A method for screening polymorphic genetic markers for disease resistance traits in livestock and its application

Abstract

This invention discloses a method and application for screening polymorphic genetic markers of disease resistance traits in livestock, relating to the fields of animal molecular genetics and genome breeding technology. This invention uses single-cell transcriptome sequencing to locate key cell subpopulations for disease resistance, and then conducts targeted eQTL analysis on these purified subpopulations. This addresses the shortcomings of relying on mixed samples, which leads to a lack of cell type specificity in markers. Immune cells are grouped using single-cell transcriptome sequencing, and key cell subpopulations in the disease resistance response are screened using pivotal scoring. Cell sorting technology is then used to purify these subpopulations, ensuring that subsequent eQTL analysis is conducted only on these core subpopulations. This process clearly distinguishes the specific cell types involved in the genetic variation, enabling the screened eQTL markers to be directly associated with disease resistance regulatory mechanisms. This achieves a precise correspondence between markers and cell functions, eliminating the interference of mixed samples on marker specificity and giving the markers clear cell type specificity and functional targeting.

Description

Technical Field

[0001] This invention relates to the fields of animal molecular genetics and genome breeding technology, specifically to a method and application for screening polymorphic genetic markers for disease resistance traits in livestock. Background Technology

[0002] Disease resistance in livestock is a core economic trait for ensuring the healthy development of animal husbandry. Molecular marker-assisted selection is a key technology for breeding disease-resistant strains and addressing the problems of frequent disease outbreaks and antibiotic overuse. Expressed quantitative trait loci (eQTLs) can directly link genomic variation with gene expression and disease resistance phenotypes, and have become a core direction for screening genetic markers for disease resistance in livestock. However, existing screening methods have significant limitations, resulting in genetic markers that are insufficient to meet the needs of precision breeding. Therefore, a new screening technology that can overcome existing bottlenecks and possess both specificity and functionality is urgently needed.

[0003] Current methods for screening eQTL markers related to disease resistance in livestock rely on sequencing analysis of mixed cell samples such as whole blood and lung tissue. These methods do not target key cell subpopulations involved in the disease resistance response. Under this model, the resulting eQTL markers represent the average effect of the mixed cell population and cannot distinguish the specific immune cell subpopulations that play a role in genetic variation, lacking clear cell type specificity. Furthermore, due to the complex cell types in mixed samples, the correlation between markers and disease resistance phenotypes is low, and the functional directionality is ambiguous. This makes it difficult to accurately correspond to the core mechanisms of disease resistance regulation, resulting in difficulties in accurately screening disease-resistant individuals when using these markers in molecular breeding, and hindering the targeted improvement of disease resistance traits in livestock.

[0004] In summary, the core deficiency of existing technologies lies in their failure to focus on key cellular subpopulations in livestock disease resistance responses. eQTL markers relying on pooled sample screening suffer from poor specificity, weak functional targeting, and weak association with disease resistance phenotypes, thus limiting the accuracy and efficiency of molecular breeding for livestock disease resistance. Therefore, developing a method centered on key cellular subpopulations that can screen for highly specific and functionally defined disease resistance genetic markers has become a pressing technical challenge in this field. Summary of the Invention

[0005] The purpose of this invention is to overcome the shortcomings of existing technologies and provide a method and application for screening polymorphic genetic markers for disease resistance traits in livestock. This method can locate key cell subpopulations for disease resistance through single-cell transcriptome sequencing, and then conduct targeted eQTL analysis on these purified subpopulations. This addresses the drawback of relying on mixed samples, which leads to a lack of cell type specificity in markers. By using single-cell transcriptome sequencing to group immune cells and combining pivotal scoring to screen key cell subpopulations in the disease resistance response, and then purifying these subpopulations using cell sorting technology, the subsequent eQTL analysis is ensured to be conducted only on these core subpopulations. This process can clearly distinguish the specific cell types involved in the genetic variation, enabling the screened eQTL markers to be directly associated with disease resistance regulatory mechanisms. This achieves a precise correspondence between markers and cell functions, eliminating the interference of mixed samples on marker specificity and giving the markers clear cell type specificity and functional targeting.

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution: On one hand, a method for screening polymorphic genetic markers for disease resistance traits in livestock, the steps of which are as follows:

[0007] S100. For target livestock, based on the level of resistance to specific pathogens, individuals with disease-resistant phenotypes and individuals with susceptible phenotypes are screened, and immune tissue samples related to the target disease are collected and prepared into single-cell suspensions.

[0008] S200. Perform single-cell transcriptome sequencing on single-cell suspensions of the disease-resistant individuals and the susceptible individuals, calculate pivotal scores, screen out cell subpopulations that meet the threshold requirements for pivotal scores, and identify the cell subpopulations that meet the threshold requirements as disease-resistant cell subpopulations.

[0009] S300. Based on the specific surface markers of the disease-resistant cell subpopulation, cell sorting technology is used to sort samples from all individuals in the livestock disease-resistant response sample library to obtain purified key disease-resistant cell subpopulations, and the purity of the purified key cell subpopulations must reach a preset threshold.

[0010] S400, after extraction and purification of genomic DNA and total RNA of key disease-resistant cell subpopulations, whole-genome SNP genotyping and transcriptome sequencing were performed, and SNP genotype matrix and gene expression matrix were constructed. eQTL association analysis was performed using a linear mixture model to screen out eQTL sites that meet the first association significance requirement and form a specific eQTL site dataset.

[0011] S500. Genome-wide association analysis was performed on the specific eQTL locus dataset and the disease resistance / susceptibility phenotype data to screen out candidate genetic markers that meet the second significance requirement of the disease resistance phenotype and whose target genes are disease resistance-related functional genes. The polymorphic genetic markers of the disease resistance trait in livestock were determined through genotype-gene expression level verification, genotype-disease resistance phenotype verification and in vitro functional verification.

[0012] Further, in S100, the target livestock is any one of cattle, sheep, or pigs; the criterion for determining the disease-resistant phenotype is a pathogenic load ≤ 10³ CFU / mL; and the criterion for determining the susceptible phenotype is a pathogenic load ≥ 10³ CFU / mL. 6 CFU / mL.

[0013] Furthermore, the step in S200 for calculating the pivotal score of each cell subpopulation is as follows:

[0014] Cell populations were obtained from single-cell transcriptome sequencing data of individuals with disease-resistant and susceptible phenotypes, and gene expression levels were quantified for each cell subpopulation.

[0015] For each cell subpopulation, the number of differentially expressed genes in the disease resistance response between individuals with the disease-resistant phenotype and those with the susceptible phenotype was counted. ,in, For the first The study identified several cell subpopulations and calculated the maximum number of differentially expressed genes across all subpopulations. ;

[0016] right Normalization was performed to obtain the normalized disease resistance response activity value. A co-expression network of disease resistance response genes was constructed based on the gene expression levels of all cell subpopulations, and the betweenness centrality of each cell subpopulation in the co-expression network of disease resistance response genes was calculated. And calculate the maximum value of the median centrality for all cell subsets. ,right Normalization is performed to obtain the normalized network hub status value. ;

[0017] Calculate the first pivotal score of individual cell subpopulations ,in, The weighting coefficients, ranging from 0.4 to 0.6, are used to calculate the pivotal scores for each cell subpopulation. With preset threshold When a comparison is performed, When the threshold requirement is met, the cell subpopulation that meets the threshold requirement is identified as the disease-resistant cell subpopulation.

[0018] Furthermore, in S300, the specific surface markers of the disease-resistant cell subset are determined according to cell type:

[0019] When the disease-resistant cell subset is dendritic cells, the surface markers are a combination of CD11c and CD103;

[0020] When the disease-resistant cell subset is macrophages, the surface marker is CD14;

[0021] When the disease-resistant cell subset is cytotoxic T cells, the surface marker is CD8α.

[0022] Furthermore, in S300, the cell sorting technology is flow cytometry cell sorting technology, and the sorting steps of the flow cytometry cell sorting technology are as follows:

[0023] The single-cell suspension of the sample was stained with fluorescently labeled surface marker antibodies;

[0024] Set up negative and positive controls and adjust the flow cytometry sorting parameters;

[0025] The stained single-cell suspension was sorted, and the purity of the purified key disease-resistant cell subpopulation was set at a threshold of 95%. The purity was detected by flow cytometry to detect the positive rate of surface markers in the sorted cells.

[0026] Furthermore, in S400, genomic DNA is extracted using the phenol-chloroform method, and the extracted genomic DNA meets the following requirements: concentration ≥ 50 ng / μL and absorbance ratio OD260 / 280 at wavelengths of 260 nm and 280 nm is 1.8-2.0.

[0027] Total RNA was extracted using the Trizol method, and the extracted total RNA was ≥200 ng / μL with an OD260 / 280 ratio of 2.0-2.2.

[0028] Furthermore, the working principle of the whole-genome SNP genotyping is as follows: The genomic DNA of purified disease-resistant key cell subpopulations is fully sequenced using whole-genome resequencing technology to obtain the complete genome sequence information. Single nucleotide polymorphism (SNP) sites in the genome are identified through bioinformatics analysis, and an SNP genotype matrix is ​​constructed to provide genotype data for eQTL association analysis. The specific steps are as follows:

[0029] Qualified genomic DNA was extracted from the purified disease-resistant key cell subpopulation and subjected to DNA fragmentation to break the DNA into 300-500bp fragments.

[0030] Fragmented DNA was subjected to end repair, A-tailing, and adapter ligation to construct a whole-genome sequencing library;

[0031] Whole-genome resequencing technology was used to perform sequencing on a sequencing platform, with a sequencing depth of ≥10× to ensure that each SNP site was effectively covered.

[0032] The raw sequencing data were subjected to quality control to filter out low-quality reads: reads with Q30 < 80%, reads containing adapter contamination or N base ratio > 5%;

[0033] The clean reads after quality control are compared with the reference genome of the target livestock. The comparison efficiency must be ≥95%, and SNP sites are identified.

[0034] The identified SNP sites were filtered out, and low-quality SNPs with sequencing depth <3×, genotype deletion rate >5%, and minimum allele frequency <0.05 were removed. The detection rate of the retained SNP sites was ≥98%, and an SNP genotype matrix was constructed for each individual.

[0035] The working principle of the transcriptome sequencing is as follows: Using high-throughput sequencing technology, the total RNA of purified key disease-resistant cell subpopulations is sequenced to obtain the expression information of all mRNAs within the cells, quantifying the expression level of each gene, constructing a gene expression matrix, and providing gene expression data for eQTL association analysis. The specific steps are as follows:

[0036] Qualified total RNA was extracted from purified disease-resistant key cell subpopulations and mRNA was enriched using oligo(dT) magnetic beads to remove interference from non-coding RNAs such as rRNA and tRNA.

[0037] The enriched mRNA was fragmented into 200-300bp fragments. Using the fragmented mRNA as a template, cDNA was synthesized by reverse transcription. Then, end repair, A-tailing and adapter ligation were performed to construct a transcriptome sequencing library.

[0038] Transcriptome sequencing was performed with a read length of PE150 and a sequencing depth of ≥15× to ensure the accuracy of gene expression quantification.

[0039] The raw sequencing data were subjected to quality control to filter out low-quality reads: reads with Q30 < 80%, reads containing adapter contamination or N base ratio > 5%;

[0040] The clean reads after quality control are compared with the reference genome of the target livestock. The comparison efficiency must be ≥90%. Gene expression matrix of key cell subpopulations for each individual is constructed.

[0041] Furthermore, in S400, the working principle of using a linear mixture model for eQTL association analysis is as follows:

[0042] Using the SNP genotype matrix as the independent variable and the gene expression matrix as the dependent variable, while incorporating individual sex, individual age and sequencing batch as covariates into the linear mixture model, we eliminated the influence of population stratification and experimental confounding factors on the association analysis results.

[0043] pass Calculate the significance of the association Value, of which, For the first The SNP locus and the first Significance of eQTL associations for gene expression levels value, For association analysis Statistic, This represents the mean gene expression level. The variance of gene expression levels;

[0044] The presupposed standard for the first eQTL association significance requirement is: eQTL association significance. Value < ;

[0045] The eQTL sites are classified into cis eQTLs and trans eQTLs based on the genomic distance between the SNP site and the target gene. Cis eQTLs are those where the genomic distance between the SNP site and the target gene is ≤1Mb, and trans eQTLs are those where the genomic distance between the SNP site and the target gene is >1Mb.

[0046] Furthermore, in step S500, when performing genome-wide association analysis between the specific eQTL locus dataset and the livestock disease resistance / susceptibility phenotype data, the genotype of the eQTL locus is used as the independent variable and the livestock disease resistance / susceptibility phenotype is used as the dependent variable to calculate the significance of the association between each eQTL locus and the disease resistance phenotype. value;

[0047] The pre-set standard for the second association significance requirement is: the significance of the association between eQTL loci and the disease resistance phenotype. value < ;

[0048] The preset criteria for the candidate genetic markers are: they meet the second association significance requirement with the disease resistance phenotype, and the eQTL target genes corresponding to the candidate genetic markers are disease resistance-related functional genes, including immune regulation genes, inflammatory response genes, and pathogen recognition receptor genes;

[0049] The steps for genotype-gene expression level verification are as follows: detect the expression levels of candidate genetic marker target genes in disease-resistant key cell subpopulations of individuals with different genotypes, analyze the differences in target gene expression levels between different genotypes using the significance test, and the verification pass criterion is that the difference in target gene expression levels between different genotypes is ≥1.5 times and the P value is <0.01;

[0050] The specific steps for genotype-resistant phenotype verification are as follows: statistically analyze the survival rate and pathogen clearance efficiency of individuals with different genotypes after challenge with the target pathogen. The verification criteria are that the difference in survival rate between different genotypes is ≥20% and the difference in pathogen clearance efficiency is ≥30%.

[0051] The specific steps for in vitro functional validation are as follows: construct in vitro cell models of key cell subpopulations of candidate genetic markers with different genotypes, infect cell models with the target pathogen, detect the pathogen phagocytosis efficiency and the secretion level of disease resistance-related cytokines in the cell models, and the validation criteria are that the pathogen phagocytosis efficiency and cytokine secretion level of the cell models with the disease resistance dominant genotype are significantly higher than those of the cell models with the susceptible genotype (difference ≥ 1.5 times and P value < 0.01).

[0052] On the other hand, the application of a polymorphic genetic marker screening method for disease resistance traits in livestock includes molecular marker-assisted selection breeding for disease-resistant livestock strains, genetic improvement of disease resistance traits in livestock, and preparation of disease resistance trait detection kits for livestock.

[0053] Compared with existing technologies, this method and application for screening polymorphic genetic markers for disease resistance traits in livestock has the following advantages:

[0054] I. This invention uses single-cell transcriptome sequencing to locate key cell subpopulations in disease resistance, and then conducts targeted eQTL analysis on these purified subpopulations. This addresses the shortcomings of relying on mixed samples, which leads to a lack of cell type specificity in markers. By using single-cell transcriptome sequencing to group immune cells and combining pivotal scoring to screen key cell subpopulations in the disease resistance response, cell sorting technology is used to purify these subpopulations. This ensures that subsequent eQTL analysis is only performed on these core subpopulations. This process can clearly distinguish the specific cell types involved in genetic variation, enabling the screened eQTL markers to be directly associated with disease resistance regulatory mechanisms. This achieves a precise correspondence between markers and cell functions, eliminating the interference of mixed samples on marker specificity and giving the markers clear cell type specificity and functional orientation.

[0055] II. This invention solves the problems of weak marker function and weak association with disease resistance phenotype by purifying key cell subpopulations, performing subpopulation-specific eQTL analysis, and verifying phenotype-genotype linkage. It conducts whole-genome SNP genotyping and transcriptome sequencing in the purified key cell subpopulations to construct the SNP genotype matrix and gene expression matrix for this subpopulation. eQTL association analysis is used to screen for genetic variations that regulate gene expression in this subpopulation. Combined with disease resistance phenotype data, markers that are highly significantly associated with the disease resistance phenotype and whose target genes are disease resistance-related functional genes are selected. The marker function is confirmed through triple verification. This process allows the markers to directly correspond to the disease resistance regulatory function of the key cell subpopulation, clarifying the mechanism by which the markers participate in the disease resistance response by regulating the expression of core disease resistance genes, and achieving precise linkage between markers, cell function, and disease resistance phenotype.

[0056] Other advantages, objectives and features of the invention will be set forth in part in the description which follows, and in part will be apparent to those skilled in the art from the following examination or study, or may be learned from the practice of the invention. Attached Figure Description

[0057] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are merely some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without any creative effort.

[0058] Figure 1 A flowchart illustrating a method for screening polymorphic genetic markers for disease resistance traits in livestock;

[0059] Figure 2 This is a flowchart of whole-genome SNP typing and genotype matrix construction in an embodiment of the present invention;

[0060] Figure 3 This is a flowchart of transcriptome sequencing and gene expression matrix construction in an embodiment of the present invention. Detailed Implementation

[0061] To further illustrate the technical means and effects of the present invention in achieving its intended purpose, the following detailed description of the specific implementation methods, structures, features, and effects of the present invention, in conjunction with the accompanying drawings and preferred embodiments, is provided below.

[0062] To address the shortcomings of existing methods for screening genetic markers for disease resistance traits in livestock, which rely on mixed tissue samples and fail to focus on key cell types in disease resistance responses, resulting in poor specificity, weak functional targeting, and weak association with disease resistance phenotypes, this invention provides a method for screening polymorphic genetic markers for disease resistance traits in livestock. This method aims to reveal the molecular mechanisms by which genomic variations regulate disease resistance at the cell type-specific level by integrating single-cell transcriptome sequencing, key immune cell subset purification, and quantitative trait locus analysis based on purified cell subsets. This allows for the screening of genetic markers with a clear cellular functional basis and a strong association with disease resistance phenotypes. This invention is primarily applied to molecular breeding and disease resistance genetic improvement in major livestock such as cattle, sheep, and pigs. Traditional methods for screening eQTL markers in mixed cell backgrounds cannot distinguish the specific cell types involved in the variation, limiting the markers' application value. This invention constructs a precise screening pathway from cellular function to molecular markers through a complete technical system encompassing screening of phenotypic extreme individuals, identification of key subpopulations at the single-cell level, subpopulation purification, subpopulation-specific eQTL analysis, phenotypic association, and functional verification.

[0063] Specifically, such as Figure 1 As shown, a method for screening polymorphic genetic markers for disease resistance traits in livestock is described, and the steps of this screening method are as follows:

[0064] S100. For target livestock, based on the level of resistance to specific pathogens, individuals with disease-resistant phenotypes and individuals with susceptible phenotypes are screened, and immune tissue samples related to the target disease are collected and prepared into single-cell suspensions.

[0065] Single-cell transcriptome sequencing was performed on S200 cells from individuals with resistant and susceptible phenotypes, respectively. Pivotal scores were calculated, and cell subpopulations that met the threshold requirements were screened out. Cell subpopulations that met the threshold requirements were identified as resistant cell subpopulations.

[0066] S300. Based on the specific surface markers of the disease-resistant cell subpopulation, cell sorting technology is used to sort samples from all individuals in the livestock disease-resistant response sample library to obtain purified key disease-resistant cell subpopulations, and the purity of the purified key cell subpopulations must reach a preset threshold.

[0067] S400, after extraction and purification of genomic DNA and total RNA of key disease-resistant cell subpopulations, whole-genome SNP genotyping and transcriptome sequencing were performed, and SNP genotype matrix and gene expression matrix were constructed. eQTL association analysis was performed using a linear mixture model to screen out eQTL sites that meet the first association significance requirement and form a specific eQTL site dataset.

[0068] S500. Genome-wide association analysis was performed on the specific eQTL locus dataset and the disease resistance / susceptibility phenotype data to screen out candidate genetic markers that meet the second significance requirement of the disease resistance phenotype and whose target genes are disease resistance-related functional genes. The polymorphic genetic markers of the disease resistance trait in livestock were determined through genotype-gene expression level verification, genotype-disease resistance phenotype verification and in vitro functional verification.

[0069] In its implementation, S100 screens and prepares samples from individuals with extreme disease-resistant / susceptible phenotypes. By acquiring samples from individuals with clear and extreme disease-resistant phenotypes, it selects target livestock populations and monitors and records the post-infection clinical manifestations and pathogen load of each individual in the population for a specific pathogen. Based on pre-defined phenotypic criteria: the criteria for disease-resistant individuals is a post-infection pathogen load in blood or target tissue ≤10³ CFU / mL; the criteria for susceptible individuals is a concurrent pathogen load ≥10³ CFU / mL. 6 CFU / mL. Individuals with disease-resistant phenotypes and susceptible phenotypes were screened from the population separately.

[0070] Immune tissues related to the target disease were collected from the target livestock. The collected tissue samples were placed in a pre-cooled balanced salt solution containing protease, mechanically shredded, and then gently digested using a combination of collagenase IV and DNase I to prepare a single-cell suspension. The tissue residue was removed by filtration through a cell sieve, and red blood cells were removed using red blood cell lysis buffer. The cells were resuspended in a serum-containing culture medium and viable cell counts were performed. The single-cell suspensions from the disease-resistant group and the susceptible group were labeled, aliquoted, and frozen in liquid nitrogen.

[0071] Single-cell suspensions from the disease-resistant and susceptible groups prepared by S100 were extracted. Using an equivalent single-cell sequencing platform, single-cell RNA sequencing libraries were constructed and sequenced for both groups. The obtained raw sequencing data were compared and gene counted to generate a gene expression matrix for each cell. Standard analyses were performed using bioinformatics software: quality control (filtering low-quality cells, such as cells with <200 or >5000 genes, or mitochondrial gene proportion >20%), data standardization and scaling, selection of highly variable genes, and principal component analysis (PCA). Based on principal components, graph clustering algorithms were used to perform unsupervised grouping of all cells, resulting in multiple cell subpopulations. Each cell subpopulation was annotated using known cell marker genes to identify the main immune cell types. A pivotal score for each cell subpopulation was calculated to quantify its relative importance in the disease response. The specific steps are as follows:

[0072] For each cell subpopulation, the gene expression profiles of the disease-resistant group and the susceptible group were compared within the subpopulation. Differentially expressed genes between the two groups were screened out, and the significance threshold was set as adjusted P value < 0.01 and expression difference > 2.

[0073] Count the total number of differentially expressed genes that meet the criteria within this subgroup. Calculate the total number of cell subpopulations. maximum value The normalized disease resistance response activity value of the m-th cell subset is... ,Should The closer the value is to 1, the more significant the transcriptomic differences between disease-resistant and susceptible individuals in this subgroup;

[0074] Gene expression data from all cell subpopulations were integrated to construct a co-expression network of genes related to disease resistance response. Based on the gene expression levels of all cell subpopulations, a co-expression network of disease resistance response genes was built, and the betweenness centrality of each cell subpopulation within the co-expression network was calculated. And calculate the maximum value of the median centrality for all cell subsets. ,right Normalization is performed to obtain the normalized network hub status value. ,Should The higher the value, the more central and pivotal the subgroup is in the gene regulatory network of disease resistance response;

[0075] Calculate the pivotal score of the m-th cell subpopulation ,in, This is a weighting coefficient with a value range of 0.4-0.6;

[0076] All cell subpopulations according to their Sort the values ​​in descending order. With preset threshold A comparison will satisfy The cell subpopulation was identified as a key cell subpopulation for disease resistance.

[0077] Based on the cell types of the key disease-resistant cell subpopulations identified in S200, specific combinations of surface markers are selected. In this embodiment, for example:

[0078] If the key subpopulation is dendritic cells, then the combination of CD11c and CD103 should be selected for sorting;

[0079] If the key subset is monocytes / macrophages, then CD14 is selected as the surface marker;

[0080] If the key subset is cytotoxic T cells, then CD8α is selected as the surface marker.

[0081] Tissue samples were taken from the livestock disease resistance response sample bank, and single-cell suspensions were prepared according to the S100 method. These suspensions were then purified using flow cytometry cell sorting technology. The specific steps are as follows:

[0082] Incubate single-cell suspensions with fluorescein-labeled antibodies specific to selected surface markers on ice in the dark for 30 minutes.

[0083] Cells stained with isotype control antibodies were used as negative controls, and cell lines known to highly express the marker or clearly positive samples were used as positive controls. Voltage and compensation were adjusted on the flow cytometer.

[0084] Using a high-precision flow cytometer, target cells are sorted according to the set criteria of live cells, single cells, and positive surface markers, and then directly collected into collection tubes containing culture medium.

[0085] After sorting, the positive rate of target surface markers is detected again by flow cytometry. The purity of the purified key anti-disease cell subpopulation must reach a preset threshold of ≥95%. If the purity does not meet the standard, the staining or sorting strategy needs to be optimized and the cells re-sorted.

[0086] High-quality genomic DNA and total RNA were extracted in parallel from the purified key cell subpopulations, including:

[0087] For genomic DNA extraction: the phenol-chloroform method is used, and the extracted DNA must have a concentration ≥50 ng / μL. The OD260 / 280 ratio must be between 1.8 and 2.0 as determined by a spectrophotometer to ensure that there is no protein or phenol contamination.

[0088] For total RNA extraction: the Trizol method was used, and the extracted RNA had to meet the following requirements: concentration ≥200ng / μL, OD260 / 280 ratio between 2.0 and 2.2, and RNA integrity index (RIN) ≥7.0.

[0089] like Figure 2 As shown, the steps for genome-wide SNP genotyping and genotype matrix construction are as follows:

[0090] Qualified genomic DNA was fragmented to 300-500 bp, and end repair, A-tailing, and adapter ligation were performed using a library preparation kit to construct a whole genome resequencing library.

[0091] Perform paired-end sequencing on the sequencing platform, with the sequencing depth of each sample set to ≥10×;

[0092] The raw sequencing data was quality controlled to filter out low-quality reads (Q30 < 80%) and reads containing adapters or N bases with a proportion > 5%. The clean reads after quality control were then aligned to the target livestock reference genome with an alignment efficiency of ≥ 95%.

[0093] Sites with sequencing depth <3×, genotype deletion rate >5%, and minimum allele frequency (MAF) <0.05 were removed. The detection rate of the retained SNP sites must be ≥98%. Finally, an SNP genotype matrix of behavioral individuals and filtered SNP sites was constructed.

[0094] like Figure 3 As shown, the steps for transcriptome sequencing and gene expression matrix construction are as follows:

[0095] mRNA was enriched from total RNA using oligo(dT) magnetic beads, and the enriched mRNA was fragmented to 200-300 bp to synthesize cDNA and construct strand-specific transcriptome sequencing libraries.

[0096] Perform paired-end sequencing on the sequencing platform, with the sequencing depth of each sample set to ≥15×;

[0097] The raw data is quality controlled and filtered, and the reads are aligned to the reference genome with an alignment efficiency of ≥90%. The read count of each gene is counted, and a gene expression matrix of behavioral genes is constructed and listed as an individual.

[0098] eQTL Association Analysis:

[0099] A linear mixture model was used for eQTL mapping to control for kinship and potential hierarchical structure among individuals. SNP genotype was used as a fixed-effect independent variable, gene expression level was used as a dependent variable, and individual sex, age and sequencing batch were included as covariates in the model.

[0100] The correlation statistics and corresponding values ​​are calculated through model fitting. value ,in, For the first The SNP locus and the first Significance of eQTL associations for gene expression levels value, For association analysis Statistic, This represents the mean gene expression level. To determine the variance of gene expression levels, a threshold for the first association significance is set as follows: < SNP-gene pairs that meet this significance level are identified as eQTLs. Based on the genomic distance between the SNP and the transcription start site of the target gene, eQTLs can be divided into cis eQTLs and trans eQTLs. All significant eQTL sites (SNPs) and their target gene information are compiled to form a specific eQTL site dataset. This dataset is used to reflect the genetic variations that significantly regulate gene expression in key disease-resistant cell subpopulations.

[0101] The SNP loci in the obtained specific eQTL locus dataset were used to perform association analysis with the disease resistance / susceptibility phenotype data. The phenotype was a binary variable (disease resistance = 1, susceptibility = 0). The p-value of the association between each eQTL locus and the disease resistance phenotype was calculated, and a threshold for the significance of the second association was set. Value < 1×10 6 .

[0102] Candidate genetic markers are screened from eQTL loci that simultaneously meet the following two conditions:

[0103] Association with disease resistance phenotype Value < 1×10 6 ;

[0104] The target genes (i.e., genes regulated by this eQTL locus) are known or predicted disease resistance-related functional genes, including immune regulatory genes, inflammatory response genes, and pathogen recognition receptor genes. Eligible eQTL loci are considered candidate genetic markers. Genotype-gene expression level verification, genotype-disease resistance phenotype verification, and in vitro functional verification are performed on these candidate markers to identify polymorphic genetic markers for disease resistance traits in livestock.

[0105] Genotype-gene expression level validation: In individuals that have undergone genotyping, key disease-resistant cell subsets are collected, and the expression levels of candidate genetic marker target genes are detected. Differences in target gene expression levels among individuals with different genotypes are compared. The validation pass criterion is: a fold change ≥ 1.5, and a statistically significant difference test. A value < 0.01 confirms that the marker does indeed regulate the expression of specific genes in key cells.

[0106] Genotype-resistance phenotype validation: The clinical outcomes of individuals carrying different candidate genetic marker genotypes after challenge with the target pathogen were statistically analyzed. The indicators included: survival rate, disease severity score, and pathogen clearance efficiency. The validation criteria were: the difference in survival rate between different genotypes ≥20% and the difference in pathogen clearance efficiency ≥30%.

[0107] In vitro functional validation: Using gene editing technology, cell models carrying different alleles of candidate genetic markers were constructed in in vitro cultured key disease-resistant cell lines. After infecting the cell models with the target pathogen, their functional indicators were tested: pathogen phagocytosis efficiency and the secretion level of disease resistance-related cytokines. The validation criteria were: the pathogen phagocytosis efficiency and cytokine secretion level of the cell model carrying the disease resistance dominant genotype were significantly higher than those of the cell model carrying the susceptibility genotype, with a fold difference ≥ 1.5 times. Value < 0.01.

[0108] The candidate genetic markers that passed the above triple verification were finally identified as polymorphic genetic markers that can be used for molecular breeding of the target disease resistance trait in this livestock.

[0109] The genetic markers screened through this invention have a clear cellular functional basis and strong phenotypic associations, and can be applied to:

[0110] Molecular marker-assisted selection breeding of disease-resistant livestock strains: In the core breeding population, by genotyping candidate markers, individuals carrying disease-resistant dominant alleles are selected as parents in an early and accurate manner, thereby accelerating the genetic progress of disease resistance traits.

[0111] Genetic improvement of disease resistance traits in livestock: as an important supplementary information to the genomic selection reference population, improving the accuracy of genomic prediction of disease resistance traits.

[0112] Molecular testing of disease susceptibility risk in livestock: In commercial farms, individuals are marked and tested to assess their innate susceptibility risk to a specific disease, enabling the implementation of differentiated feeding management and immunization strategies.

[0113] Preparation of disease resistance test kits for livestock: Based on identified genetic markers, develop PCR or gene chip kits for rapid on-site detection, and promote the industrial application of the technology.

[0114] In summary, this invention establishes a complete technical chain from extreme phenotype individuals, identification of key single-cell subpopulations, subpopulation purification, subpopulation-specific multi-omics analysis, phenotypic association, and multi-level validation. This enables the screening of high-quality genetic markers that are directly related to disease resistance mechanisms, have clear functional interpretations, and application value, providing a powerful and precise tool for molecular breeding of disease resistance in livestock.

[0115] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any simple modifications, equivalent changes and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the scope of the present invention.

Claims

1. A method for screening polymorphic genetic markers for disease resistance traits in livestock, characterized in that, The steps of this screening method are as follows: S100. For target livestock, based on the level of resistance to specific pathogens, individuals with disease-resistant phenotypes and individuals with susceptible phenotypes are screened, and immune tissue samples related to the target disease are collected and prepared into single-cell suspensions. S200. Perform single-cell transcriptome sequencing on single-cell suspensions of the disease-resistant individuals and the susceptible individuals, calculate pivotal scores, screen out cell subpopulations that meet the threshold requirements for pivotal scores, and identify the cell subpopulations that meet the threshold requirements as disease-resistant cell subpopulations. S300. Based on the specific surface markers of the disease-resistant cell subpopulation, cell sorting technology is used to sort samples from all individuals in the livestock disease-resistant response sample library to obtain purified key disease-resistant cell subpopulations, and the purity of the purified key cell subpopulations must reach a preset threshold. S400, after extraction and purification of genomic DNA and total RNA of key disease-resistant cell subpopulations, whole-genome SNP genotyping and transcriptome sequencing were performed, and SNP genotype matrix and gene expression matrix were constructed. eQTL association analysis was performed using a linear mixture model to screen out eQTL sites that meet the first association significance requirement and form a specific eQTL site dataset. S500. Genome-wide association analysis was performed on the specific eQTL locus dataset and the disease resistance / susceptibility phenotype data to screen out candidate genetic markers that meet the second significance requirement of the disease resistance phenotype and whose target genes are disease resistance-related functional genes. The polymorphic genetic markers of the disease resistance trait in livestock were determined through genotype-gene expression level verification, genotype-disease resistance phenotype verification and in vitro functional verification.

2. The method for screening polymorphic genetic markers for disease resistance traits in livestock according to claim 1, characterized in that, In the S100, the target livestock is any one of cattle, sheep, and pigs, the determination criterion for the resistant phenotype individual is pathogen load ≤ 10 6 CFU / mL, and the determination criterion for the susceptible phenotype individual is pathogen load ≥ 10 6 CFU / mL.

3. The method for screening polymorphic genetic markers for disease resistance traits in livestock according to claim 1, characterized in that, The steps for calculating the pivotal score of each cell subpopulation in S200 are as follows: Cell populations were obtained from single-cell transcriptome sequencing data of individuals with disease-resistant and susceptible phenotypes, and gene expression levels were quantified for each cell subpopulation. For each cell subpopulation, the number of differentially expressed genes in the disease resistance response between individuals with the disease-resistant phenotype and those with the susceptible phenotype was counted. ,in, For the first The study identified several cell subpopulations and calculated the maximum number of differentially expressed genes across all subpopulations. ; right Normalization was performed to obtain the normalized disease resistance response activity value. A co-expression network of disease resistance response genes was constructed based on the gene expression levels of all cell subpopulations, and the betweenness centrality of each cell subpopulation in the co-expression network of disease resistance response genes was calculated. And calculate the maximum value of the median centrality for all cell subsets. ,right Normalization is performed to obtain the normalized network hub status value. ; Calculate the first pivotal score of individual cell subpopulations ,in, The weighting coefficients, ranging from 0.4 to 0.6, are used to calculate the pivotal scores for each cell subpopulation. With preset threshold When a comparison is performed, When the threshold requirement is met, the cell subpopulation that meets the threshold requirement is identified as the disease-resistant cell subpopulation.

4. The method for screening polymorphic genetic markers for disease resistance traits in livestock according to claim 1, characterized in that, In S300, the specific surface markers of the disease-resistant cell subset are determined according to the cell type: When the disease-resistant cell subset is dendritic cells, the surface markers are a combination of CD11c and CD103; When the disease-resistant cell subset is macrophages, the surface marker is CD14; When the disease-resistant cell subset is cytotoxic T cells, the surface marker is CD8α.

5. The method for screening polymorphic genetic markers for disease resistance traits in livestock according to claim 1, characterized in that, In S300, the cell sorting technology is flow cytometry cell sorting technology, and the sorting steps of the flow cytometry cell sorting technology are as follows: The single-cell suspension of the sample was stained with fluorescently labeled surface marker antibodies; Set up negative and positive controls and adjust the flow cytometry sorting parameters; The stained single-cell suspension was sorted, and the purity of the purified key disease-resistant cell subpopulation was set at a threshold of 95%. The purity was detected by flow cytometry to detect the positive rate of surface markers in the sorted cells.

6. The method for screening polymorphic genetic markers for disease resistance traits in livestock according to claim 1, characterized in that, In the S400 process, genomic DNA is extracted using the phenol-chloroform method, and the extracted genomic DNA meets the requirements of a concentration ≥50 ng / μL and an OD260 / 280 ratio of 1.8-2.

0. Total RNA was extracted using the Trizol method, and the extracted total RNA was ≥200 ng / μL with an OD260 / 280 ratio of 2.0-2.

2.

7. The method for screening polymorphic genetic markers for disease resistance traits in livestock according to claim 1, characterized in that, The working principle of the whole-genome SNP typing is as follows: the genomic DNA of the purified disease-resistant key cell subpopulation is fully sequenced by whole-genome resequencing technology to obtain the complete sequence information of the genome, and the single nucleotide polymorphism (SNP) sites in the genome are identified by bioinformatics analysis to construct the SNP genotype matrix. The working principle of the transcriptome sequencing is as follows: using high-throughput sequencing technology, the total RNA of the purified disease-resistant key cell subpopulation is sequenced to obtain the expression information of all mRNAs in the cell, the expression level of each gene is quantitatively analyzed, and a gene expression matrix is ​​constructed.

8. The method for screening polymorphic genetic markers for disease resistance traits in livestock according to claim 1, characterized in that, In the S400, the working principle of using a linear mixture model for eQTL association analysis is as follows: The SNP genotype matrix was used as the independent variable and the gene expression matrix as the dependent variable. Individual sex, individual age and sequencing batch were also included as covariates in the linear mixed model. pass Calculate the significance of eQTL associations Value, of which, For the first The SNP locus and the first Significance of eQTL associations for gene expression levels value, For association analysis Statistic, This represents the mean gene expression level. The variance of gene expression levels; The presupposed standard for the first eQTL association significance requirement is: eQTL association significance. Value < .

9. The method for screening polymorphic genetic markers for disease resistance traits in livestock according to claim 1, characterized in that, In step S500, when performing genome-wide association analysis between the specific eQTL locus dataset and livestock disease resistance / susceptibility phenotype data, the genotype of the eQTL locus is used as the independent variable and the disease resistance / susceptibility phenotype of the livestock is used as the dependent variable to calculate the significance of the association between each eQTL locus and the disease resistance phenotype. value; The pre-set standard for the second association significance requirement is: the significance of the association between eQTL loci and the disease resistance phenotype. value < ; The preset criteria for the candidate genetic markers are: they meet the second association significance requirement with the disease resistance phenotype, and the eQTL target genes corresponding to the candidate genetic markers are disease resistance-related functional genes, including immune regulation genes, inflammatory response genes, and pathogen recognition receptor genes.

10. An application of a method for screening polymorphic genetic markers of disease resistance traits in livestock, applicable to the method for screening polymorphic genetic markers of disease resistance traits in livestock as described in any one of claims 1-9, characterized in that, This includes molecular marker-assisted selection breeding for disease-resistant livestock strains, genetic improvement of disease-resistant traits in livestock, and preparation of test kits for disease-resistant traits in livestock.

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