Meat duck whole genome molecular probe combination, 50k gene chip and application thereof

Abstract

The present application belongs to the technical field of gene detection and gene molecular breeding, and particularly relates to a meat duck whole genome molecular probe combination based on molecular phenotype screening, a 50K gene chip and application thereof. The present application provides a molecular probe combination of a marker site combination for meat duck whole genome selection and a gene chip, which simultaneously covers 7 representative meat duck breeds, 71 economic traits, has rich polymorphism in a meat duck population, is more targeted, and has lower detection cost and faster speed compared to high-throughput sequencing. The present application mines relevant sites for meat duck growth, feed efficiency, slaughter, reproduction, egg quality and various types of molecular phenotypes, designs a breeding chip, has higher selection accuracy compared to high-throughput sequencing technology, has high breeding value, can be widely applied to genotype detection of meat duck breeding, and has a pioneering significance for meat duck genome selection breeding.

Description

Technical Field

[0001] This invention belongs to the field of gene detection and gene molecular breeding technology, specifically involving a combination of molecular probes for the whole genome of meat ducks based on molecular phenotypic screening and a 50K gene chip and their application. Background Technology

[0002] The meat duck industry, as one of the most dynamic and distinctive sectors of my country's animal husbandry, saw a national output exceeding 5 billion ducks in 2024. Currently, white-feathered meat ducks dominate, accounting for 83.3% of the total. Meat ducks not only provide society with a large quantity of meat, down products, and a wide variety of by-products, but also play a vital role in enriching people's dietary culture. Meat duck breeding contributes over 40% to the development of the meat duck industry. Current meat duck breeding techniques mainly rely on traditional direct selection of individual phenotypes, family selection, and BLUP selection, but there is still significant room for improvement in selection efficiency. Genome-wide selection (WHM) technology is a breeding technique that uses genetic markers covering the entire genome to predict phenotypes. Compared to traditional breeding methods, this technology enables earlier selection, shortens generation intervals, and improves the accuracy of breeding value estimation.

[0003] Genotyping is a prerequisite for genome-wide selection breeding. Currently, the main method for genotyping in whole-genome selection of meat ducks is next-generation resequencing. While this technology can detect markers at the whole-genome level, the sequencing cost remains high, the sequencing cycle is long, and the subsequent marker data analysis is cumbersome, making it difficult to widely apply in breeding. Gene chip technology is a highly efficient and low-cost genome breeding technology with advantages such as short cycle time, low cost, and high analysis efficiency, and has been widely used in livestock and poultry genome-wide selection breeding. However, although liquid-phase chips for ducks exist, these chips cannot meet the requirements of whole-genome selection breeding. Their locus design is based on only a limited number of trait-related loci, and there is a lack of practical validation of the application effects of whole-genome selection. Therefore, there is an urgent need to develop universal breeding chips for economic traits such as growth, feed efficiency, slaughter, reproduction, and meat quality in meat ducks to meet the comprehensive breeding application needs of genome-wide selection technology for multiple traits, and to accelerate the application and promotion of genome-wide selection technology in meat ducks. Summary of the Invention

[0004] To overcome the shortcomings of existing technologies, this invention provides a whole-genome breeding chip for meat ducks and its application method. The chip contains a series of core loci significantly associated with 71 traits, including growth, feed efficiency, slaughter, reproduction, egg quality, and meat quality. The chip also includes background loci for meat ducks, ensuring the accuracy of the chip's detection. It can be widely used for whole-genome selection breeding of various economic traits in meat ducks, marker-assisted breeding, trait-related genotype identification, kinship identification, population genetic diversity analysis, and genome-wide association studies.

[0005] This invention discloses a molecular probe array of marker sites for whole-genome selection of meat ducks. The marker site array consists of 53,618 molecular markers, namely 53,130 SNPs and 488 INDELs. The location information of the 53,130 SNPs is shown in Table 1, and the location information of the 488 INDELs is shown in Table 2. The reference genome for the physical location is the meat duck reference genome IASCAAS_PekinDuck_T2T.

[0006] Table 1: Location information of 53,130 SNPs

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[0112] Table 2: Location information of 488 INDELs

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[0116] The aforementioned combination of whole-genome molecular probes for meat ducks was used in the preparation of meat duck genome gene chips.

[0117] The present invention also discloses a 50K gene chip for whole-genome selection of meat ducks, wherein the chip is loaded with the aforementioned combination of whole-genome molecular probes for meat ducks.

[0118] Preferably, the 50K gene chip used for whole-genome selection of meat ducks is a liquid-phase chip.

[0119] The present invention also discloses a kit for genotyping of meat ducks, the kit comprising the aforementioned combination of whole genome molecular probes of meat ducks or the aforementioned 50K gene chip of whole genome of meat ducks.

[0120] The application of the aforementioned whole-genome molecular probe combination for meat ducks, the aforementioned whole-genome gene chip for meat ducks, or the aforementioned kit for genotyping of meat ducks in the selection of meat duck genomes.

[0121] Application of the aforementioned whole-genome molecular probe combination for meat ducks, the aforementioned whole-genome gene chip for meat ducks, or the aforementioned kit for genotyping of meat ducks in meat duck breed identification, breed tracing, or kinship identification.

[0122] The application of the aforementioned whole-genome molecular probe combination for meat ducks, the aforementioned whole-genome gene chip for meat ducks, or the aforementioned kit for genotyping of meat ducks in whole-genome association analysis or trait-related gene localization of meat ducks.

[0123] The application of the aforementioned whole-genome molecular probe combination for meat ducks, the aforementioned whole-genome gene chip for meat ducks, or the aforementioned kit for genotyping of meat ducks in the analysis of genetic diversity of meat ducks.

[0124] Applications of the aforementioned whole-genome molecular probe combination for meat ducks, the aforementioned whole-genome gene chip for meat ducks, or the aforementioned kit for genotyping of meat ducks in the analysis, improvement, protection, or pedigree reconstruction of meat duck germplasm resources.

[0125] Compared with the prior art, the present invention has the following beneficial effects:

[0126] The whole-genome molecular probe combination and whole-genome chip for meat ducks described in this invention are designed for whole-genome sequencing of meat duck breeds. They exhibit rich polymorphisms within the meat duck population, are more targeted, and are lower in cost and faster than high-throughput sequencing. The chips target relevant loci for meat duck growth, feed efficiency, slaughter, reproduction, egg quality, and various molecular phenotypes, and are designed for breeding. Compared to high-throughput sequencing technology, they offer higher selection accuracy and have significant breeding value. They can be widely applied to genotype detection in meat duck breeding and are groundbreaking in the field of genomic selection breeding for meat ducks. Attached Figure Description

[0127] Figure 1 Flowchart for designing 50K gene chip loci for meat ducks;

[0128] Figure 2 Distribution map of different chromosomes for 50K gene chip marker sites in meat ducks;

[0129] Figure 3 A distribution map of the number of different chromosomes at 50K gene chip marker sites in meat ducks;

[0130] Figure 4 This is a graph showing the results of the genome-wide association analysis of plasma cholinesterase.

[0131] Figure 5 Principal component analysis plots of test data for two strains. Detailed Implementation

[0132] The technical solution of the present invention will be described in detail below with reference to embodiments. The following embodiments are only used to more clearly illustrate the present invention and should not be construed as limiting the scope of protection of the present invention. Unless otherwise stated, the technical or scientific terms used in the present invention have the ordinary meaning understood by those skilled in the art. Unless otherwise specified, the experimental methods in the following embodiments are conventional methods, performed according to the techniques or conditions described in the literature in the field or according to the product instructions. Unless otherwise specified, the materials, reagents, etc., used in the following embodiments are commercially available.

[0133] like Figure 1 As shown, the preparation method of the liquid-phase gene chip for 50K breeding of meat ducks includes the following steps:

[0134] (1) In a breeding population of 3795 meat ducks, considering 71 traits in seven categories of duck growth, feed efficiency, carcass, meat quality, reproduction and egg quality, genome-wide association analysis (GWAS) of genotype and phenotypic relationship was conducted based on a mixed linear model. Significant GWAS signals were removed by linkage disequilibrium (LD) to obtain functional markers related to growth, feed efficiency, slaughter traits, meat quality traits, reproductive traits and egg quality traits.

[0135] (2) Phenotypic results of 16 plasma biochemical indicators were determined: alanine aminotransferase, aspartate aminotransferase, cholinesterase, total protein, albumin, total bilirubin, direct bilirubin, alkaline phosphatase, lactate dehydrogenase, glucose, uric acid, total cholesterol, triglycerides, high-density lipoprotein cholesterol, low-density lipoprotein cholesterol and phosphorus. Using genome-wide association analysis, significant GWAS signals were deredundant by LD to obtain functional markers affecting plasma biochemical indicators.

[0136] (3) 1174 and 1018 metabolites were identified in muscle and plasma, respectively. Using genome-wide association analysis and redundancy removal of metabolites, functional markers affecting the abundance of muscle and plasma metabolites were obtained.

[0137] (4) The molecular phenotypes of microbial amplicon sequence variants (ASVs) in cecal contents were identified, and functional markers affecting the abundance of cecal microorganisms were obtained by genome-wide association analysis and redundancy removal using LD.

[0138] (5) Functional markers regulating gene expression were obtained by cis-eQTL localization, conditional eQTL analysis, and LD redundancy removal of genes in the pectoral muscle, liver, and pancreas.

[0139] (6) Download resequencing data of 371 ducks of 6 breeds from the NCBI SRA database, including 166 Shaoxing ducks, 122 wild ducks, 33 Liancheng white ducks, 25 Z-type Beijing ducks, 17 Cherry Valley ducks, and 8 Maple Leaf ducks. Genotypes were identified and used for selection signal (Fst) analysis to screen for selected functional markers.

[0140] (7) Select sites that meet the following criteria: heterozygosity <50% (65% when filling gaps), deletion rate <10%, minimum allele frequency ≥0.35 (65% when filling gaps), and select background sites according to the principle of uniform distribution.

[0141] (8) Using a probe design system, qualified probes that meet the above functional sites and background sites are screened to create a 50K gene chip for meat duck genome breeding as described above.

[0142] Example 1: Preparation of 50K breeding chip for meat ducks

[0143] I. Construction of breeding populations and determination of phenotypic traits

[0144] The breeding population used in this invention all comes from the Zhongxin White-feathered Meat Duck resource population jointly established by the Beijing Institute of Animal Husbandry and Veterinary Medicine of the Chinese Academy of Agricultural Sciences and Shandong Zhongxin Food Co., Ltd. A total of 3795 Zhongxin White-feathered Meat Ducks from three generations between 2020 and 2023 were selected for the experiment. Eleven growth and feed efficiency traits were measured, including 14-day entry weight, 42-day exit weight, weight gain, metabolic weight, feed intake, feed conversion ratio (FCR), residual feed intake (RFI), breast muscle width, keel length, breast muscle thickness (measured by ultrasound), and breast muscle volume. Among them, a 42-day slaughter experiment was conducted on 1,019 meat ducks, and 38 slaughter traits were measured, including carcass weight / percentage, eviscerated weight / percentage, foot weight / percentage, breast skin weight / percentage, neck skin weight / percentage, breast muscle weight / percentage, pectoral minor muscle weight / percentage, side leg weight / percentage, wing root weight / percentage, two-segment wing weight / percentage, whole wing weight / percentage, head weight / percentage, neck weight / percentage, skeleton weight / percentage, intestine weight / percentage, liver weight / percentage, heart weight / percentage, gizzard weight / percentage, and abdominal fat weight / percentage, as well as 6 meat quality traits, including breast muscle shear force, water loss rate, pH, meat color a, meat color b, and meat color L. Seven reproductive traits were measured in 1,814 female ducks, including the number of eggs laid at 210 days, 280 days, 300 days, early laying period (25-28 weeks), peak laying period (29-47 weeks), late laying period (48-60 weeks), and laying rate (25-60 weeks). Nine egg quality traits were also measured, including egg weight, egg length diameter, egg short diameter, egg shape index, eggshell strength, eggshell weight, yolk weight, yolk height, and Haugh units.

[0145] II. Genotyping

[0146] Blood was collected from 3795 meat ducks in the aforementioned breeding population, and whole-genome DNA was extracted using the standard phenol-chloroform method. A DNA library was constructed, and after the library passed evaluation, PE150 sequencing was performed using the DNBSEQ-T7 sequencing platform. Sequencing sequences obtained from the sequencing were processed using Fastp (v.0.23.2) software to remove sequencing adapters and low-quality bases, yielding Clean Reads. The mem algorithm in BWA (v.0.7.17-r1188) software was used to align the Clean Reads to the reference genome IASCAAS_PekinDuck_T2T. PCR repetitive sequences in the post-alignment BAM file were removed using Picard (v.1.123) software. Genomic variants were extracted and identified using the HaplotypeCaller module of GATK (v.4.6.1.0). Identified variant types included SNPs and Indels. Quality control was performed on the merged genotype data of 3,795 meat ducks. The quality control retained 10,349,420 genomic variation sites located on autosomes with a minimum gene frequency greater than 0.05 and a variation detection rate greater than 0.9. These sites were used for downstream meat duck chip design.

[0147] III. Molecular phenotyping

[0148] From the breeding population used for slaughter trait determination, 507 meat ducks were selected for molecular phenotyping at 42 days of age, following the principle of family coverage. The molecular phenotypes measured included: plasma biochemical indicators, abundance of metabolites in muscle and plasma, abundance of cecal microorganisms, and expression levels of various genes in pectoral muscle, liver, and pancreas tissues.

[0149] (1) Plasma biochemical indicators

[0150] Blood was collected from the wing veins of 507 42-day-old broiler ducks using EDTA anticoagulant tubes. The blood was centrifuged at 3000 rpm for 10 minutes, and the supernatant was collected and stored at -80°C for plasma biochemical and metabolite assays. Sixteen biochemical indicators were measured in the plasma using a fully automated biochemical analyzer (Hitachi 7080), including alanine aminotransferase (ALT), aspartate aminotransferase (AST), cholinesterase (CHE), total protein (TP), albumin (ALB), total bilirubin (TBIL), direct bilirubin (DBIL), alkaline phosphatase (ALP), lactate dehydrogenase (LDH), glucose (GLU), uric acid (UA), total cholesterol (CHOL), triglycerides (TG), high-density lipoprotein cholesterol (HDLC), low-density lipoprotein cholesterol (LDLC), and phosphorus (P). Reagents for these biochemical assays were purchased from Mindray Bio-Medical Electronics Co., Ltd.

[0151] (2) Abundance of metabolites in muscle and plasma

[0152] Muscle metabolite determination: 100 mg of breast muscle from 42-day-old ducks was collected, ground in liquid nitrogen, placed in an EP tube, and 500 μL of 80% methanol was added. The mixture was vortexed, incubated on ice for 5 min, and centrifuged at 15000 g and 4℃ for 20 min. A certain amount of the supernatant was diluted with mass spectrometry-grade water to a methanol content of 53%, centrifuged at 15000 g and 4℃ for 20 min, and the supernatant was collected and analyzed by liquid chromatography-mass spectrometry (LC-MS). Plasma metabolite determination: 100 μL of sample was placed in an EP tube, and 400 μL of 80% methanol aqueous solution was added. Other steps were the same as for muscle metabolite determination. The raw file (.raw) obtained from mass spectrometry was imported into CompoundDiscoverer 3.3 software for spectral processing and database search to obtain qualitative and quantitative results of metabolites. The quantitative results were converted using log2 for final statistical analysis.

[0153] (3) Expression levels of various genes in pectoral muscle, liver, and pancreas tissues

[0154] Tissues from the pectoral muscle, liver, and pancreas of 42-day-old ducks were collected, placed in cryovials, and stored in liquid nitrogen for later use. RNA was extracted from the tissue samples using the Trizol method. After RNA testing was passed, mRNA was enriched from the total RNA using Oligo dT magnetic beads to construct a cDNA library. After library testing was passed, paired-end PE150 sequencing was performed on the Illumina platform, with a sequencing throughput greater than 6G. After sequencing, adapter sequences were removed using Cutadapt (v2.8), and low-quality bases were removed using Fastp (v.0.23.2). The quality-controlled reads were aligned to the duck reference genome IASCAAS_PekinDuck_T2T using STAR (v2.7.10b). The aligned files and gene annotation GTF files were processed using Stringtie (v2.2.1), and gene expression (TPM) was quantified. Genes with TPM > 0.1 expression levels in at least 20% of the samples and located on autosomes were retained, resulting in 15,406 pectoral muscle genes, 14,228 liver genes, and 12,433 sebaceous gland genes as molecular phenotypes for downstream microarray site mining.

[0155] (4) Abundance of cecal microorganisms

[0156] Cecal contents were collected from 42-day-old broiler ducks, placed in cryovials, and stored in liquid nitrogen for later use. DNA was extracted from the cecal contents using the Qiagen microbial DNA extraction kit. After the DNA samples passed amplification and testing, the PCR products were mixed and purified. Then, 16S amplicon libraries were prepared through end repair, A-tailing, sequencing adapter addition, and purification. The amplified region was V3-V4. After library approval, paired-end PE150 sequencing was performed on an Illumina platform, with a sequencing depth of 20M reads. After sequencing, adapter sequences were removed using Cutadapt v2.8, and reads from each sample were assembled using vsearch (v2.28.1) software to obtain raw tag data. The raw tags were then filtered using FastP software to obtain high-quality tag data (Clean Tags). The Clean Tags were compared with the Silva database (https: / / www.arb-silva.de / ) using vsearch software, and chimeric sequences were removed to obtain the final effective tags. Based on sequence similarity, the sequences were clustered with 100% similarity using vsearch to generate amplicon variant sequences (ASVs). Plasmids and non-bacteria were removed through species annotation, and abundance was quantified. ASVs identified in at least 20% of the samples were retained, resulting in 1751 ASVs as microbial molecular phenotypes for downstream microarray site mining.

[0157] IV. Design of 50K breeding chip sites for meat ducks

[0158] (1) Design of loci related to important economic traits

[0159] Important economic traits in ducks included 71 traits across seven categories (growth, feed efficiency, carcass, meat quality, reproduction, and egg quality). Genome-wide association analysis (GWA) between genotype and phenotypic traits was performed using a mixed linear model. Covariates included batch, sex, season, and the first three principal components of genotype. The association analysis was conducted using GCTA (v1.93.0) software with mlma parameters. The p-value was p < 1 × 10⁻⁶. -5 After filtering the GWAS results, 1721 significant loci for growth traits, 525 significant loci for feed efficiency traits, 27014 significant loci for slaughter traits, 1023 significant loci for meat quality traits, 861 significant loci for reproductive traits, and 1799 significant loci for egg quality traits were obtained.

[0160] Because strong linkage disequilibrium (LD) exists between significant sites within the same signal in GWAS results, considering a single marker site is sufficient to capture the genetic variance of the entire signal when designing the chip. Therefore, further redundancy removal of significant GWAS sites is necessary using LD. First, SNPs are ranked according to the significance of their p-values ​​from the GWAS results, resulting in a candidate SNP list. The top SNP in the ranking is selected, and the LD degree r² between this SNP and its upstream and downstream 500 kb range is calculated. Candidates with excessively high linkage (r²) to the current top SNP are removed. 2 If the p-value is greater than 0.2, the top SNP is retained. If no SNP with a high linkage to the current top SNP exists, the top SNP is considered an isolated SNP caused by a false positive and is not retained. This completes the removal of the first top SNP and the reduction of the candidate list. Next, this process is repeated to remove the next SNP with the highest p-value in the new candidate list, until all SNPs are detected. Through the above analysis, 158 independent markers for growth traits, 82 independent markers for feed efficiency, 3386 independent markers for slaughter traits, 135 independent markers for meat quality traits, 101 independent markers for reproductive traits, and 284 independent markers for egg quality traits were obtained.

[0161] (2) Design of molecular phenotypic related sites for plasma biochemical indicators

[0162] Genome-wide association analysis (GWAS) was performed on resequencing genotypes and 16 plasma biochemical marker phenotypes using a mixed linear model, considering batch, sex, and the first three principal components of genotype as covariates. GWAS analysis was conducted using GCTA (v1.93.0) software with mlma parameters. Results were obtained with p < 1 × 10⁻⁶. -5 Filtering the GWAS results yielded 6206 significant loci associated with 16 plasma biochemical markers, which were then analyzed using LDr. 2 >0.2 Redundancy removal (method as above) identified 2287 QTLs for biochemical indicators. QTLs with only 1 marker were deleted, resulting in 737 candidate QTLs affecting 16 plasma biochemical indicators. The QTL with the most significant P value was selected as the representative marker for each QTL, resulting in 737 candidate markers.

[0163] (3) Design of molecular phenotypic related sites of plasma and muscle metabolites

[0164] 1174 and 1018 metabolites were identified in muscle and plasma, respectively. After log2 transformation of metabolite abundance, rank-based inverse normal transformation was performed using GenABEL (R package) for multiple data. A mixed linear model (--mlma) in GCTA software was used to perform association analysis between SNPs and each metabolite molecular phenotype. The model considered three principal components: batch, sex, and genotype. The significance threshold for the association between genotype and metabolite molecular phenotype was P < 5 × 10⁻⁶. -8 26,622 sites were found to significantly affect 610 muscle metabolites, and 23,189 sites were found to significantly affect 709 plasma metabolites. This was determined by LDr... 2 >0.2 Redundancy removal and deletion of QTLs with only 1 label (method as above) yielded 2308 independent candidate labels affecting 317 muscle metabolites and 2144 independent candidate labels affecting 494 plasma metabolites.

[0165] (4) Design of molecular phenotypic related sites of cecal microorganisms

[0166] Based on the identified molecular phenotypes of 1751 microbial ASVs, the total sequencing count for each sample was corrected to obtain microbial abundance proportion data. Data with an abundance of 0 were filled using geometric Bayesian multiplication, and the filled data were then corrected using a central logarithmic transformation. A genome-wide association analysis was performed on each of the resequencing genotypes and the molecular phenotypes of the 1751 microbial ASVs using a mixed linear model, considering batch, sex, and the first three principal components of genotype as covariates. The significance threshold for the association between genotype and metabolite molecular phenotype was P < 5 × 10⁻⁶. -8 A total of 7201 sites were found to significantly affect 961 microbial ASVs. This was determined using LD r 2 >0.2 Redundancy was removed and QTLs with only one label were deleted (using the same method as above), resulting in 748 independent candidate labels affecting the abundance of 317 cecal microorganisms.

[0167] (5) Design of transcriptional molecular phenotypic related sites in pectoral muscle, liver, and pancreas

[0168] In pectoral muscle, liver, and pancreas, the expression levels (TPMs) of 15,406, 14,228, and 12,433 genes expressed on at least 20% of the samples at autosomes, respectively, were used as molecular phenotypes. After quantile correction and inverse normal transformation, confounding factors were calculated using the Peer software R package based on the relationships between the corrected molecular phenotypes. The top 20 confounding factors were retained as covariates for eQTL localization. Covariates also considered sex and batch number, as well as the first three principal components of genotype. The model used was as follows:

[0169] g=α+γx+βs+ε

[0170] Where g represents gene expression level, s represents random effect (i.e., SNP genotype result), x represents fixed effect, γ and β represent association matrices, a represents constant (intercept), and ε represents residual. Cis-eQTL analysis considers 1Mb of SNPs upstream and downstream of each gene. The TensorQTL permutation method is used to perform a beta distribution permutation test on the sampled SNPs. The significance Pm value of each gene is obtained through the permutation test. Genes with a Pm value less than 0.05 after FDR correction are used as the sampling significance Pm value for each gene. Based on this P value, the beta distribution parameter of the association analysis between SNPs and genes in the TensorQTL nominal mode is calculated, thus obtaining the cis-eQTL significance threshold P for each gene. Cis-eQTL results in the TensorQTL nominal mode are then selected based on the threshold. 7,078,496 mutations significantly affecting the expression of 11,832 cis genes were identified in muscle tissue, 6,629,799 mutations significantly affecting the expression of 10,648 cis genes were identified in liver tissue, and 4,888,015 mutations significantly affecting the expression of 7,821 cis genes were identified in pancreatic tissue.

[0171] To screen for causative mutations regulating gene expression from cis-eQTLs, fine mapping of cis-eQTLs was performed for each gene. First, prior information obtained through posterior deterministic approximation (DAP) was used, which considers the distance to the transcription start site (TSS) and linkage disequilibrium (LD) information between SNPs. 2>0.25), the distance between the variant and the TSS of the target gene was calculated using TORUS software to estimate prior information. Any variant with a posterior inclusion probability (PIP) >0.8 calculated using DAP was considered a fine-mapping result (causative mutation). Through the above analysis, 9650, 8162, and 5184 fine-mapping predicted causative mutations were identified in muscle, liver, and pancreas tissues, respectively. In addition, this invention performed conditional eQTL analysis on the cis-QTL results, extracting the cis-QTLs with the most significant P-values ​​for each gene and placing them as covariates in the first round of independent mutations. eQTL mapping was then performed again to detect the presence of eQTL signals in the second round, and the most significant eQTL signals were placed as covariates in the second round of independent mutations. eQTL mapping was then performed again until no additional independent signals were detected. This process was completed using the cis_independent mode of TensorQTL software. As a result, 22013, 14356, and 8399 independent mutations were obtained in pectoral muscle, liver, and pancreas, respectively. This invention combines causal mutations and independent mutations obtained from two strategies, taking the union to obtain 66,177 key regulatory sites for gene expression. These sites are then analyzed using LD r... 2 >0.2 Redundancy removal (same method as above) yielded 29,426 candidate regulatory markers for gene expression.

[0172] (6) Selecting signal sites for design

[0173] Resequencing data of 371 ducks from 6 breeds were downloaded from the NCBI SRA database (https: / / www.ncbi.nlm.nih.gov / sra / ), including 166 Shaoxing ducks, 122 wild ducks, 33 Liancheng white ducks, 25 Z-type Peking ducks, 17 Cherry Valley ducks, and 8 Maple Leaf ducks. Genotypic information was obtained through steps such as removing low-quality reads, genomic alignment, and variant identification (using the same methods as genotyping described above). This data was then merged with genotypic data from 3795 Zhongxin white-feathered meat ducks. Quality control was performed, retaining 10,349,420 genomic variant sites located on autosomes with a minimum gene frequency greater than 0.05 and a variant detection rate greater than 0.9, for use in selection signal (Fst) analysis.

[0174] To screen for specific markers in the Zhongxin white-feathered broiler duck, this invention compares the Zhongxin white-feathered broiler duck with six other breeds. Selection signal Fst analysis is performed on individual loci, and the top 0.001% of loci with the highest Fst values ​​are considered selected loci. The selection signal analysis was performed using vcftools (v0.1.13) software. After Fst analysis and merging, a total of 7662 positively selected loci for the Zhongxin white-feathered broiler duck were identified. LD r 2 >0.2 Redundancy removal (same method as above) yields 3568 selected tags.

[0175] Based on the above design principles for functional sites, a total of 40,835 functional markers were obtained.

[0176] (7) Design of background sites

[0177] The selection criteria for background sites on the microarray must meet the following conditions: heterozygosity <50% (65% when filling gaps), deletion rate <10%, minimum allele frequency ≥0.35 (65% when filling gaps), and 20,082 background sites selected according to the principle of uniform distribution.

[0178] (8) Site evaluation and experimental verification

[0179] The functional site evaluation criteria required a deletion rate <20%, a minimum allele frequency >0.1, and removal of non-secondary SNPs, resulting in 40,055 functional markers. Primers and probes were designed based on these functional markers and background site sequence information. The probes needed a GC content between 30% and 70%, and the number of homologous regions in the genome should be ≤5. Furthermore, the genotypes of an additional 200 ducks were detected using newly designed probes, and the sequencing depth of the designed sites on the chip was at least 20% of the average sequencing depth. This yielded 53,618 valid markers, which served as the final set of sites for the 50K gene chip design in meat ducks. V. Preparation and Detection of the 50K Gene Chip for Meat Ducks

[0180] The 50K gene chip for meat ducks of this invention includes 34,744 functional markers and 18,874 background loci. In terms of variation type, the chip includes 53,130 SNPs and 488 insertions / deletions (INDELs). Using the GenoBaits liquid-phase probe capture technology, a 50K locus panel was designed and synthesized based on the selected 50K loci. Two hundred samples were tested, and sequencing and data analysis showed that the locus detection rate was between 99.4% and 99.8%, with an average detection rate of 99.6%. The overall distribution of the loci on the chip is shown below. Figure 2 As shown, the number of chip loci distributed across each chromosome is as follows: Figure 3 As shown.

[0181] Example 2: Evaluation of the Application Effect of 50K Gene Chip in Genomic Selection for Meat Ducks

[0182] A slaughter trial was conducted on 330 42-day-old Sino-American white-feathered broiler ducks. Thirteen traits were measured, including 42-day body weight, feed intake, fattening weight gain, feed conversion ratio (FCR), residual feed intake (RFI), carcass weight, foot weight, breast muscle weight, side leg weight, total wing weight, neck weight, intestine weight, and gizzard weight. Blood samples were collected, and genotyping was performed using the GenoPrep Blood DNA Rapid Extraction Kit (magnetic bead method) and a 50K gene chip for broiler ducks. BLUP breeding values ​​were calculated based on the animal model, as follows:

[0183] y = Xb + Za + e,

[0184]

[0185] y represents the phenotypic value; b represents the fixed effect, including sex and batch; a represents the random effect of individual molecular phenotype. Where A is a kinship matrix obtained through pedigree information technology. X represents the additive genetic variance; X and Z are the design matrices for fixed and random effects, respectively; e is the residual. The variance components are estimated using the AI-REML algorithm, and the variance is calculated. The breeding value can be calculated using the following mixed model equations.

[0186]

[0187] The GBLUP model is similar to the BLUP model, the difference being that the A matrix is ​​replaced by the G matrix. The G matrix is ​​a kinship matrix constructed from genotype information, through... Calculate p i Let be the minimum allele frequency at the i-th locus, and M be the genotype information (0, 1, 2). The variance components are re-estimated using the AI-REML algorithm, and the calculation is performed. The genomic breeding value can be calculated using the following mixed model equations.

[0188]

[0189] The estimated breeding values ​​of the BLUP and GBLUP models were calculated using ASReml software. The breeding reference population consisted of 1319 ducks slaughtered in two generations from 2023 to 2024, while the predicted population consisted of 330 ducks in the third batch of breeding ducks in the 2024 generation. The reliability of the model predictions was calculated using the Pearson correlation coefficient between the estimated breeding values ​​and the actual phenotypic values. The difference in accuracy between BLUP based on pedigree and GBLUP based on the 50K gene chip for meat ducks was compared as an evaluation of the effectiveness of the chip-based genomic selection (Table 3). The results showed that the prediction accuracy of GBLUP based on the 50K gene chip for meat ducks was improved to some extent (0.009–0.188) across different traits, with an average accuracy improvement of 0.06, indicating that the 50K gene chip for meat ducks has a good application effect in genomic selection.

[0190] Table 3: Comparison of prediction accuracy between 50K gene chip and pedigree BLUP for meat ducks

[0191]

[0192] Example 3: Application of 50K gene chip in meat duck genome-wide association analysis

[0193] Blood samples were collected from 568 New Zealand white-feathered broiler ducks at 42 days. The supernatant was obtained by low-speed centrifugation, and plasma was obtained. Plasma cholinesterase levels were measured using a blood biochemistry analyzer. Whole-genome DNA was extracted from the lower sediment using the GenoPrep Blood DNA Rapid Extraction Kit (magnetic bead method). Targeted capture sequencing was performed using a 50K broiler duck gene chip, and the sequencing results were genotyped according to Example 1. Marker sites with a minimum allele frequency >0.05 (maf) and a deletion rate <0.1 (geno) were retained using PLINK software, while individuals with a genotyping deletion rate >0.1 (mind) were filtered out. Genome-wide association analysis was performed using a mixed linear model with GCTA software. The model is as follows:

[0194] y = Wɑ + ​​Xβ + u + e

[0195] Where y is the cholinesterase phenotype vector, α is the covariance, W is the covariance design matrix (including the first three principal components: sex, batch, and genotype), β is the marker effect vector, X is the marker genotype matrix, and u is the random multiple effects vector. G is the genomic kinship matrix, and e is the random residual. GWAS results are as follows: Figure 4 As shown, a significant signal was located on chromosome 9.

[0196] GWAS results filtered based on P < 5 × 10⁻⁸ yielded 67 signals significantly associated with plasma cholinesterase levels. The selected signal interval chr9:2762625-4191173 was annotated, containing 26 genes, including the BCHE gene. The BCHE gene has been reported to be associated with the Beijing duck. The plasma cholinesterase levels in the F2 hybrid population of wild ducks were significantly correlated (Xu et al., 2019). These results indicate that GWAS gene mapping analysis of traits can be carried out using the 50K gene chip of meat ducks, and good association analysis results can be obtained.

[0197] Example 4: Application of the 50K gene chip in the identification of different meat duck breeds

[0198] One hundred ducks each from two strains of white-feathered broiler ducks (L1 and L4) were collected, and blood samples were collected. Genomic DNA was extracted using the GenoPrep Blood DNA Rapid Extraction Kit (magnetic bead method) and sequenced using the "Guixin-1" gene chip. The sequencing results were then used for genotyping. PLINK software was used to retain marker sites with a minimum allele frequency >0.05 (--maf) and a deletion rate <0.1 (--geno), while filtering individuals with a genotyping deletion rate >0.1 (--mind). Principal Component Analysis (PCA) was then performed on the test data using the --pca parameter in PLINK software. The results are shown below. Figure 5 As shown, the L1 and L4 strains are clearly distinguishable overall. These results demonstrate that the 50K breeding gene chip for meat ducks can be effectively applied to the identification of different meat duck strains.

Claims

1. A molecular probe array for detecting marker sites in the whole genome selection of meat ducks, characterized in that, The marker site combination consists of 53,618 molecular markers, namely 53,130 SNPs and 488 INDELs. The location information of the 53,130 SNPs is shown in Table 1, and the location information of the 488 INDELs is shown in Table 2. The reference genome for the physical location is the duck reference genome IASCAAS_PekinDuck_T2T, and the meat duck is the Sino-New Zealand white-feathered meat duck.

2. The application of the molecular probe combination for detecting marker sites in the whole genome selection of meat ducks as described in claim 1 in the preparation of a whole genome gene chip for meat ducks, wherein the meat duck is the Sino-New Zealand white-feathered meat duck.

3. A 50K gene chip for whole-genome selection in meat ducks, characterized in that, The chip is loaded with a molecular probe array as described in claim 1 for detecting marker site combinations for whole-genome selection of meat ducks, wherein the meat duck is the Sino-New Zealand white-feathered meat duck.

4. The 50K gene chip for whole-genome selection of meat ducks according to claim 3, characterized in that, The chip is a liquid phase chip.

5. A kit for genotyping meat ducks, characterized in that, The kit comprises the molecular probe combination for detecting marker site combinations for whole-genome selection of meat ducks as described in claim 1 or the 50K gene chip for whole-genome selection of meat ducks as described in claim 3, wherein the meat duck is the Sino-New Zealand white-feathered meat duck.

6. The molecular probe combination for detecting marker site combinations for whole-genome selection of meat ducks as described in claim 1, or the 50K gene chip for whole-genome selection of meat ducks as described in claim 3, or the kit for genotyping of meat ducks as described in claim 5, in the application of meat duck genome selection, wherein the meat duck is the Sino-New Zealand white-feathered meat duck.

7. The molecular probe combination for detecting marker sites in whole-genome selection of meat ducks as described in claim 1, or the 50K gene chip for whole-genome selection of meat ducks as described in claim 3, or the kit for genotyping of meat ducks as described in claim 5, is used in meat duck breed identification, breed tracing, or kinship identification, wherein the meat duck is the Sino-New Zealand white-feathered meat duck.

8. The molecular probe combination for detecting marker site combinations for whole-genome selection of meat ducks as described in claim 1, or the 50K gene chip for whole-genome selection of meat ducks as described in claim 3, or the kit for genotyping of meat ducks as described in claim 5, in the application of whole-genome association analysis or trait-related gene localization of meat ducks, wherein the meat duck is the Sino-New Zealand white-feathered meat duck.

9. The molecular probe combination for detecting marker sites in whole-genome selection of meat ducks as described in claim 1, or the 50K gene chip for whole-genome selection of meat ducks as described in claim 3, or the kit for genotyping of meat ducks as described in claim 5, in the analysis of genetic diversity of meat ducks, wherein the meat duck is the Sino-New Zealand white-feathered meat duck.

10. The molecular probe combination for detecting marker sites in whole-genome selection of meat ducks as described in claim 1, or the 50K gene chip for whole-genome selection of meat ducks as described in claim 3, or the kit for genotyping of meat ducks as described in claim 5, is used in the analysis of meat duck germplasm resources, germplasm resource improvement, germplasm resource protection, or pedigree reconstruction, wherein the meat duck is the Sino-New Zealand white-feathered meat duck.

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