Method for screening dairy cow milk production performance and blood character markers and application

Abstract

The invention discloses a method for screening dairy cow milk production performance and blood character markers and application. The method comprises the following steps: performing GWAS preliminary screening by a FarmCPU method, performing GWAS analysis on a gene level, constructing a geneHapR haplotype module, and performing correlation analysis. According to the present invention, it is found for the first time that four modules exist in the LOC112441463 gene of the chromosome 15, wherein the modules Block2 and Block4 are significantly related to the number of lymphocytes and the milk yield of 305 days; h001 (CT / TA) in Block2 and H001 (GT / AC) in Block4 are haplotype dominant allelotype, the number of lymphocytes carrying haplotype individuals is relatively low, but the milk yield in 305 days is relatively high. Therefore, the H001 haplotype in the two modules of the LOC112441463 gene can be used as a molecular marker for disease-resistant breeding, so that the milk production performance of dairy cows is improved.

Description

Technical Field

[0001] This invention belongs to the field of molecular biology technology and relates to methods and applications for screening markers for milk production performance and blood traits in dairy cows. Background Technology

[0002] Dairy cows are an important dairy-producing livestock breed in my country, and their milk is of excellent quality and high nutritional value, making it popular with consumers. Improving milk quality and yield has always been a focus of attention for breeders. Studies have found a highly significant negative correlation between somatic cell count in cow milk and milk yield (r = -0.514, P < 0.01), indicating that the lower the somatic cell count in milk, the higher the milk yield (Zhang Huilin et al., 2010). Somatic cells in cow milk are composed of macrophages, lymphocytes, and polymorphonuclear neutrophils (PMNs), with lymphocytes accounting for 25% of SCCs and macrophages accounting for 60%, working together to maintain immune surveillance function. Blood is the main source of lymphocytes in milk, specifically meaning that milk lymphocytes mainly originate from the blood and migrate to mammary gland tissue guided by specific chemokines. In other words, the number of lymphocytes in the blood reflects the health status of an individual dairy cow to some extent. An abnormal increase in lymphocytes is also accompanied by an increase in the number of somatic cells in milk, thus affecting the milk quality and milk yield of the dairy cow. Clearly, effectively improving milk yield and quality has become a crucial issue for the development of my country's dairy industry. However, there are still relatively few effective markers for milk yield and quality in dairy cows. Therefore, identifying relevant markers for milk production and blood traits in dairy cows is of great significance for improving milk yield per cow and milk quality.

[0003] A key challenge in dairy cow molecular breeding lies in the precise localization of quantitative trait loci (QTLs) associated with important traits. Currently, the primary method for QTL localization in dairy cows relies on genome-wide association studies (GWAS), which has become the mainstream approach for genetic analysis of important economic traits. GWAS can identify SNP markers for known genes and screen for SNP markers for unknown genes. However, its limitation is that most SNP markers identified by GWAS are located between genomes or have small genetic effects and low reproducibility, making them difficult to further utilize in breeding. Against this backdrop, this invention will utilize gene-level association analysis, linkage disequilibrium analysis, and haplotype association analysis to precisely localize target traits based on GWAS results. The QTLs obtained through this screening method have higher accuracy and reliability, addressing the current shortage of QTLs for milk production and blood traits in dairy cows and gradually meeting the needs of dairy cow molecular breeding. Summary of the Invention

[0004] The purpose of this invention is to provide a method and application for screening markers for milk production performance and blood traits in dairy cows. Through GWAS, gene-level association analysis, linkage disequilibrium analysis, and haplotype association analysis, the invention accurately locates QTLs related to milk production performance and blood traits in dairy cows, laying the foundation for future practices in improving milk production performance and milk quality, as well as for breeding programs.

[0005] To achieve the above objectives, the technical solution adopted by the present invention is as follows:

[0006] A QTL for identifying the number of blood lymphocytes in dairy cows and milk yield over 305 days, the QTL comprising two QTLs;

[0007] One of them is located on chromosome 15, between positions 5870150 and 5871137 in the ARS-UCD2.0 version reference sequence of the dairy cow genome, with a length of 0.988Kb and a nucleotide sequence of SEQ ID NO.1;

[0008] Part Two: Located between 5873277 and 5873351 on chromosome 15 of the ARS-UCD2.0 version reference sequence of the dairy cow genome, with a length of 0.075Kb and a nucleotide sequence of SEQ ID NO.2.

[0009] To further explain, the first QTL contains haplotype H001, and the base sequence of haplotype H001 is CT / TA; the second QTL contains haplotype H001, and the base sequence of haplotype H001 is GT / AC.

[0010] To further clarify, the haplotypes H001 are all located on the LOC112441463 gene.

[0011] The present invention also provides a method for selecting or assisting in the selection of dairy cows with low blood lymphocyte counts and high milk production, by detecting haplotype H001 containing the above two QTLs, and selecting individuals carrying the H001 haplotype as parents for breeding or reproduction.

[0012] The present invention also provides the use of the haplotype H001 in improving milk production performance in dairy cows and / or reducing the number of blood lymphocytes in dairy cows.

[0013] This invention also provides a QTL screening method related to the number of blood lymphocytes and milk yield in dairy cows, comprising the following steps:

[0014] (1) Collect blood samples from dairy cows and extract DNA;

[0015] (2) Detect blood routine indicators and estimate breeding value (EBV);

[0016] (3) Whole genome resequencing and SNP genotyping;

[0017] (4) GWAS analysis was performed using the FarmCPU method;

[0018] (5) Gene-level association analysis based on MAGMA;

[0019] (6) Construct haplotype modules using geneHapR;

[0020] (7) Marker-trait association analysis based on haplotype module.

[0021] By adopting the above technical solution, the present invention has the following beneficial effects:

[0022] The technical method of this invention combines compressed test dimensions with haplotype module localization through gene-level association analysis, thereby improving the accuracy of QTL localization intervals from the traditional Mb level to the Kb level. Our team discovered two QTLs affecting the number of blood lymphocytes and milk yield in dairy cows, located on chromosome 15 of the ARS-UCD 2.0 version, between positions 5870150 and 5871137 and between positions 5873277 and 5873351, respectively, with lengths of 0.988 Kb and 0.075 Kb. Both QTLs are located within the LOC112441463 gene. Three haplotypes in Block 2 of the LOC112441463 gene are significantly associated with the number of blood lymphocytes in dairy cows. Holstein cows with the H001(CT / TA) haplotype have lower lymphocyte counts than cows with other haplotypes, and simultaneously have higher milk production, indicating that haplotype H001 is a QTL related to dairy cow milk production performance. Similarly, three haplotypes in Block 4 of the LOC112441463 gene are also significantly associated with the number of blood lymphocytes in dairy cows. Holstein cows with the H001(GT / AC) haplotype have lower lymphocyte counts than cows with other haplotypes, and simultaneously have higher milk production, indicating that this haplotype is also a QTL related to dairy cow milk production performance. The identification of these QTLs further enriches the molecular breeding marker library for dairy cows, laying the foundation for improving milk production performance. Attached Figure Description

[0023] Figure 1 This is a Manhattan plot of the genome-wide association analysis of blood lymphocyte counts in Chinese Holstein cattle, based on the present invention. Explanation of the figure labels: The markers above the dotted lines represent the three SNP molecular markers for blood lymphocyte counts selected in this invention, located on chromosomes 7 and 15 of dairy cows, respectively.

[0024] Figure 2This is a qq plot of blood lymphocyte counts from a genome-wide association study of Chinese Holstein cattle, as presented in this invention.

[0025] Figure 3 This is a haplotype analysis diagram of the quantitative trait of blood lymphocytes in Chinese Holstein cattle in this invention. The diagram shows the haplotype module constructed by the SNP in the LOC112441463 gene on chromosome 15 during the haplotype construction analysis.

[0026] Figure 4 This is a comparative analysis of lymphocyte count and milk yield among the three haplotypes of the Chinese Holstein cattle Blok2 in this invention. Figure A compares the lymphocyte count among the three haplotypes of Blok2, and Figure B compares the breeding values ​​of milk yield among the three haplotypes of Blok2.

[0027] Figure 5 This is a comparative analysis of lymphocyte count and milk yield between two haplotypes of Blok4 Holstein cattle in China, as presented in this invention. Figure A compares the lymphocyte count between the two haplotypes of Blok4, and Figure B compares the breeding values ​​of milk yield between the two haplotypes of Blok4. Detailed Implementation

[0028] The specific implementation of the invention will be further described below with reference to the accompanying drawings.

[0029] Example 1

[0030] (I) Experimental Design:

[0031] 1. Sample collection and DNA preparation: 348 Holstein cows were selected, and DNA was prepared for each individual using the Tiangen Blood Genomic DNA Extraction Kit.

[0032] 2. Phenotypic Collection and Breeding Value Estimation: Blood samples were collected from 348 Holstein dairy cows, and... The 2120i hematology analyzer completed routine blood tests for all individuals. Breeding values ​​(EBV) for hematological traits were estimated using a single-trait animal model.

[0033] The model is as follows: y = Xb + Zg + e

[0034] Where y is the blood phenotypic value, X is the fixed effects matrix, b is the fixed effects variable such as batch (2 levels), parity (4 levels), and lactation stage (4 levels); Z is the random effects matrix, g is the individual additive effect; and e is the residual effect.

[0035] 3. SNP genotyping: Whole-genome resequencing was used to sequence 348 individuals. SNP genotyping was performed using GATK 4.2 software, followed by genotyping using Beagle 5.4 software. Population SNP filtering was performed using PLINK software. The filtering criteria were as follows: individual call rate of ≥97%; SNP detection rate of ≥95%; minimum allele frequency (MAF) greater than 0.05; and Hardy-Wenger-Bohr balance test p-value less than 10. -6 Finally, 348 individuals and 1,030,098 SNPs were used for subsequent analysis.

[0036] 4. Population structure and linkage disequilibrium analysis: Principal component analysis (PCA) was performed on the population using PLINK software to evaluate the population's genetic structure; PopLDdecay software was used to evaluate the degree of genome-wide linkage disequilibrium (LD) in the population, aiming to assess the efficiency and accuracy of association analysis.

[0037] 5. Genome-wide association analysis (GWAS) method: GWAS analysis of each blood trait was performed using the FarmCPU method in rMVP software. The kinship matrix and four principal components were used as covariates. Boferroni was used to correct the p-values ​​of the GWAS analysis. The threshold for the GWAS signal was 0.05 / N, where N equals 1030098.

[0038] 6. Genome-wide association analysis (GWAS) based on genes: First, 21,521 genes were extracted from bovine .gft files (ARS-UCD2.0) using a custom script. Based on the obtained GWAS results, gene-level association analysis was performed using MAGMA software in two steps: SNP annotation and SNP association analysis. Boferroni was used to correct the p-values ​​of the GWAS analysis. The threshold for the GWAS signal was set to 0.05 / N, where N equals 14,674.

[0039] 7. Haplotype Association Analysis: Based on the GWAS results at the gene level, linkage disequilibrium association analysis was used to identify QTLs associated with blood traits, identify candidate genes, and construct linkage disequilibrium modules. The geneHapR software package was used for simple, statistical, phenotypic association, and result visualization analysis of the linkage disequilibrium modules. Genomic regions containing linkage disequilibrium blocks (P<0.05) associated with phenotypic traits were identified as candidate QTLs.

[0040] 8. Comparative analysis of QTLs with lymphocyte count and milk yield: For different haplotypes, the lymphocyte count and milk yield breeding values ​​corresponding to each haplotype individual were extracted. The differences in lymphocyte count and milk yield breeding values ​​between different haplotype groups were compared and analyzed using analysis of variance to screen QTLs related to lymphocyte count and milk yield.

[0041] (II) Test Results:

[0042] 1. Validation by gene-level association analysis

[0043] The Manhattan plot is a graph that ranks the effect values ​​of genetic markers (i.e., genome-wide P-values ​​after F-test) by their physical location on chromosomes. The horizontal axis represents the physical location on chromosomes, and the vertical axis represents -log10P. Smaller P-values ​​indicate stronger associations, represented by a larger vertical axis. The horizontal dashed line in the Manhattan plot represents the significance level; when -log10(P) > 5, the SNP is considered significantly associated with the trait. The qq plot (Quantile-Quantile plot) shows the distribution of actual P-values ​​and expected P-values ​​without the null hypothesis. It is used to detect the impact of population stratification and individual kinship on association analysis. If the observed P-values ​​and expected P-values ​​only appear at the rightmost end of the distribution, it indicates that the trait is not caused by population stratification. The qq plot is mainly used to estimate the difference between observed and predicted values ​​of quantitative traits. Generally, the quantitative trait data we obtain are normally distributed. In GWAS studies, the X and Y axes of the qq plot mainly represent the -lgP values ​​of each SNP. The predicted line is a dashed line at a 45° angle emanating from the origin. Figure 1-2 It was found that three significant GWAS signals associated with the lymphocyte count (nLYM) trait were focused on chromosomes 7 and 15 of dairy cows. Figure 1 The GWAS signal on chromosome 7 is the OR7A116 gene, while the GWAS signal on chromosome 15 corresponds to the KBTBD3 and LOC112441463 genes, respectively. The QQ plot further confirms the reliability of the analysis results. Figure 2 ).

[0044] 2. Based on GWAS signal and attenuation distance from linkage disequilibrium analysis, further research revealed that four haplotype modules (Block 1-Block 4) are associated with the lymphocyte count (nLYM) trait. Figure 3 These four haplotype modules are all located on the LOC112441463 gene. Block 1 contains 7 SNPs, spanning a 0.869kb region on bovine chromosome 15. In addition, Block 2, Block 3, and Block 4 each contain 2 SNPs, with lengths of 0.988, 0.572, and 0.075kb, respectively.

[0045] 3. Haplotype screening results related to lymphocyte count (nLYM): Haplotype association analysis revealed that all four haplotype modules in the LOC112441463 gene were significantly associated with the lymphocyte count trait (Table 1). Holstein cows with the 001 (CT / TA) haplotype in Block 2 had a lower lymphocyte count than cows with other haplotypes. Figure 4 A), and H001 is also the dominant haplotype, indicating that the H001 allele is a candidate QTL affecting the nLYM trait in dairy cows. Furthermore, Holstein cows with the H001(GT / AC) haplotype in Block 4 had lower lymphocyte counts compared to cows with other haplotypes. Figure 4 B), and H001 is also a dominant haplotype, indicating that the H001 allele is a candidate QTL that affects the nLYM trait in dairy cows.

[0046] Table 1. Association between the LOC112441463 intragene haplotype module and milk yield and lymphocyte data.

[0047]

[0048]

[0049] 4. Haplotype screening results related to milk yield at 305 days: Haplotype association analysis revealed two haplotype modules in the LOC112441463 gene that were significantly associated with milk yield at 305 days (Table 1). Holstein cows with the H001 (CT / TA) haplotype in Block 2 had higher milk yields than cows with other haplotypes. Figure 5 A), and H001 is also the dominant haplotype, indicating that the H001 allele is a candidate QTL for the milk yield trait in dairy cows. Furthermore, Holstein cows with the H001 (GT / AC) haplotype in Block 4 have higher milk yields than cows with other haplotypes. Figure 5 B), and H001 is also a dominant haplotype, indicating that the H001 allele is a candidate QTL that affects the milk yield trait in dairy cows.

[0050] Example 2:

[0051] A method for selecting or assisting in the selection of dairy cows with low blood lymphocyte counts and high milk production involves detecting haplotype H001, which contains two QTLs on the LOC112441463 gene on chromosome 15 of dairy cows. The first QTL contains haplotype H001 with a base sequence of CT / TA, and the second QTL contains haplotype H001 with a base sequence of GT / AC. Individuals carrying the H001 haplotype are selected as parents for breeding or reproduction.

[0052] The above description is a detailed description of the preferred embodiments of the present invention. However, the embodiments are not intended to limit the scope of the patent application of the present invention. All equivalent changes or modifications made under the technical spirit of the present invention should fall within the patent scope covered by the present invention.

Claims

1. A QTL for identifying the correlation between bovine blood lymphocyte count and 305-day milk yield, characterized in that, The QTL includes two QTLs; One of them is located on chromosome 15 between positions 5870150 and 5871137 in the ARS-UCD2.0 version reference sequence of the dairy cow genome, with a length of 0.988Kb and a nucleotide sequence of SEQ ID NO.1; Part Two: Located between 5873277 and 5873351 on chromosome 15 of the ARS-UCD2.0 version reference sequence of the dairy cow genome, with a length of 0.075Kb and a nucleotide sequence of SEQ ID NO.

2.

2. The QTL as described in claim 1, characterized in that, The first QTL contains haplotype H001, the base sequence of which is CT / TA; the second QTL contains haplotype H001, the base sequence of which is GT / AC.

3. The QTL as described in claim 2, characterized in that, The haplotypes H001 are all located on the LOC112441463 gene.

4. A method for selecting or assisting in the selection of dairy cows with low blood lymphocyte counts and high milk production, characterized in that, The haplotype H001 containing both QTLs of claim 2 is detected, and individuals carrying the H001 haplotype are selected as parents for breeding or reproduction.

5. The use of haplotype H001 as described in claim 2 in improving milk production performance in dairy cows and / or reducing the number of blood lymphocytes in dairy cows.

6. A method for screening QTLs related to the number of blood lymphocytes and milk yield in dairy cows, characterized in that, Includes the following steps: (1) Collect blood samples from dairy cows and extract DNA; (2) Detect blood routine indicators and estimate breeding value (EBV); (3) Whole genome resequencing and SNP typing; (4) GWAS analysis was performed using the FarmCPU method; (5) GWAS analysis at the gene level based on MAGMA; (6) Construct haplotype modules using geneHapR; (7) Marker-trait association analysis based on haplotype module.

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