.97%. 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 filtered reads; Cleanbases: Total number of filtered bases; Cleanrate: Ratio of clean bases to 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 bases can be correctly identified determines the quality of sequencing. The sequencer itself, sequencing reagents, and samples all affect the quality of bases. FastQC v0.11.9 is used to generate a basic quality distribution map of sequencing data, which reflects the stability of sequencing quality during the sequencing process.

[0151] Results: Taking sample 000316 as an example, its basic mass distribution diagram is shown below. Figure 2 The horizontal axis represents the basic position of the filtered data, and the vertical axis represents the average basic quality value. The average basic quality value in the distribution chart is greater than 30, indicating that the sequencing data of sample 000316 is of relatively high quality. Analysis of other samples also shows that their basic quality is acceptable, indicating that the sequencing data from this experiment was very successful.

[0152] 4. Genetic Evolutionary Analysis

[0153] (1) Construction of phylogenetic tree

[0154] All obtained SNP loci were first screened according to a deletion rate ≤ 0.1% and an MAF value ≥ 0.05, retaining binary SNP loci located on chromosomes. After quality control, 15,511,550 SNP loci were obtained.

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

[0156] Results: A phylogenetic tree was constructed from the 15,511,550 SNP loci obtained after quality control. (See attached phylogenetic tree). Figure 3 The phylogenetic results show that the 208 Guanzhong dairy goat samples are closely related and have similar genetic characteristics.

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

[0158] Genetic distances between samples were calculated using the phylogenetic tree described above, and PCA cluster analysis was performed. The results are shown in [the table below]. Figure 4 .

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

[0160] (3) Group structure analysis

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

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

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

[0164] Using 208 lactating ewes and 30 breeding rams from the Shenda Ranch in Inner Mongolia as the subject, a detailed study was conducted on the data of these individuals, including their weight, body size, and milk production traits. 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 microarrays for dairy goat breeding.

[0165] 1. Genome-wide Association Study (GWAS)

[0166] (1) Filtering of variant sites

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

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

[0169] Table 2 Site Screening Criteria

[0170]

[0171]

[0172] Note: Population metrics are not considered for GWAS loci.

[0173] (2) GWAS analysis

[0174] Genome-wide association analysis (GWA) was performed on 238 dairy goat traits using rMVP v1.1.0. Three computational models were used: Generalized Linear Model (GLM), Mixed Linear Model (MLM), and FarmCPU model. Principal components were added as covariates to the models for calibration. GWA was performed on 16 phenotypic traits. These 16 phenotypic traits were: body height, body length, chest circumference, cannon bone circumference, body weight, chest width, chest depth, waist angle width, cross height, milk yield, milk fat percentage, milk protein percentage, lactose percentage, ash percentage, total dry matter ratio, and somatic cell count.

[0175] 2GWAS analysis results

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

[0177] Figures 7-15 Manhattan plots of nine traits, including body height and body length, for 208 dairy goats. Figures 16-24 QQ plots were generated for nine traits, including body height and body length, from 208 dairy goats. Body height, body length, chest girth, cannon bone girth, and body weight were based on data from 30 breeding rams and a combined dataset of 238 dairy goats. Loci significantly associated with a phenotype can be located if a locus is found in the plot; otherwise, it cannot be located.

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

[0179] Of the significant loci identified in the chest circumference trait, the GLM model had 1468 loci, the FarmCPU model had 197 loci, and the MLM model had 906 loci. SNP loci were mainly distributed on chromosomes 1, 2, 6, 8, and 14.

[0180] Of the significant loci identified for the chest depth trait, the GLM model had 219 loci, the FarmCPU model had 248 loci, and the MLM model had 70 loci. SNP loci were mainly distributed on chromosomes 1, 13, 17, 22, and 29.

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

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

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

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

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

[0186] Significant loci for the high trait in the cruciate region are found in the GLM model (107 loci), the FarmCPU model (87 loci), and the MLM model (23 loci). SNP loci are mainly distributed on chromosomes 1, 5, 8, and 22.

[0187] Among the significant loci for the wide waist angle trait, the GLM model had 199 loci, the FarmCPU model had 210 loci, and the MLM model had 108 loci. SNP loci were mainly distributed on chromosomes 3, 4, 16, 25, and 28.

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

[0189] Manhattan plots and QQ plots of loci significantly associated with milk production phenotypes are shown in [reference needed]. Figure 25 Genome-wide analysis of the 22-day average milk yield trait in dairy goats revealed multiple significant loci, including 231 GLM models, 231 FarmCPU models, and 23 MLM models. Candidate genes such as XXYLT1, GULP1, OLFM3, CD9, TTK, LHPP, and MYO10 were screened on chromosomes 2, 3, 9, 11, 12, and 16.

[0190] Based on the Manhattan plot and QQ plot of milk fat percentage traits, see Figure 26 Significant loci were located using three models: 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] Based on the Manhattan plot and QQ plot of the milk protein percentage trait, see Figure 27Significant loci were located using three models: 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] Based on the Manhattan plot and QQ plot of the lactose percentage trait, see Figure 28 Significant loci were located using three models: 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] Based on the Manhattan plot and QQ plot of ash content characteristics, see Figure 29 Significant loci were located using three models: 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] Based on the Manhattan plot and QQ plot of the total dry matter ratio, see Figure 30 Significant loci were located using three models: 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] Based on the Manhattan diagram and QQ diagram of somatic cell number traits, see Figure 31 Significant loci were located using three models: 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] III. Development and Verification of 80K Dairy Goat Breeding Chip

[0197] 1. SNP site types

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

[0199] Table 3 SNP site types

[0200] Site type SNP number Resequencing data 74712 GWAS sites 1627 Variety-specific sites 1030 Literature Collection of Functional Genes 3738 total 81107

[0201] 2. SNP locus information statistics

[0202] The statistical results of the SNP site information are shown in Table 4.

[0203] Table 4. SNP locus information statistics table

[0204]

[0205]

[0206] 3. Location information of SNP sites

[0207] The location information of 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]

[0319]

[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] A schematic diagram showing the distribution of SNP sites across chromosomes in the genome is shown below. Figure 32 ,pass Figure 32 It can be seen that SNP sites are distributed on all chromosomes and the distribution is relatively uniform, thus showing their uniform distribution throughout the genome.

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

[0358] Example 2: Application of 80K dairy goat breeding chips in dairy goat herds

[0359] Genome-wide association analysis was performed on the weight, body size, and slaughter traits of 20 one-year-old male dairy goats using a developed 80K dairy goat breeding chip.

[0360] 1. Data quality control results

[0361] 81,107 SNP loci obtained from 80K liquid-phase microarray sequencing of dairy goats were analyzed using Plink 1.9 according to the criteria of deletion rate ≤0.1%, MAF value ≥0.05, minimum allele frequency ≥0.05, and Hardy-Weinberg equilibrium P>1×10⁻⁶. -6 After performing data quality control according to the standard, 72,792 SNP loci were obtained from 20 individuals.

[0362] Visualize the deletion rate at individual loci, the individual deletion rate for each genotype, the minimum allele frequency, and the Hardy-Weinberg p-value, and plot statistical histograms, such as... Figure 33 As shown. Observation Figure 33 The overall distribution of the deletion rate in Figure a shows that most sites have a low deletion rate (less than 0.05). Observation Figure 33 The missing rate distribution of the samples in Figure b shows that most individuals have a low missing rate (less than 0.05), indicating good data quality. Figure 33 In Figure c, all SNPs with a minimum allele frequency of less than 5% screened out need to be filtered out. Figure 33 In the d-plot, P>1×10⁻⁶ is the result of the Hardy-Weinberg equilibrium test. -6 SNPs were identified. Samples with a detection rate of less than 95% were removed, and SNPs that did not meet quality standards were filtered out.

[0363] 2. Population structure analysis results

[0364] (1) Principal component analysis results

[0365] Principal component analysis was performed on the dairy goat population using Plink 1.9, following the same steps as described above. The analysis revealed that the sample population was relatively concentrated, and the first and second principal components showed that the differences between individuals within the dairy goat population were small. Figure 34 ).

[0366] (2) Phylogenetic tree construction results

[0367] Based on the quality-controlled SNP markers, the phylogenetic tree constructed using Plink 1.9 is as follows: Figure 35 As shown, the sample populations exhibit certain similarities in genetic characteristics and are closely related.

[0368] (3) LD attenuation analysis results

[0369] The results of LD decay analysis of dairy goat populations using PopLDdecay v1.5 are as follows: Figure 36 As shown, the difference in population diversity is judged by the LD decay rate. Generally, the LD decay rate of wild populations is faster than that of domesticated populations. The sufficiency of markers in GWAS association analysis is judged by comparing the LD decay distance (0.1) and the average distance between markers. Figure 36 The LD attenuation distance (0.1) and the average distance between markers indicate that there are enough markers in the GWAS analysis.

[0370] (4) Results of Kinship Analysis

[0371] A heatmap of the kinship matrix was obtained through GCTAv1.94.0 analysis of the kinship of 20 male dairy goats, as shown below. Figure 37 As shown, the heatmap of the kinship matrix uses different shades of color to represent the kinship distance between male dairy goats, with lighter colors representing more distant relationships. Therefore, from... Figure 37 The data shows that the samples are of the same variety and are closely related.

[0372] 3. Genome-wide association analysis results of goat traits

[0373] (1) Genomic association analysis of body weight

[0374] In the genome-wide association analysis results of body weight (e.g.) Figure 38 Manhattan chart of body weight Figure 39 In the weight QQ graph (as shown), five SNP loci with significant correlations at the genome-wide level were found, located on chromosomes 3, 4, 7, 10, and 16, respectively. Furthermore, Sahana et al. defined SNP loci with significant correlations at the chromosome-wide level as the standard for significant correlations at the chromosome-wide level, and the p-value corresponding to such significantly correlated SNP loci should be less than 10. -4(Sahana et al., 2010). Thus, 15 SNP loci that were significantly correlated at the chromosome level were obtained, located on chromosomes 2, 7, 8, 11, 15, 18, 20, 21, 24, and 28.

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

[0376] Genome-wide association analysis results of body height (e.g.) Figure 40 Manhattan height map, Figure 41 As shown in the body height QQ diagram, one SNP locus with significant correlation at the whole genome level was found, located on chromosome 20. There were 12 SNP loci with significant correlation at the chromosome level, located on chromosomes 4, 6, 8, 14, and 20, respectively.

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

[0378] Genome-wide association analysis results of in vivo oblique length (e.g.) Figure 42 Manhattan diagram of oblique length, Figure 43 As shown in the oblique QQ diagram, one SNP locus with significant correlation at the whole genome level was found, located on chromosome 21. There were eight SNP loci with significant correlation at the chromosome level, located on chromosomes 2, 3, 9, 12, 14, 21, and 27, respectively.

[0379] (4) Genome-wide association analysis of chest circumference

[0380] No SNPs significantly associated with chest circumference were found in the genome-wide association analysis (e.g., Figure 44 Manhattan bust chart Figure 45 (See the QQ chart for bust size). There are 14 SNP loci that are significantly correlated at the chromosomal level, located on chromosomes 3, 4, 6, 14, 15, 19, 20, 24, and 29.

[0381] (5) Deep thoracic genome-wide association analysis

[0382] Results of genome-wide association analysis in the deep chest region (e.g.) Figure 46 Manhattan-style chest view. Figure 47 In the chest QQ map (as shown), one SNP locus was found to be significantly associated at the whole genome level, located on chromosome 15. There were 13 SNP loci that were significantly associated at the chromosome level, located on chromosomes 7, 8, 20, 21, and 29, respectively.

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

[0384] In the genome-wide association analysis results of chest width (e.g.) Figure 48 Manhattan diagram of chest width Figure 49In the chest width QQ diagram (as shown), one SNP locus was found to be significantly associated at the whole genome level, located on chromosome 26. Nine SNP loci were found to be significantly associated at the chromosome level, located on chromosomes 3, 10, 13, 20, 25, 26, and 29, respectively.

[0385] (7) Genome-wide association analysis of hip width

[0386] In the genome-wide association analysis results of the buttock width (e.g. Figure 50 Wide buttocks Manhattan map, Figure 51 As shown in the QQ diagram, one SNP locus with significant correlation at the whole genome level was found, located on chromosome 13. Eight SNP loci with significant correlation at the chromosome level were found, located on chromosomes 3, 18, 19, and 25, respectively.

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

[0388] No SNPs significantly associated with natural length were found in genome-wide association analysis (e.g., Figure 52 Manhattan-style buttocks Figure 53 (As shown in the QQ diagram of the buttocks). There are 5 SNP loci that are significantly correlated at the chromosome level, located on chromosomes 1, 5, and 19, respectively.

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

[0390] In the genome-wide association analysis results of abdominal circumference (e.g. Figure 54 Manhattan waistline chart Figure 55 In the abdominal circumference QQ diagram shown, one SNP locus was found to be significantly associated at the whole genome level, located on chromosome 23. Eight SNP loci were found to be significantly associated at the chromosome level, located on chromosomes 6, 8, 10, 13, 27, and 29, respectively.

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

[0392] In the genome-wide association analysis results of waist angle width (e.g.) Figure 56 Manhattan diagram with waist angle width Figure 57 In the diagram (shown in the QQ diagram), one SNP locus was found to be significantly associated at the whole genome level, located on chromosome 21. There were 18 SNP loci that were significantly associated at the chromosome level, located on chromosomes 3, 5, 6, 10, 11, 12, 13, 14, and 27, respectively.

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

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

[0395] (12) Frontal width genome-wide association analysis

[0396] In frontal width genome-wide association analysis results (e.g. Figure 60 Manhattan diagram of forehead width Figure 61 In the frontal width QQ diagram (as shown), two SNP loci significantly correlated at the whole genome level were found, located on chromosomes 13 and 18, respectively. Three SNP loci significantly correlated at the chromosome level were found, located on chromosomes 5 and 17, respectively.

[0397] (13) Genome-wide association analysis of the piezoresistive system

[0398] No SNPs significantly associated with tube perimeter were found in genome-wide association analysis (e.g., Figure 62 Manhattan map of the pipe enclosure, Figure 63 (As shown in the QQ diagram). There are 16 SNP loci that are significantly correlated at the chromosome level, located on chromosomes 1, 3, 4, 7, 9, 10, 14, 20, 23, and 25.

[0399] (14) Genome-wide association study of pre-slaughter live weight

[0400] No SNPs significantly associated with pre-slaughter live weight were found in genome-wide association analysis (e.g. Figure 64 Manhattan map before slaughter Figure 65 (See the pre-slaughter live weight QQ diagram). There are three SNP loci that are significantly correlated at the chromosomal level, located on chromosomes 4, 5, and 10, respectively.

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

[0402] No SNPs significantly associated with carcass weight were found in genome-wide association analysis (e.g., Figure 66 Manhattan map of carcass weight. Figure 67 (Carcass weight QQ diagram shown). There are 10 SNP loci that are significantly correlated at the chromosomal 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 SNPs significantly associated with net meat weight were found in genome-wide association analysis (e.g., Figure 68 Manhattan weight of net meat Figure 69(See the QQ chart for net meat weight). There are 11 SNP loci that are significantly correlated at the chromosomal level, located on chromosomes 4, 5, 8, 10, 15, 19, 21, and 24.

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

[0406] No SNPs significantly associated with slaughter rate were found in genome-wide association analysis (e.g. Figure 70 Manhattan plot of slaughter rate Figure 71 (As shown in the QQ plot of dressing percentage). There are 7 SNP loci that are significantly correlated at the chromosome level, located on chromosomes 7 and 8 respectively.

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

[0408] In the genome-wide association analysis results of net meat yield (e.g. Figure 72 Manhattan plot of net meat percentage Figure 73 In the net meat yield QQ chart, four SNP loci with significant correlation at the whole genome level were found, located on chromosome 8. Nine SNP loci with significant correlation at the chromosome level were 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 of carcass net meat yield (e.g.) Figure 74 Manhattan plot of carcass net meat yield. Figure 75 In the carcass net meat yield (Q graph shown), one SNP locus with significant correlation at the whole genome level was found, located on chromosome 16. Two SNP loci with significant correlation at the chromosome level were found, located on chromosomes 5 and 9, respectively.

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

[0412] No SNPs significantly associated with extraocular muscle area were found in the genome-wide association analysis (e.g., Figure 76 Manhattan diagram of eye muscle area Figure 77 (See the QQ diagram of eye muscle area). There are 5 SNP loci that are significantly correlated at the chromosomal level, located on chromosomes 4, 5, 6, 7, and 18, respectively.

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

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

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

[0416] In genome-wide association analysis results for thick back fat (e.g.) Figure 80 Manhattan map of thick back fat. Figure 81 In the q diagram (shown in the dorsal fat thickness diagram), one SNP locus was found to be significantly associated at the whole genome level, located on chromosome 14. Ten SNP loci were found to be significantly associated at the chromosome level, located on chromosomes 5, 8, 12, 13, 18, 21, and 23, respectively.

[0417] (23) Tail weight genome-wide association analysis

[0418] No SNPs significantly associated with tail weight were found in the genome-wide association analysis of tail weight (e.g., ...). Figure 82 The Manhattan plot, Figure 83 (As shown in the tail weight QQ diagram). There are 9 SNP loci that are significantly correlated at the chromosome level, located on chromosomes 10, 11, 22, and 26, respectively.

[0419] (24) Genome-wide association analysis of flesh-colored L*

[0420] In the genome-wide association analysis results of flesh-colored L* (e.g. Figure 84 Flesh-colored L* Manhattan picture, Figure 85 As shown in the flesh-colored L*QQ diagram, three SNP loci were found to be significantly associated at the whole genome level, located on chromosomes 9, 11, and 25, respectively. Twelve SNP loci were found to be significantly associated at the chromosome level, located on chromosomes 4, 5, 7, 8, 9, 11, 24, 26, and 31, respectively.

[0421] (25) Genome-wide association analysis of flesh-colored a*

[0422] In the genome-wide association analysis results of flesh-colored a* (e.g. Figure 86 Flesh-colored A* Manhattan picture, Figure 87 As shown in the flesh-colored a*QQ diagram, one SNP locus with significant correlation at the whole genome level was found, located on chromosome 10. There were 15 SNP loci with significant correlation at the chromosome level, located on chromosomes 1, 2, 4, 6, 10, 13, 17, and 21, respectively.

[0423] (26) Genome-wide association analysis of flesh-colored b*

[0424] No SNPs significantly associated with flesh-colored b* were found in the genome-wide association analysis (e.g., ...). Figure 88 Flesh-colored b* Manhattan picture, Figure 89(As shown in the flesh-colored b*QQ image). There are 11 SNP loci that are significantly correlated at the chromosomal level, located on chromosomes 2, 4, 9, 13, 20, 25, 26, and 29.

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

[0426] No SNPs significantly associated with pH were found in the genome-wide association analysis of pH (e.g., Figure 90 pH Manhattan plot, Figure 91 (As shown in the pH QQ diagram). There are three SNP sites that are significantly correlated at the chromosome level, located on chromosomes 11 and 12, respectively.

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

[0428] No SNPs significantly associated with drip loss were found in the genome-wide association analysis of drip loss (e.g., ...). Figure 92 Manhattan diagram of dripping water loss Figure 93 (See the QQ diagram for water droplet loss). There are 8 SNP loci that are significantly correlated at the chromosomal level, located on chromosomes 4, 9, 17, 21, and 25, respectively.

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

[0430] No SNPs significantly associated with cooked meat yield were found in the genome-wide association analysis (e.g., ...). Figure 94 Manhattan plot of cooked meat percentage Figure 95 (As shown in the QQ graph of cooked meat percentage). There are 19 SNP loci that are significantly correlated at the chromosomal level, located on chromosomes 1, 3, 4, 7, 8, 9, 13, 19, 20, 25, and 27.

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

[0432] In the genome-wide association analysis results of shear force (e.g. Figure 96 Manhattan plot of shear force Figure 97 As shown in the shear force QQ plot, six SNP loci were found to be significantly associated at the genome-wide level, located on chromosomes 8, 9, 16, 24, and 27, respectively. Twelve SNP loci were found to be significantly associated at the chromosome level, located on chromosomes 5, 8, 10, 15, 24, 27, 28, and 29, respectively.

[0433] 4 candidate genes

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

[0435] Table 6 Candidate genes related to traits

[0436]

[0437]

[0438]

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

[0440] Although the above embodiments have provided a detailed description of the present invention, they are only some embodiments of the present invention, and not all embodiments. People can obtain other embodiments based on these embodiments without creative effort, and these embodiments all fall within the protection scope of the present invention.

Claims

1. A breeding chip for dairy goats, characterized in that, The dairy goat breeding chip contains probes designed with gene sequences covering SNP sites. The SNP sites include 81,107 SNP sites located in the dairy goat reference genome version GCA_015443085.

1. The location information of the SNP sites is shown in Table 5. The location information of the SNP sites in Table 5 is as follows:

2. The application of the breeding chip according to claim 1 in molecular marker-assisted breeding of dairy goats.

3. The application of the breeding chip according to claim 1 in trait association analysis of dairy goats.

4. The application of the breeding chip according to claim 1 in the construction of genetic maps and gene localization of dairy goats.

5. The application of the breeding chip according to claim 1 in gene analysis and / or screening of dairy goat breeding resources.

6. The application of the breeding chip according to claim 1 in genome-wide association analysis of dairy goats.

7. The application of the breeding chip according to claim 1 in the whole genome linkage analysis of dairy goats.

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