A duck whole genome chip and a manufacturing method and application thereof

Abstract

The present application relates to the technical field of molecular breeding, in particular to a duck whole genome chip and a manufacturing method and application thereof.The duck whole genome chip has 40876 probe target points, 25465 of which are derived from a duck reference genome, and the remaining 15411 are derived from duck breed specificity and trait-associated SNPs.The 40K whole genome liquid phase chip has an average detection rate of more than 99%, a total number of effective markers of more than 40,000, strong stability, and is suitable for genetic improvement and resource evaluation of duck breeds.In addition, the SNP sites contained in the chip are obtained by mining important traits of needle ducks, and the sites can be used to stably and efficiently perform genomic selection on important traits of ducks, which helps to accelerate the progress of duck breeding.

Description

Technical Field

[0001] This invention relates to the field of molecular breeding technology, specifically to a duck whole genome chip, its fabrication method, and its application. Background Technology

[0002] As the world's largest producer and consumer of duck meat, my country boasts a massive duck meat industry with an annual output exceeding 10 million tons. However, for a long time, my country has been reliant on foreign breeds for its ducks, with the high cost of importing breeding stock becoming a major bottleneck hindering the industry's development. Genotyping chips can identify genetic variations in animals. By comparing the genomes of different breeds or populations, genes or markers related to traits can be discovered. These genes or markers play a crucial role in animal genetic mechanism research and breeding, providing scientific and technological tools for breeding. However, currently, there are no genotyping chips specifically for duck meat.

[0003] Existing duck SNP chips mainly include "Chu Duck No. 1" 20K, "Yongqiang Muscovy Duck No. 1" 50K, "Danduoxin" 30K, "Qiangying No. 1" 60K, and "Beijing Duck" 60K. Patent announcement number CN118086527B discloses a 20K low-density SNP chip for egg-laying ducks based on targeted capture sequencing. This chip is mainly screened based on the whole-genome resequencing results of five local egg-laying duck breeds: Jingjiang Duck, Shaoxing Duck, Shanma Duck, Jinding Duck, and Youxian Ma Duck. Patent application CN117512121A discloses a SNP molecular marker for Jiaji duck, which includes 20,327 SNP sites. The physical locations of the 20,327 SNP sites are determined by sequence alignment based on the reference genome of Muscovy duck (sequence reference article: Chromosome-level genome assembly of the Muscovy duck provides insight in to fatty liver susceptibility, Biotechnology Project Registry No.: PRJCA009398 (Registry No.: GWHBJBF0000000)).

[0004] As can be seen from the above, existing duck SNP chips mainly focus on local ducks and lack information on duck genome variation, which cannot meet the needs of duck genome breeding and resource conservation. Therefore, there is an urgent need to develop gene chips that include duck breed-specific SNPs as well as SNPs related to important traits such as body weight, feed utilization efficiency, egg weight, and egg production. Summary of the Invention

[0005] The main objective of this invention is to provide a duck whole-genome chip, its fabrication method, and its applications. The duck whole-genome chip of this invention contains 40,876 probe targets, of which 26,824 are derived from the duck reference genome, and the remaining 15,411 are derived from duck breed-specific and trait-associated SNPs. The duck whole-genome chip of this invention can be used for duck genome selection breeding, genetic diversity analysis, breed identification, kinship identification, and genome-wide association analysis in my country, thus contributing to the protection, improvement, and breeding of duck germplasm resources in my country.

[0006] To achieve the above objectives, the present invention adopts the following technical solution:

[0007] In a first aspect, the present invention provides a combination of SNP molecular markers for duck genotyping, the combination of SNP molecular markers consisting of 40,876 SNP molecular markers, the positions of the SNP molecular markers on the duck reference genome ZJU1.0 as shown in Table 1 of the specification.

[0008] In a second aspect, the present invention provides a duck whole genome chip, wherein the chip is loaded with a combination of molecular probes for detecting the SNP molecular marker combinations described in the first aspect above.

[0009] Furthermore, the chip is a liquid phase chip.

[0010] In a third aspect, the present invention provides a method for fabricating the duck whole genome chip described in the second aspect above, the method comprising the following steps:

[0011] S1. Whole genome resequencing was performed on multiple duck breeds. After mutation identification and quality control, SNP sites were initially screened. S2. SNP sites related to duck economic traits and breed specificity were screened. S3. Background sites for the microarray were selected. S4. Supplementary sites were selected in large gap regions. S5. The sites selected in steps S2, S3 and S4 were merged, and SNP markers that were close to each other were filtered out. Based on the SNP site sequence information, primers were designed and molecular probes were synthesized to obtain the duck whole genome microarray.

[0012] Furthermore, the traits include, but are not limited to, body weight, feed utilization rate, egg weight, number of eggs laid, age at first laying, tibia length, and pectoral muscle thickness.

[0013] In a fifth aspect, the present invention provides the use of the SNP molecular marker combination for duck genotyping described in the first aspect above, and the duck whole genome chip described in the second aspect, wherein the use is any of the following:

[0014] (1) Application in duck trait improvement or prediction;

[0015] (2) Application in genome-wide association analysis or trait-related gene mapping in ducks;

[0016] (3) Application in duck genome selection breeding;

[0017] (4) Application in duck germplasm resource improvement;

[0018] (5) Applications in duck genetic diversity analysis, duck breed identification, breed tracing or kinship identification;

[0019] (6) Application in filling high-density chips.

[0020] In a sixth aspect, the present invention provides a method for predicting duck traits, the method comprising genotyping a sample to be tested using the duck whole genome chip described in the second aspect above, and making predictions based on the genotyping data using a model.

[0021] Furthermore, the traits include, but are not limited to, body weight, egg weight, number of eggs laid, age at first laying, tibia length, and pectoral muscle thickness.

[0022] Furthermore, the models include, but are not limited to, GBLUP and ssGBLUP.

[0023] The beneficial effects of this invention are reflected in:

[0024] This invention performs whole-genome resequencing on four duck strains (breeds), thus exhibiting rich polymorphism in duck populations. Furthermore, the 40K whole-genome liquid microarray described in this invention has an average detection rate of over 99%, with a total of more than 40,000 effective markers, demonstrating strong stability and making it suitable for genetic improvement and resource evaluation of duck breeds.

[0025] The SNP loci included in the chip described in this invention are mined from important duck traits. These loci enable stable and efficient genomic selection of important duck traits, facilitating faster breeding of these traits. Based on the genotyping data obtained from the chip described in this invention, the GBLUP or ssGBLUP models can significantly improve the accuracy of trait prediction.

[0026] The chip described in this invention is a liquid-phase chip based on targeted capture sequencing technology. It can not only genotype the target locus but also genotype SNPs within a certain range adjacent to the target locus, thus obtaining more genotyping information. Furthermore, compared to solid-phase chips, its detection process is simpler, has higher throughput, lower cost, and is easier to analyze, indicating a broad market prospect. Attached Figure Description

[0027] Figure 1 Distribution map of SNP loci on each chromosome;

[0028] Figure 2Comparison of population structure detection results between resequencing and the duck 40K whole-genome liquid-phase chip described in this invention: (a) PCA results detected by the duck 40K whole-genome liquid-phase chip described in this invention; (b) PCA results of the resequencing method. Detailed Implementation

[0029] It should be noted that the following detailed descriptions are exemplary and intended to provide further illustration of the invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0030] It should be noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the exemplary embodiments of the present invention. As used herein, the singular form is intended to include the plural form as well, unless the context clearly indicates otherwise. Furthermore, it should be understood that when the terms "comprising" and / or "including" are used in this specification, they indicate the presence of features, steps, operations, and / or combinations thereof.

[0031] To enable those skilled in the art to better understand the technical solution of the present invention, the technical solution of the present invention will be described in detail below with reference to specific embodiments.

[0032] Example 1: Method for fabricating a duck 40K whole genome liquid-phase chip

[0033] The manufacturing method includes the following steps:

[0034] Step S1: Multi-breed duck whole-genome resequencing. A total of 240 ducks from four breeds were selected, including 60 H1 strain (primary selected body weight), 60 H2 strain (primary selected feed utilization efficiency), 60 H3 strain (primary selected egg weight), and 60 H4 strain (primary selected egg production). After blood collection and DNA extraction, libraries were constructed for each qualified sample using standard procedures. All libraries were sequenced on an Illumina® HiSeq X 10 with an average sequencing depth of 10x. After preliminary quality control of the sequencing data, the data were aligned to the duck reference genome (ZJU1.0) using BWA software, and SNP mutations were identified using the Haplotype caller module of GATK4 software. SNPs with genotyping errors, minimum allele frequencies <5%, alignment rates <95%, and those not aligned to chromosomes 1-29, Z, or W were filtered out. After quality control, 9,821,917 SNPs were retained for subsequent analysis.

[0035] Step S2 involves using comparative genomics to screen for trait-related SNPs and breed-specific SNPs. A sliding window approach is used to analyze genetic differentiation among different breeds and other duck species, identifying selected regions in the genome for each breed. Considering the high genomic nucleotide diversity and heterozygosity of the population, the window size and step size are reduced to 40 Kb and 10 Kb, respectively, when calculating the Weir-Fst value for each window. The top 1% of outliers in each window are considered as hypothetical selected genomic regions. Genome-wide association analysis (GLM) is used to obtain SNP markers associated with body weight, feed utilization efficiency, egg weight, and egg production. This part uses a GLM model implemented using plink software. Integrating the information from significant GLM SNPs and selected regions obtained through comparative genomics screening, a total of 15,411 breed-specific and trait-related SNPs were identified.

[0036] Step S3: Microarray background site selection. The selection criteria are based on resequencing data from 240 ducks, meeting the following requirements: minimum sequencing depth ≥5x, deletion rate <10%, minimum allele frequency >0.35. Sites with low na, low het, and high maf are selected according to the principle of uniform distribution. Approximately 15M sites are selected for evaluation. The site evaluation parameters are: len 110-110, gc 30-70, hom 5, d 2, size 120, dis 10. From the evaluated sites, sites with low na, low het, and high maf are selected again according to the principle of uniform distribution, finally obtaining 26K SNP markers.

[0037] Step S4: Selection of supplementary sites in the large GAP region. The selection criteria were based on the resequencing data of 240 ducks, meeting the requirements of minimum sequencing depth ≥5x, deletion rate <10%, and minimum allele frequency >0.25. Approximately 20K sites were selected and evaluated, ultimately yielding 579 SNP markers.

[0038] In step S5, the selected sites from steps S2, S3 and S4 are merged, and SNP markers that are close to each other are filtered out, resulting in a total of 40,876 SNP sites, and finally a duck 40K whole genome liquid phase chip is generated.

[0039] The locations of the 40,876 SNP molecular markers on the duck reference genome ZJU1.0 are shown in Table 1 below. The location information of the SNP sites is represented in the form of chromosome number: physical location.

[0040] Table 1 lists the 40,876 SNP molecular marker locations.

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[0078] Example 2: Application of Duck 40K Whole Genome Liquid Chromatography Chip in Duck DNA Sample Detection

[0079] 1. Extraction of duck genomic DNA samples: Blood was collected from the wing vein of the duck, and DNA was extracted using the phenol-chloroform method.

[0080] 2. DNA Sample Quality Testing: DNA concentration was determined using an Agilent 2100 Bioanalyzer (Agilent), and DNA integrity was assessed using 1% agarose gel electrophoresis. Samples that passed the tests were stored at 4°C for later use.

[0081] 3. Liquid crystal chip testing: Operate according to the standard procedure for liquid crystal chip testing.

[0082] (http: / / www.molbreeding.com / index.php / Technology / GenoBaits.html).

[0083] 4. Data Analysis: The raw data were quality controlled using FASTP software (Chen et al., 2018). Then, the sequencing data were aligned to the duck reference genome ZJU1.0 using BWA software. SNPs were detected and genotyping was performed using the standard procedure of GATK4 software.

[0084] Example 3: Reliability analysis of duck 40K whole genome liquid phase chip detection.

[0085] Three ducks were randomly selected from four different breeds of ducks, resulting in a total of 12 samples. Blood DNA was extracted from these samples, and liquid-phase chip probes were synthesized according to the designed chip information. The 12 samples were then analyzed using the duck 40K whole genome liquid-phase chip described in this invention. The results are shown in Table 2.

[0086] Table 2. Detection rate of duck 40K whole genome liquid phase chip

[0087]

[0088] As shown in Table 3, the sample locus capture efficiency ranged from 98.93% to 99.16%, with an average detection rate of 99.06%. Using the duck 40K whole-genome liquid-phase chip described in this invention, the average SNP marker detection rate was above 99%, and the total number of effective markers exceeded 40,000, which is sufficient to meet the needs of subsequent analysis.

[0089] Example 4: Evaluation of the effect of duck 40K whole-genome liquid microarray on breed genetic diversity

[0090] (1) Using the genome information of 238 ducks from the resequencing, liquid phase chip probes were synthesized according to the designed chip information, and the liquid phase chip was used to detect the 238 samples.

[0091] (2) SNP data quality control. SNPs with genotyping errors, minimum allele frequency < 5%, and alignment rate < 95% were filtered out.

[0092] (3) Principal Component Analysis (PCA). Principal component analysis (PCA) was performed on the obtained genome-wide SNP and liquid-phase microarray SNP datasets using Plink (version 1.9) to analyze population structure and compare the results. The results are attached. Figure 2 As shown, the duck 40K whole genome liquid phase chip of the present invention can effectively classify different breeds of ducks. The results are basically consistent with the resequencing results, proving that the duck 40K whole genome liquid phase chip of the present invention can be used for duck breed identification.

[0093] Example 5: Evaluation of the application effect of duck 40K whole-genome liquid array in genome association analysis

[0094] (1) Data was obtained from the genotyping data of 400 H3 strain ducks using the duck 40K whole genome liquid phase chip described in this invention. Phenotypic information included body weight and shank length at 35 days of age.

[0095] (2) The statistical model used in the GWAS analysis is:

[0096]

[0097] In the model, Represents a vector of phenotypic values; This represents the fixed-effects design matrix where the first column is 1; Label genotype vectors, These are the correlation coefficient vector including the intercept and the label effect vector, respectively; These are the random effects vector and the residual vector, respectively. These represent the ratio of the two variance components and the residual variance, respectively; K is the center kinship matrix estimated by SNPs after quality control. It is an identity matrix. Representing an n-dimensional multivariate normal distribution. The Wald test was used to screen for SNPs associated with body weight and tibia length traits.

[0098] Considering the numerous linkage disequilibrium regions among genomic genetic markers, using Bonferroni correction to set thresholds is overly conservative. To avoid this issue, the `indep-pairwise 25 5 0.2` command in Plink software was used to calculate the number of independent SNPs on all chromosomes. Specifically, this means using 25 SNPs as a window, 5 SNPs as the step size per step, and 0.2 as the r² threshold. We adjusted the genome-wide significance threshold (0.00000103) and the genome-wide potential significance threshold (0.0000206) based on the number of independent SNPs. The analysis results are shown in Table 3.

[0099] Table 3. Results of GWAS analysis of the duck 40K whole genome using liquid microarray.

[0100]

[0101] Table 3 shows that, using the duck 40K whole-genome liquid-phase microarray information described in this invention, GWAS analysis was performed, screening for 10 SNPs related to body weight and identifying a genomic region located on chromosome 14 with an average MAF of 0.2992; 10 SNP markers related to shank length were also obtained, with an average MAF of 0.2473. These results indicate that the duck 40K whole-genome liquid-phase microarray described in this invention can be practically applied to duck genome association analysis, and its 40K density is sufficient to meet the requirements of genome association analysis.

[0102] Example 6: Evaluation of the application effect of duck 40K whole-genome liquid array in genome selection

[0103] In this embodiment, the duck 40K whole-genome liquid phase chip described above was used to genotype 400 H3 strain ducks, obtaining genotyping data, including pedigree and phenotypic information (age at first laying, number of eggs laid, egg weight, body weight at 35 days of age, shank length at 35 days of age, and pectoral muscle thickness at 40 days of age). Two models, GBLUP and ssGBLUP, were used to genetically evaluate traits such as age at first laying, number of eggs laid, egg weight, body weight at 35 days of age, shank length at 35 days of age, and pectoral muscle thickness at 40 days of age, and the results were compared with the traditional PBLUP model.

[0104] The GBLUP model belongs to the LMM model category, in which It is a vector of random additive genetic effects, denoted as , It is to estimate the genetic variance. It is a random residual vector that follows , This estimates the residual variance. The G matrix is ​​a kinship matrix constructed based on genomic information using the VanRaden algorithm. The equation for the VanRaden algorithm is as follows:

[0105]

[0106] In the formula, M is the normalized genotype matrix, and m is the number of markers. It is the first The minimum allele frequency at each locus is used to estimate the variance components by combining the relationship matrix, phenotype, and fixed effects.

[0107] The H matrix is ​​used to estimate the genomic breeding value, and the calculation equation for ssGBLUP is the same as that of the LMM model.

[0108] The equations for the mixed model are shown below:

[0109]

[0110] The H matrix, constructed using genomic information and pedigree records, is built using the following method:

[0111]

[0112] Where A, A22, G, and H represent kinship matrices constructed based on pedigree, kinship matrices constructed based on pedigree information of individuals with genotypes, kinship matrices constructed based on genomic information, and kinship matrices combining pedigree and genomic information, respectively. According to VanRaden's report on constructing the H matrix, relative weights for G and A22 are set in the H matrix. ,Will =0.05 is used as the default parameter for constructing the H matrix. After weighting the G matrix, it becomes: =0.95G+0.05A.

[0113] The G matrix and the A22 matrix are corrected to the same level using the following method:

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[0115] In the equation, G* is the adjusted G matrix, where G represents the kinship matrix constructed based on genomic information. The formulas for calculating a and b are as follows:

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[0118] The equation for the merged H matrix is:

[0119]

[0120] The PBLUP model belongs to the traditional linear mixed model (LMM), and its statistical model equation is as follows:

[0121]

[0122] in It is a vector of phenotypic values; and It is the correlation matrix of fixed effects and additive genetic effects. In this study, generations and batches were added to the model as fixed effects. It is a fixed effects vector. It is a vector of random additive genetic effects, denoted as , It is to estimate the genetic variance. It is a random residual vector that follows , It estimates the residual variance. The matrix is ​​a kinship matrix constructed based on genealogy.

[0123] The accuracy of predictions is estimated using 5-fold cross-validation. The main idea is to divide the data into a Training (reference) Population and a Valiation (test) Population, also known as the training population and the test population. In genome selection, the performance of different methods is compared primarily through cross-validation.

[0124] in,

[0125] The formula for calculating correlation is:

[0126] The accuracy calculation formula is:

[0127] The results are shown in Table 4 below.

[0128] Table 4. Accuracy of different genetic assessment models

[0129]

[0130] As shown in Table 4 above, the GBLUP and ssGBLUP models exhibit significantly higher accuracy than the traditional PBLUP model in selecting multiple traits, including age at first laying, number of eggs laid, egg weight, body weight at 35 days, shank length at 35 days, and pectoral muscle thickness at 40 days. Specifically, the GBLUP model's prediction accuracy for age at first laying, number of eggs laid, egg weight, body weight, shank length, and pectoral muscle thickness is 14.3%, 19.1%, 31.8%, 92.3%, 140%, and 9.5% higher than that of PBLUP, respectively. Therefore, the duck 40K whole-genome liquid-phase chip described in this invention not only demonstrates high accuracy in genome selection but also exhibits strong stability.

[0131] The results above show that the duck whole genome chip and kit described in this invention can be applied to duck whole genome association analysis or trait-related gene mapping, genome selection, duck germplasm resource improvement, duck genetic diversity analysis, duck breed identification, breed tracing or kinship identification, and has the advantages of high accuracy and strong stability.

[0132] The above embodiments are preferred embodiments of the present invention, but the embodiments of the present invention are not limited to the above embodiments. Any changes, modifications, substitutions, combinations, or simplifications made without departing from the spirit and principle of the present invention shall be considered equivalent substitutions and shall be included within the protection scope of the present invention.

Claims

1. A duck whole genome chip, characterized in that, The chip is loaded with a combination of molecular probes to detect the following combinations of SNP molecular markers; The SNP molecular marker combination consists of 40,876 SNP molecular markers, and the positions of the SNP molecular markers on the duck reference genome ZJU1.0 are shown in Table 1 of the specification.

2. The duck whole genome chip as described in claim 1, characterized in that, The chip is a liquid phase chip.

3. A kit for duck genotyping, characterized in that, The kit includes the duck whole genome chip as described in claim 1 or 2.

4. The use of the duck whole genome chip as described in claim 1 or 2 or the kit as described in claim 3, wherein the use is any of the following: (1) Application in duck trait improvement or prediction; (2) Application in genome-wide association analysis or trait-related gene mapping in ducks; (3) Application in duck genome selection breeding; (4) Application in duck germplasm resource improvement; (5) Applications in duck genetic diversity analysis, duck breed identification, breed tracing or kinship identification; (6) Application in filling high-density chips.

5. A method for predicting duck traits, characterized in that, Genotyping of the duck whole genome chip as described in claim 1 or 2 was performed on the sample to be tested, and prediction was made based on the genotyping data using a model. The traits include body weight, egg weight, number of eggs laid, age at first laying, tibia length, and pectoral muscle thickness.

6. The duck trait prediction method as described in claim 5, characterized in that, The models include GBLUP and ssGBLUP.

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