A genome-wide association analysis method for milk production traits in dairy goats and its application

Abstract

The present invention belongs to the field of biotechnology and specifically discloses a genome-wide association analysis method for milk production-related traits in dairy goats and its application. 330 ewes were randomly selected from two Xinong Saanen dairy goat populations as experimental subjects for genome-wide association analysis. Traits such as milk production, milk composition, and udder phenotype were collected. Blood samples were collected during lactation, and genomic DNA was extracted. The extracted genomic DNA was tested for integrity, purity, and concentration. 298 qualified samples were resequenced. Genome-wide association analysis was then performed on seven indicators of production-related traits in Xinong Saanen dairy goats to screen candidate genes, providing theoretical support for the future selection and breeding of new high-quality dairy goat varieties.

Description

Technical Field

[0001] The invention belongs to the technical field of biological breeding, and specifically discloses a genome-wide association analysis method for milk production-related traits of dairy goats and an application thereof. Background Art

[0002] The development of the dairy industry is an integral part of animal husbandry, and its progress is of great significance to the development of both animal husbandry and agriculture in my country. As one of my country's distinctive dairy industries, the goat milk industry is currently experiencing steady growth, but it also faces certain challenges, such as a lack of breeding stock and the degeneration of breeding stock. Milk production performance, primarily encompassing traits such as milk yield and milk composition, is a key indicator of the economic value of dairy goats. However, there is still a significant gap between the production performance of Chinese dairy goats and that of international dairy goats. To overcome this predicament and improve dairy goat production, genetic improvement and selection of existing breeds are essential. Genetic improvement of dairy livestock breeds relies on the discovery of key genes. With the advancement of science and technology, the application of genomic correlation technologies to study quantitative traits in plants and animals has become increasingly widespread and effective. Furthermore, through methods such as genomic selection, significant improvements in production performance can be achieved. Genome-wide association studies (GWAS) are increasingly being used in plant and animal breeding, and even in human disease research. They combine resequencing methods with statistical principles to identify candidate regions and genes associated with target traits across the entire genome. At present, there are few reports on genes related to milk production traits of dairy goats at home and abroad, and there is still a large research gap in the study of production-related traits of dairy goats using large-scale sequencing methods.

[0003] Genome-wide association analysis (GWAS) can provide breeders with a deeper understanding of the genetic mechanisms and key genes underlying dairy goat production performance, providing a scientific basis for dairy goat breed improvement and accelerating the breeding process. Therefore, the application of GWAS in dairy goat genetic breeding is urgently needed and has broad prospects, and the development of relevant GWAS methods is also in urgent need. Summary of the Invention

[0004] In response to the current technical problems, the present invention provides a genome-wide association analysis method for milk production traits of dairy goats and its application.

[0005] The present invention includes the following technical solutions:

[0006] A genome-wide association analysis method for milk production traits in dairy goats, comprising the following steps:

[0007] Step 1: Breeding and selection of experimental animals

[0008] The dairy goats were housed in a conventional manner, and several goats were randomly selected from a group of dairy goats in the 9-10 month lactation period as experimental subjects for whole genome resequencing;

[0009] Step 2: Sample collection and processing

[0010] Including sheep blood and sheep milk collection:

[0011] Blood was collected during lactation using EDTA-k2 anticoagulant tubes. After blood collection, the tubes were slowly inverted to mix thoroughly, allowing the blood and anticoagulant to mix thoroughly. The tubes were then placed in an insulated box containing ice packs and transported to the laboratory for storage at -80°C.

[0012] Goat milk is collected during lactation using preservative-filled centrifuge tubes, which are shaken and mixed thoroughly. After collection, the tubes are placed in an insulated box with ice packs and transported to the testing center.

[0013] Step 3: Milk production data collection and udder phenotype determination

[0014] The collected data included milk production, milk composition, and udder width and depth. Milk composition was measured monthly from the start of lactation, while milk production was recorded daily from the start of lactation. Udder phenotypes were measured using imageJ software from hindquarters photographs.

[0015] Step 4: Extraction and detection of genomic DNA

[0016] Genomic DNA was extracted using the cetyltrimethylammonium bromide method according to conventional extraction procedures. The extracted genomic DNA was tested for integrity, purity, and concentration. Those that met the requirements were retained, while those that did not meet the requirements were eliminated or re-extracted and tested;

[0017] Step 5: Sequencing data quality control and comparison with reference genome

[0018] The experiment was performed according to the standard protocol provided by Illumina. For qualified genomic DNA samples, appropriate size fragments were selected by gel electrophoresis, and then amplified by PCR to construct a library. The constructed library was then quality tested, and qualified libraries were sequenced by Illumina.

[0019] Step 6. Data processing and statistical analysis

[0020] 1) Descriptive statistical analysis of milk production-related traits

[0021] Descriptive statistical analysis of milk production-related traits was obtained using IBM SPss Statistics 21.0 software (Analysis - Descriptive Statistics). The production trait indicators analyzed included milk yield, lactose, milk fat, milk protein, udder width, udder depth, and udder base length.

[0022] The results of the analysis were sample size, mean, variance, standard deviation, and coefficient of variation;

[0023] 2) Genome-wide association analysis

[0024] After quality control and filling of the sequencing data, association analysis was performed using GMAT software to identify SNPs associated with milk production performance of Saanen goats:

[0025] y=Xβ+Zkγk+ξ+p+e (1)

[0026] In the formula, y is the phenotypic vector, Xβ is the population structure effect and the field and parity fixed effects, Zkγk is the marker effect to be tested, ξ~N(0,Kφ2) is the polygenic effect, p~N(0,Iσp2) is the system environment effect, and e~N(0,Iσ2) is the residual effect; K in the polygenic effect is the kinship matrix inferred by the markers;

[0027] 3) Group stratification

[0028] Draw QQ graphs for dairy goat production-related traits to determine whether there are biases and sample group stratification phenomena in the association analysis;

[0029] 4) Gene annotation of significant SNPs

[0030] After obtaining the significant SNPs site of the whole genome association analysis, the base sequence of 100kb upstream and downstream of the site was downloaded, and the sequence was compared with the NCBI and Ensembl databases to determine the genes near the significant SNPs site and screen out the genes closest to the site.

[0031] Furthermore, in the above-mentioned genome-wide association analysis method for milk production traits of dairy goats, the dairy goats are selected from Xinong Saanen dairy goats.

[0032] Furthermore, in the above-mentioned genome-wide association analysis method for milk production traits of dairy goats, the number of dairy goats in step 1 is 300-400, preferably 330.

[0033] Furthermore, in the above-mentioned genome-wide association analysis method for milk production traits of dairy goats, in the extraction and detection of genomic DNA in step 4, when sequencing on the Illumina sequencing system, base sequencing quality distribution analysis, base type distribution check and filtering of the original image data file obtained by high-throughput sequencing are required.

[0034] Furthermore, in the above-mentioned whole-genome association analysis method for milk production traits of dairy goats, in step 5, the sequencing data quality control and comparison statistics with the reference genome are performed, and the final sequence obtained by sequencing is relocated to the reference genome, and then subsequent analysis is performed; the ratio of Clean-Reads that can be located on the reference genome to the total Clean-Reads is called the comparison efficiency; the reference gene species is the Saanen dairy goat.

[0035] Furthermore, in the above-mentioned whole-genome association analysis method for milk production traits of dairy goats, in step 6, 3) the production-related traits of dairy goats in population stratification are composed of 11 indicators: milk production CNL, milk fat Z, milk protein D, lactose T, somatic cell count TX8, udder width RFK, udder depth SD, udder bottom plane DPM, number of lambs born CG5, number of live lambs CHGS and average litter weight PJWZ.

[0036] Furthermore, the above-mentioned whole-genome association analysis method for milk production traits of dairy goats detected a total of 51 SNPs significantly associated with milk production across the whole genome, 28 SNPs significantly associated with milk composition, and 475 SNPs significantly associated with mammary phenotypes. The genes found near these sites include: ZRANB2, TIMP1, SLIT3, and BCL2.

[0037] Furthermore, the above-mentioned genome-wide association analysis method for milk production traits of dairy goats is used in dairy goat breeding.

[0038] Furthermore, the present invention also discloses the use of several genes in dairy goat breeding, wherein the genes include one or more of ZRANB2, TIMP1, SLIT3, and BCL2.

[0039] The present invention has the following beneficial effects:

[0040] The present invention provides a genome-wide association analysis method for milk production traits of dairy goats. 330 ewes were randomly selected from two Xinong Saanen dairy goat groups as experimental subjects for the genome-wide association analysis. Relevant traits such as milk production, milk composition, and udder phenotype were collected. Blood samples were collected during lactation, and genomic DNA was extracted. The integrity, purity, and concentration of the extracted genomic DNA were tested, and 298 qualified samples were finally resequenced. Then, genome-wide association analysis was performed on seven indicators of production-related traits of Xinong Saanen dairy goats, and candidate genes ZRANB2, TIMP1, SLIT3, and BCL2 were screened. This method provides a powerful method and practical support for the future selection and breeding of new high-quality dairy goat varieties. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1This is a Manhattan plot of the results of the whole-genome association analysis of milk production; the abscissa represents chromosomal position, the ordinate represents the -log of the association value, each dot represents a single nucleotide polymorphism (SNP), and the upper horizontal line represents the significance threshold. (Except for the mammary traits of udder width, depth, and base plane, independent SNPs were extracted using the PLINK command --indep-pai rwi se 100 10 0.2, and the significance threshold was calculated as 1 / number of independent SNPs. For the mammary traits of udder width, depth, and base plane, the threshold was determined using 1000 permutation tests.)

[0042] Figure 2 This is the Manhattan plot of the milk fat gene-wide association analysis results;

[0043] Figure 3 This is the Manhattan plot of the milk protein gene-wide association analysis results;

[0044] Figure 4 This is the Manhattan plot of the results of the lactose gene-wide association analysis;

[0045] Figure 5 This is the Manhattan plot of the results of the whole gene association analysis of breast width;

[0046] Figure 6 This is the Manhattan plot of the results of the deep whole-gene association analysis for breast cancer;

[0047] Figure 7 This is the Manhattan plot of the results of the whole-gene association analysis of the breast base plane;

[0048] Figure 8 is a flow chart of the present invention;

[0049] Figure 9 This is the QQ plot result of milk production;

[0050] Figure 10 This is the QQ plot result of milk fat;

[0051] Figure 11 This is the QQ plot result of milk protein;

[0052] Figure 12 This is the QQ plot result of lactose;

[0053] Figure 13 This is the QQ plot result of breast width;

[0054] Figure 14 This is the QQ map result of breast depth;

[0055] Figure 15 This is the QQ map result of the breast bottom plane;

[0056] Figure 16This is the result before SNP quality control; DETAILED DESCRIPTION

[0057] To make the objectives, technical solutions, and advantages of the present invention more clearly understood, the present invention is further described in detail below with reference to the accompanying drawings and examples. However, it should be understood that the specific embodiments described herein are merely illustrative of the present invention and are not intended to limit the scope of the present invention. In addition, in the following description, descriptions of well-known structures and technologies are omitted to avoid unnecessary confusion of the present invention.

[0058] Example

[0059] 1 Materials and Methods

[0060] 1.1 Experimental Materials

[0061] 1.1.1 Experimental animals

[0062] The experiment used Xinong Saanen dairy goats, which came from the original breeding farm of Northwest A&F University and Zhejiang Baoyuan Animal Husbandry Co., Ltd. in Hutang Village, Daciyan Town, Jiande City, Hangzhou City, Zhejiang Province. 118 and 212 were randomly selected respectively, for a total of 330 goats as experimental subjects for whole genome resequencing.

[0063] 1.1.2 Sample collection and processing

[0064] Sample collection mainly involves blood and goat milk collection. Blood is collected during lactation using EDTA-k2 anticoagulant tubes (PET). After blood collection, the anticoagulant tubes must be slowly turned upside down to mix thoroughly so that the blood and anticoagulant are fully mixed. The anticoagulant tubes containing blood are placed in an insulated box containing ice packs. After being transported to the laboratory, they are stored in a -80°C refrigerator for later use. Milk components are collected during lactation using centrifuge tubes that have been pre-preserved with preservatives, and shaken to mix. After collection, the centrifuge tubes are placed in an insulated box containing ice packs and transported to the testing center.

[0065] 1.2 Experimental methods

[0066] 1.2.1 Production performance data recording and udder phenotype determination

[0067] Initial data collection focused on milk production and milk composition. Udder phenotyping was performed on 330 Saanen goats during the lactation period, with udder phenotypes photographed and recorded before milking each day. This data was then measured using ImageJ software. Milk composition was determined using the same methods as described in Chapter 2. Milk production was accurately recorded daily from the start of lactation. After this data collection and screening, the final experimental subjects were 298 Xinong Saanen goats.

[0068] 1.2.2 Experimental technical route Figure 8 shown

[0069] 1.2.3 Extraction of genomic DNA

[0070] Genomic DNA was extracted using the cetyl trimethyl ammonium bromide (CTAB) method according to conventional extraction procedures. The extracted genomic DNA was tested for integrity, purity, and concentration. Those that met the requirements were retained, while those that did not meet the requirements were eliminated or re-extracted and tested.

[0071] 1.2.4 Sequencing data quality control and comparison with reference genome

[0072] The experiment was performed according to the standard protocol provided by Illumina. For qualified genomic DNA samples, appropriate size fragments were selected by gel electrophoresis. Then, libraries were amplified and constructed using PCR. The constructed libraries were quality-tested, and qualified libraries were sequenced using Illumina sequencing, performed by Beijing Compson Agricultural Technology Co., Ltd. To ensure the quality of information analysis, base sequencing quality distribution analysis and base type distribution were performed during sequencing on the Illumina sequencing system, as well as filtering of the raw image data (Raw Reads) files generated by high-throughput sequencing.

[0073] The final sequence obtained by sequencing is remapped to the reference genome for subsequent analysis. The ratio of clean reads that can be mapped to the reference genome to the total clean reads is called the alignment efficiency, eMapped (%). The reference genome species is the Xinong Saanen dairy goat, and the reference genome is not currently available.

[0074] 1.3 Data processing and statistical analysis

[0075] 1.3.1 Descriptive statistical analysis of milk production-related traits

[0076] Descriptive statistical analysis of milk production-related traits was obtained using the IBM SPSS Statistics 21.0 software Analysis-Descriptive Statistics Analysis. The production trait indicators analyzed included milk yield, lactose, milk fat, milk protein, udder width, udder depth, and udder base length. The analysis results included sample number (N), mean (Mean), variance (V), standard deviation (SD), and coefficient of variation (CV).

[0077] 1.3.2 Genome-wide association analysis

[0078] After the sequencing data were quality controlled and filled, GMAT software was used for association analysis to mine SNPs related to the milk production performance of Saanen dairy goats.

[0079] y=Xβ+Zkγk+ξ+p+e (1)

[0080] In the formula, y is the phenotypic vector, Xβ is the population structure effect and fixed effects such as field and parity, Zkγk is the marker effect to be tested, ξ~N(0,Kφ²) is the polygenic effect, p~N(0,Iσp²) is the system-environment effect, and e~N(0,Iσ²) is the residual effect. K in the polygenic effect is the kinship matrix inferred from the markers.

[0081] During data analysis, because the threshold calculated using the Bonferroni method (0.05 / number of SNPs) was too strict, independent SNPs were extracted using PLINK with the --indep-pairwise 100 100.2 parameter, with significance thresholds calculated as 1 / number of independent SNPs, except for breast width, depth, and base plane. For breast width, depth, and base plane, thresholds were determined using 1000 permutation tests.

[0082] 1.3.3 Group stratification

[0083] Population stratification refers to the difference in allele frequencies due to different ancestries, which has been proven to be a confounding factor and may lead to many false positive results. Therefore, when conducting an association analysis on the egg-laying performance of Shaoxing ducks, a QQ plot (Quantile-Quantile Plot) was drawn for 11 indicators of Saanen dairy goat production performance to determine whether there was any deviation in the association analysis and stratification of the sample population.

[0084] 1.3.4 Gene annotation of significant SNPs

[0085] After obtaining the significant SNPs site of the whole genome association analysis, the base sequence of 100kb upstream and downstream of the site was downloaded, and the sequence was compared with the NCBI and Ensembl databases to determine the genes near the significant SNPs site and screen out the genes closest to the site.

[0086] 2. Results Analysis

[0087] 2.1 Genomic DNA test results

[0088] All genomic DNA extracted from samples needs to be tested for quality by agarose gel electrophoresis. Some genomic DNA test results are as follows: Figure 1As shown, it is required that the electrophoresis sample loading wells are clean and free of contamination, the main bands are clear, and there is no trailing. In addition, for the purity detection of DNA, 1.6 < OD260 / OD280 < 2.0 and 1.8 < OD260 / OD230 < 2.1 are required. The detection results of genomic DNA must meet the above requirements simultaneously before constructing a library, etc. Unqualified samples need to be discarded or the DNA needs to be extracted again. After detection, a total of 298 samples in this experiment met the qualified requirements.

[0089] 2.2 Quality control of sequencing data and phenotypic quality control

[0090] The sequencing data is quality controlled according to the following conditions: loci on non-autosomes and non-SNPs; loci with a deletion rate > 10%; loci with a minor allele frequency < 0.05; P values of the Hardy-Weinberg test

[0091] < 0.000001; detailed quality control information is shown in Table 1.

[0092] Table 1 Quality control results of sequencing data

[0093]

[0094] 2.3 Statistical analysis of SNP detection results between samples and reference genomes

[0095] Mainly, the chromosomal distribution and quantity of SNPs before and after quality control are statistically analyzed. The results are shown in Figure 16 .

[0096] 2.4 Descriptive statistical analysis of production traits

[0097] The results of the descriptive statistical analysis of production traits are shown in Table 2. From the results of the descriptive statistical analysis, it can be seen that among the 298 individuals analyzed, the average daily milk production is 1.24 kg, the average udder width is 11.89 cm, the average udder depth is 15.57 cm, and the average width of the udder bottom plane is 8.17 cm.

[0098] Table 2 Descriptive statistics of production traits

[0099]

[0100] 2.5 Results of genome-wide association analysis

[0101] In this experiment, the repeatability mixed linear model, general mixed linear model, and Logistic model of the GMAT software were used to conduct association analysis respectively, and a genome-wide association analysis of the production performance of 298 Xinong Sannen dairy goats (298 individuals who had their genomic DNA extracted and met the quality requirements) was carried out. SNPs significantly associated with milk production, milk composition, and udder phenotype were identified across the genome.

[0102] 2.5.1 Results of genome-wide association analysis of milk production

[0103] The results of some SNPs in the genome-wide association analysis of milk production are shown in Table 3 , and the corresponding Manhattan plots are shown in Figure 1 There are 50 SNPs that are significantly correlated with milk production, mainly located on multiple chromosomes such as Chr3, Chr16, Chr18, and Chr22, and 25 related genes.

[0104] Table 3 Results of genome-wide association analysis of milk production (partial)

[0105]

[0106] 2.5.2 Results of genome-wide association analysis of milk components

[0107] The results of some SNPs in the genome-wide association analysis of milk production are shown in Table 4 , and the corresponding Manhattan plots are shown in Figure 2-4 There are 28 SNPs that are significantly correlated with milk components (milk fat, milk protein, and lactose), which are mainly located on multiple chromosomes such as Chr3, Chr6, and Chr15.

[0108] Table 4 Results of genome-wide association analysis of milk components (partial)

[0109]

[0110] 2.5.3 Results of genome-wide association analysis of breast phenotypes

[0111] The results of some SNPs in the genome-wide association analysis of milk production are shown in Table 5 , and the corresponding Manhattan plots are shown in Figure 5-7 There are 475 SNPs that are significantly associated with breast phenotypes (breast width, breast depth, and breast base plane), which are mainly located on multiple chromosomes such as Chr7, Chr8, Chr9, and Chr24.

[0112] Table 5 Results of genome-wide association analysis of breast phenotypes (partial)

[0113]

[0114] 2.6 Group stratification assessment results

[0115] All the traits studied have significant SNPs sites, and their QQ plots are shown in the following figure: Figures 9-15 As shown, the horizontal axis represents the expected value and the vertical axis represents the observed value. The thin line in the figure represents the 45° line, which is the predicted threshold. The gray area is the 95% confidence interval of the scattered points on the figure. The farther the SNP is from the solid line, the better the association strength. Figure 7-11As can be seen, most of the loci in the lower left corner of the graph are on the diagonal line, indicating that the model selection is reasonable. The loci in the upper right corner that exceed the diagonal line and the confidence interval are highly significant with the target trait. In summary, this shows that there is no population stratification in the experimental population.

[0116] 2.7 Gene annotation of genome-wide significantly associated SNPs

[0117] Through GWAS, 50 SNPs were found to be significantly correlated with milk production, mainly distributed on chromosomes chr3, chr18, and chr22. Preliminary gene annotation of these sites was performed using NCBI and Ensembl. Some of the annotation results are detailed in Table 6.

[0118] Table 6 Annotation results of genome-wide association analysis of milk production (partial)

[0119]

[0120] There are 28 SNPs that are significantly correlated with milk components (milk fat, milk protein, and lactose), which were annotated using the same method. Some of the annotation results are detailed in Table 7.

[0121] Table 7 Annotation results of genome-wide association analysis of milk components (partial)

[0122]

[0123] There are 475 SNPs that are significantly associated with breast phenotypes (breast width, breast depth, and breast base plane), which were annotated using the same method. Some of the annotation results are detailed in Table 8.

[0124] Table 8 Annotation results of genome-wide association analysis of breast phenotypes (partial)

[0125]

[0126] 3 Conclusion

[0127] 3.1 Production trait phenotype of Xinong Saanen dairy goats

[0128] The initial sample size for this experiment was 330 Xinong Saanen goats. These 330 goats were randomly selected from two large herds housed in barns. Milk production was recorded twice daily, in the morning and afternoon. To ensure data authenticity and accuracy, individuals with fewer than ten milk production records were eliminated. To reduce human error, all udder phenotype measurements were performed by the same person. Milk production was recorded daily for each goat starting at 7:00 AM from the start of calving. The data were 1:1 with electronic and paper versions, and milk production conformed to basic production patterns, ensuring the data source was reliable. Genomic DNA was extracted and tested for integrity, purity, and concentration, resulting in a total of 298 goats.

[0129] Currently, GWAS studies in dairy livestock primarily focus on dairy cows, leaving a significant gap in research on dairy goats. Therefore, we chose to conduct a genome-wide association study on milk yield, milk composition, and mammary phenotypes related to production in Xinong Saanen dairy goats. By selecting for these traits, we aim to improve production performance, address the rate-limiting factors affecting genetic improvement, and identify key genes associated with milk production across the entire genome.

[0130] 3.2 Gene annotation of significantly associated SNPs

[0131] In this study, we detected a total of 50 SNPs significantly associated with milk production. Eleven SNPs on chromosome 3 were significantly associated with milk production, including one located between the ZRANB2 and NEGR1 genes, six located upstream of the LRRIQ3 gene, and four associated with the FPGT-TNNI3K gene. Twenty-eight SNPs were significantly associated with milk components (milk fat, milk protein, and lactose), five with milk fat, eight with milk protein, and 15 with lactose. A total of 475 SNPs were significantly associated with mammary phenotypes (breast width, depth, and mammary base plane), including 217 with mammary width, 185 with mammary depth, and 73 with mammary base plane.

[0132] 3.2.1 ZRANB2 gene

[0133] The ZRANB2 gene, short for Zinc Finger Ran-Binding Domain-Containing Protein 2, encodes a protein with RNA-binding activity predicted to be involved in RNA splicing and mRNA processing. In a study of human breast cancer, Iris et al. identified 1,723 AS events and 41 splicing factors regulated in a breast cancer model with acquired resistance to doxorubicin. An RNAi screen for splicing factors identified the poorly characterized ZRANB2 and SYF2, whose depletion partially reversed doxorubicin resistance. Using RNAi and RNA-seq in resistant cells, we found that the AS program controlled by ZRANB2 and SYF2 was enriched in resistance-associated AS events and converged on an ECT2 splice variant encompassing exon 5 (ECT2-Ex5+). Both ZRANB2 and SYF2 were associated with ECT2 pre-messenger RNA, and depletion of the ECT2-Ex5+ isoform reduced doxorubicin resistance. In conclusion, we identify alternative splicing programs controlled by ZRANB2 and SYF2 that converge on ECT2 and are involved in the response to drug resistance in breast cancer.

[0134] 3.2.2TIMP1 gene

[0135] The full name of the TIMP1 gene is TIMP Metallopeptidase Inhibitor 1, which belongs to the TIMP gene family. The proteins encoded by this gene family are natural inhibitors of matrix metalloproteinases (MMPs), a group of peptidases involved in the degradation of the extracellular matrix. In addition to its inhibitory effect on most known MMPs, the encoded protein can also promote cell proliferation in a variety of cell types and may also have anti-apoptotic functions. Anieta et al. evaluated the prognostic value of TIMP1 mRNA and novel TIMP1 mRNA splice variants in 1,301 patients with primary breast cancer. It was found that while high concentrations of TIMP-1 protein were associated with poor prognosis, high concentrations of TIMP1-v1+2 mRNA were associated with a good prognosis in patients with primary breast cancer.

[0136] 3.2.3SLIT3 gene

[0137] The SLIT3 gene, short for Slit Guidance Ligand 3, encodes a protein that may interact with the CIR homolog receptor to influence cell migration. Dickinson et al. found frequent promoter hypermethylation and transcriptional silencing of SLIT2 in lung, breast, colorectal, and glioma cell lines, as well as in primary tumors. SLIT3 was methylated in 29 breast cancer cell lines.

[0138] 3.2.4 BCL2 gene

[0139] The full name of the BCL2 gene is BCL2 Apoptosis Regulator, which is a protein-coding gene. This gene encodes an integral outer mitochondrial membrane protein that prevents apoptosis in certain cells (such as lymphocytes). Its related pathways include MIF-mediated glucocorticoid regulation and TGF-β pathway. Strange et al. found in their study of mammary gland development and breast cancer that

[0140] Activation of Akt1 by hormone- or anchor-mediated pathways regulates mammary epithelial survival and contributes to tumor initiation. Akt1's mechanism of action involves regulating the ratio of Bcl-2 family members involved in the control of apoptosis. Bcl-2 family proteins are also expressed in a pattern consistent with Akt1 regulation.

[0141] These genes are all close to or within significant SNPs sites, and are all related to human breast cancer or mammary gland function regulation. Although there has been no specific research in dairy goats to prove the correlation with goat mammary gland development, analysis of their location and function suggests that there may be a certain correlation with milk production performance, which can be further verified through subsequent experiments.

[0142] The results of genome-wide association analysis showed that a total of 50 SNPs were detected across the genome that were significantly correlated with milk production, 28 SNPs were significantly correlated with milk components (milk fat, milk protein, lactose), and 475 SNPs were significantly correlated with breast phenotypes (breast width, breast depth, and breast bottom plane). Candidate genes such as ZRANB2, TIMP1, SLIT3, and BCL2 were found near these sites.

[0143] Based on the above examples, the present invention discloses a genome-wide association analysis method for milk production-related traits in dairy goats. 330 ewes were randomly selected from two Xinong Saanen dairy goat populations as experimental subjects for genome-wide association analysis. Traits such as milk production, milk composition, and mammary phenotype were collected. Blood samples were collected during lactation, and genomic DNA was extracted. The extracted genomic DNA was tested for integrity, purity, and concentration. 298 qualified samples were resequenced. Genome-wide association analysis was then performed on seven indicators of production-related traits in Xinong Saanen dairy goats to screen candidate genes, providing theoretical and practical support for the future selection and breeding of new high-quality dairy goat varieties.

[0144] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the scope of protection of the present invention. Therefore, based on the innovative concept of the present invention, changes and modifications to the embodiments described herein, or equivalent structural or equivalent process transformations made using the contents of the present invention's description and drawings, and direct or indirect application of the above technical solutions to other related technical fields, are all included in the scope of protection of the patent of the present invention.

Claims

1. A genome-wide association analysis method for milk production traits in dairy goats, characterized in that: The following steps are involved: Step 1: Breeding and selection of experimental animals The dairy goats were housed in a conventional manner, and several goats were randomly selected from a group of dairy goats in the 9-10 month lactation period as experimental subjects for whole genome resequencing; Step 2: Sample collection and processing Including sheep blood and sheep milk collection: Blood was collected during lactation using EDTA-k2 anticoagulant tubes. After blood collection, the tubes were slowly inverted to mix thoroughly, allowing the blood and anticoagulant to mix thoroughly. The tubes were then placed in an insulated box containing ice packs and transported to the laboratory for storage at -80°C. Goat milk is collected during lactation using preservative-filled centrifuge tubes, which are shaken and mixed thoroughly. After collection, the tubes are placed in an insulated box with ice packs and transported to the testing center. Step 3: Milk production data collection and udder phenotype determination Data collected included two phenotypes: milk production, milk composition, and udder width and udder depth; Milk composition was measured once a month after the start of lactation; milk production was recorded daily from the start of lactation; and udder phenotype was measured using imageJ software on hindquarters photographs. Step 4: Extraction and detection of genomic DNA Genomic DNA was extracted using the cetyltrimethylammonium bromide method according to conventional extraction procedures. The extracted genomic DNA was tested for integrity, purity, and concentration. Those that met the requirements were retained, while those that did not meet the requirements were eliminated or re-extracted and tested; Step 5: Sequencing data quality control and comparison with reference genome The experiment was performed according to the standard protocol provided by Illumina. For qualified genomic DNA samples, appropriate size fragments were selected by gel electrophoresis, and then amplified by PCR to construct a library. The constructed library was then quality tested, and qualified libraries were sequenced by Illumina. Step 6. Data processing and statistical analysis 1) Descriptive statistical analysis of milk production-related traits Descriptive statistical analysis of milk production-related traits was obtained using IBM SPss Statistics 21.0 software (Analysis - Descriptive Statistics). The production trait indicators analyzed included milk yield, lactose, milk fat, milk protein, udder width, udder depth, and udder base length. The results of the analysis were sample size, mean, variance, standard deviation, and coefficient of variation; 2) Genome-wide association analysis After quality control and filling of the sequencing data, association analysis was performed using GMAT software to identify SNPs associated with milk production performance of Saanen goats: y=Xβ+Zkγk+ξ+p+e (1) In the formula, y is the phenotypic vector, Xβ is the population structure effect and the field and parity fixed effects, Zkγk is the marker effect to be tested, ξ~N(0,Kφ2) is the polygenic effect, p~N(0,Iσp2) is the system environment effect, and e~N(0,Iσ2) is the residual effect; K in the polygenic effect is the kinship matrix inferred by the markers; 3) Group stratification Draw QQ graphs for dairy goat production-related traits to determine whether there are biases and sample group stratification phenomena in the association analysis; 4) Gene annotation of significant SNPs After obtaining the significant SNPs site of the whole genome association analysis, the base sequence of 100kb upstream and downstream of the site was downloaded, and the sequence was compared with the NCBI and Ensembl databases to determine the genes near the significant SNPs site and screen out the genes closest to the site.

2. A genome-wide association analysis method for milk production traits in dairy goats according to claim 1, characterized in that: The dairy goats are selected from Xinong Saanen dairy goats.

3. A genome-wide association analysis method for milk production traits in dairy goats according to claim 1, characterized in that: The number of dairy goats in step 1 is 300-400, preferably 330.

4. A genome-wide association analysis method for milk production traits in dairy goats according to claim 1, characterized in that: In the extraction and detection of genomic DNA in step 4, when sequencing using the Illumina sequencing system, base sequencing quality distribution analysis, base type distribution check, and filtering of the raw image data files obtained by high-throughput sequencing are required.

5. A genome-wide association analysis method for milk production traits in dairy goats according to claim 1, characterized in that: In step 5, sequencing data quality control and comparison with the reference genome statistics, the final sequence obtained by sequencing is relocated to the reference genome for subsequent analysis. The ratio of clean reads that can be mapped to the reference genome to the total clean reads is called the comparison efficiency. The reference gene species is the Saanen dairy goat.

6. A genome-wide association analysis method for milk production traits in dairy goats according to claim 1, characterized in that: In step 6, 3) the production-related traits of dairy goats in the population stratification are composed of 11 indicators: milk yield CNL, milk fat Z, milk protein D, lactose T, somatic cell count TX8, udder width RFK, udder depth SD, udder bottom plane DPM, number of lambs born CG5, number of lambs born alive CHGS and average litter weight PJWZ.

7. A genome-wide association analysis method for milk production traits in dairy goats according to claim 1, characterized in that: A total of 51 SNPs significantly associated with milk production were detected across the entire genome, 28 SNPs significantly associated with milk composition, and 475 SNPs significantly associated with breast phenotypes. The genes found near these sites include: ZRANB2, TIMP1, SLIT3, and BCL2.

8. Use of the analytical method according to any one of claims 1 to 7 in dairy goat breeding.

9. Use of several genes in dairy goat breeding, characterized in that: The genes include one or more of ZRANB2, TIMP1, SLIT3, and BCL2.

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