Method for whole genome association analysis of pig reproductive traits based on resequencing data

Abstract

The application discloses a kind of pig reproductive traits whole genome association analysis methods based on resequencing data, comprising: selecting test pig population, collecting the tissue sample of test pig population, and constructing DNA library based on tissue sample;Gene data in DNA library is whole set genome resequencing, and several SNP marker sites meeting preset quality requirements are screened;Several SNP marker sites are analyzed based on whole genome association analysis, and single nucleotide polymorphism SNPs and candidate genes significantly associated with reproductive traits are screened;Single nucleotide polymorphism SNPs and candidate genes are subjected to enrichment analysis, and obtain reproductive traits related candidate genes.The application utilizes whole gene resequencing, and then combines pig reproductive trait parameters to carry out whole genome association analysis, to lay a foundation for improving pig breeding, reproductive efficiency and economic benefit.

Description

Technical Field

[0001] This invention belongs to the field of animal reproductive technology, and in particular relates to a genome-wide association analysis method for pig reproductive traits based on resequencing data. Background Technology

[0002] Reproductive traits are crucial for sow productivity, directly impacting the economic benefits of pig farms. Total litter size, live litter size, and healthy litter size are key indicators of sow reproductive performance. The heritability of litter size is around 0.1, classifying it as a low-heritability trait, while the genetic correlation between total litter size and healthy litter size is high. Numerous studies have shown that improving sow reproductive performance using traditional breeding methods is a slow process.

[0003] Genome-wide association analysis (GWAS) has been widely used in the study of reproductive indicators in pigs, such as total litter size, number of live piglets, birth litter weight, number of healthy piglets, number of weak piglets, and number of teats. This invention uses GWAS to identify SNPs and candidate genes related to reproductive traits, laying the foundation for improving pig breeding, reproductive efficiency, and economic benefits. Summary of the Invention

[0004] The purpose of this invention is to provide a genome-wide association analysis method for pig reproductive traits based on resequencing data, so as to solve the problems existing in the prior art.

[0005] To achieve the above objectives, this invention provides a method for genome-wide association analysis of porcine reproductive traits based on resequencing data, comprising:

[0006] Select experimental pig herds, collect tissue samples from the experimental pig herds, and construct DNA libraries based on the tissue samples;

[0007] The entire genome was resequencing the gene data in the DNA library to screen for several SNP marker sites that met the preset quality requirements;

[0008] Based on genome-wide association analysis, several SNP marker sites were analyzed to screen single nucleotide polymorphisms (SNPs) and candidate genes that are significantly associated with reproductive traits.

[0009] Enrichment analysis was performed on the single nucleotide polymorphisms (SNPs) and candidate genes to obtain candidate genes related to reproductive traits.

[0010] Optionally, farrowing records from three consecutive parities are collected, and 21 sows in the high-breeding group and 19 sows in the low-breeding group are selected as the experimental pig herd based on the farrowing records.

[0011] Optionally, ear tissue samples were collected from each pig in the sample herd for whole-genome resequencing.

[0012] Optionally, the reproductive traits include total number of offspring, number of live offspring, and number of healthy offspring.

[0013] Optionally, the process of constructing a DNA library based on the tissue sample includes: transporting the tissue sample via cryotubes and dry ice, and using the tissue sample for DNA library construction after passing quality inspection.

[0014] Optionally, the process of performing full genome resequencing on the gene data in the DNA library and screening for several SNP marker sites that meet preset quality requirements includes:

[0015] Whole-genome sequencing was performed on the tissue sample using an average sequencing depth of 10× to obtain raw sequencing data, and the raw sequencing data was then subjected to quality control.

[0016] A sow reference genome was constructed, and the raw sequencing data selected through quality control was compared with the sow reference genome. Based on the comparison results, several SNP marker sites that meet the preset quality requirements were selected.

[0017] Optionally, the process of screening for single nucleotide polymorphisms (SNPs) and candidate genes that are significantly associated with reproductive traits includes:

[0018] Several SNP marker loci were filtered according to preset conditions. Gemma software was used to perform association analysis on the phenotypic traits of total litter size, number of live piglets, and number of healthy piglets in sows with several SNP marker loci based on genome-wide association analysis model, so as to obtain the association relationship between traits and SNP marker loci.

[0019] Optionally, GO and KEGG functional enrichment analyses can be performed on several SNP marker sites and their upstream and downstream positions in the genome. By combining the association between traits and SNP marker sites, candidate genes related to reproductive traits can be obtained.

[0020] The technical effects of this invention are as follows:

[0021] This invention records reproductive trait parameters of sows, such as total number of piglets born in three consecutive litters, number of live piglets born, and number of healthy piglets born. Whole-genome resequencing was performed using Illumina Novaseq 6000, followed by GWAS to identify SNPs and candidate genes related to reproductive traits. In other words, whole-genome resequencing, combined with pig reproductive trait parameters, is used to perform genome-wide association analysis, laying the foundation for improving pig breeding, reproductive efficiency, and economic benefits. Attached Figure Description

[0022] The accompanying drawings, which form part of this application, are used to provide a further understanding of this application. The illustrative embodiments and descriptions of this application are used to explain this application and do not constitute an undue limitation of this application. In the drawings:

[0023] Figure 1 The following is the mean distribution of the total number of offspring, the number of live offspring, and the number of healthy offspring for the three reproductive traits in this embodiment of the invention: (a) is the total number of offspring, (b) is the number of live offspring, and (c) is the number of healthy offspring.

[0024] Figure 2 The Manhattan plots for genome-wide association analysis of sow reproductive traits in this embodiment of the invention are as follows: (a) is the Manhattan plot for total number of piglets born, (b) is the Manhattan plot for the number of live piglets born, and (c) is the Manhattan plot for the number of healthy piglets born.

[0025] Figure 3 The following are quantile-quantile plots of sow reproductive traits in embodiments of the present invention: (a) is the quantile-quantile plot of total litter size, (b) is the quantile-quantile plot of live litter size, and (c) is the quantile-quantile plot of healthy litter size.

[0026] Figure 4 This is a flowchart of the method in an embodiment of the present invention. Detailed Implementation

[0027] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. This application will now be described in detail with reference to the accompanying drawings and embodiments.

[0028] It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases the steps shown or described may be executed in a different order than that shown here.

[0029] Example 1

[0030] like Figure 1-4 As shown in this embodiment, a genome-wide association analysis method for pig reproductive traits based on resequencing data is provided, including:

[0031] 1. Materials and Methods

[0032] 1.1 Sample collection and library construction

[0033] The experimental pig herd came from Guangxi Nongken Yongxin Livestock Group Xijiang Animal Husbandry Co., Ltd., and sow reproductive records were collected from March 2020 to August 2022, including total number of piglets born, number of live piglets born, and number of healthy piglets born. All phenotypic records were records of sows' three consecutive litters.

[0034] Ear tissue samples were collected from 21 sows in the high-breeding group (H) (average litter size of 15 or more live piglets) and 19 sows in the low-breeding group (L) (average litter size of 10 or less live piglets). The samples were placed in cryovials and sent to Guangzhou Kidiao Biotechnology Co., Ltd. for DNA extraction. After passing quality inspection, a DNA library was constructed. Whole genome resequencing was performed using the Illumina Novaseq 6000PE150 platform. The average sequencing depth was 10×, the pig genome size was 2.5G, and the total data volume was 25G.

[0035] 1.2 Full genome resequencing

[0036] The raw sequencing data underwent quality control, specifically by using FASTP (v.0.20.0) software to filter out low-quality reads and obtaining high-quality Clean Reads, which were then subjected to quality control. Subsequently, BWA (v0.7.17) software was used to align the data to the publicly available pig reference genome (Sscrofa11.1 GCF_000003025.6 version). Finally, GATK4 software was used to analyze the data to obtain high-quality SNP loci for GWAS analysis. The filtering parameters are -Window 4, -filter"QD<4.0||FS>60.01|MQ<40.0", -G_filter GQ<20, where QD is Variant Confidence / Qualityby Depth; FS is Phred-scaled p-value using Fisher's exact test to detect strand bias; MQ is RMS Mapping Quality, which is the root mean square of the alignment quality values ​​of all reads aligned to this locus; and GQ is Genotype Quality.

[0037] 1.3 Genome-wide association analysis

[0038] The obtained SNP data were filtered using a Perl script in conjunction with Plink v2.0 software according to the following conditions: non-secondary alleles, minor allele (MAF) frequencies less than 0.05, deletion rates greater than 0.7, and heterozygosity rates greater than 0.8.

[0039] R software (v3.5.0) was used to perform statistical analysis on phenotypic data, including mean, standard deviation, standard error, kurtosis, and other relevant descriptive information. Gemma software (v0.98.1) was used to calculate the phenotypic traits of total litter size, live litter size, and healthy litter size in sows using the commonly used GWAS model (GLM). The unified expression for the genome-wide association analysis model is: y = Xα + Q β +Kμ+e.

[0040] Where y is the phenotypic vector, X is the genotype matrix, α is the genotype effect vector, Q is the fixed effects matrix (which can contain information such as population structure, sex, location, and number of events), β is the fixed effects vector, K is the random effects matrix (mainly referring to the kinship matrix), μ is the random effects vector, and e is the residual vector. For each SNP locus, α is tested to see if it is 0. The probability p of α being 0 is used to measure the association between the marker genotype and the phenotype. The smaller the p value, the lower the probability of α being 0, and the more likely the marker is to be associated with the trait.

[0041] 1.4 GO and KEGG enrichment analysis

[0042] Significant SNP sites were screened based on a preset threshold of probability value p. For significant SNP sites and their corresponding genomic locations within 50kb upstream and downstream, GO and KEGG functional enrichment analyses were performed on annotated candidate genes. In addition, candidate genes related to reproductive traits were mined by combining literature.

[0043] 2 Results

[0044] 2.1 Results of Phenotypic Data Analysis

[0045] Table 1 details the reproductive traits of sows, including total litter size, live litter size, and healthy litter size across three consecutive litters. As shown in Table 1, the average values ​​for total litter size, live litter size, and healthy litter size were 12.65, 11.65, and 10.93, respectively; the maximum values ​​were 20, 18, and 17, respectively; the minimum values ​​were 6, 6, and 4, respectively; and the coefficients of variation were 29.56%, 29.27%, and 30.92%, respectively. These results indicate that these reproductive traits in sows have significant genetic selection potential.

[0046] Table 1

[0047]

[0048] For details on the mean distribution frequency of reproductive traits, please refer to [link / reference]. Figure 1 ,Depend on Figure 1 It can be seen that the mean distribution of the three traits—total number of offspring, number of live offspring, and number of healthy offspring—basically follows a normal distribution, indicating that the sample is representative and can be used as a sample for subsequent experiments.

[0049] 2.2 Results of whole-genome SNP quality control

[0050] The 19,294,917 SNPs obtained from the initial analysis were subjected to quality control. First, non-secondary alleles were removed. Using MAF > 0.05 as the criterion, 4,448,546 loci were removed, 35,466 loci with a SNP deletion rate > 0.7 were removed, and 102,233 loci with a heterozygosity rate > 0.8 were removed. The remaining 14,708,672 SNPs were used for subsequent association analysis.

[0051] 2.3 Results of Genome-wide Association Analysis

[0052] The results of GWAS analysis on three reproductive traits of sows—total number of piglets born, number of live piglets born, and number of healthy piglets born—are as follows: Figure 2 As shown in Figure 3. From Figure 2 The Manhattan study showed that SNPs at genome-wide significance or potential significance were detected in all three reproductive traits: total litter size, live litter size, and healthy litter size. Two SNPs in the total litter size reached the Bonferroni correction threshold, and one SNP was above the correction threshold. Similarly, one SNP in the live litter size reached the correction threshold, and one SNP in the healthy litter size reached the Bonferroni correction threshold, and one SNP was above the correction threshold. Figure 3 From the quantile-quantile plots: Two observations of total litter size exceeded the expected value (significant), and two observations were comparable to the expected value (potentially significant). One observation of live litter size was comparable to the expected value (potentially significant). One observation of healthy litter size was also comparable to the expected value (potentially significant). Overall, the results from the QQ plot and the Manhattan plot are largely consistent.

[0053] Candidate genes related to reproductive traits were mined from the above-mentioned significant SNP sites and their genomic locations within 50kb upstream and downstream. The results are shown in Table 2. Table 2 shows that the RORA gene is present near the site on chromosome LUXT01044478.1 associated with total litter size; the SFRP1 gene is present near the site on chromosome LUXT01078608.1 associated with total litter size; the regions on chromosome LUXT01094249.1 associated with reproductive traits include genes such as HSD17B1 and STAT3; the IGF1 gene is present near the site on chromosome LUXT01046818.1 associated with total litter size; and the DAZL gene, including genes such as HSD17B1 and STAT3, is present in the regions on chromosome LUXT01020655.1 associated with the number of live and healthy offspring. Based on the biological functions of the screened genes, IGF1, DAZL, HSD17B1, SFRP1, STAT3, and SF3B6 were selected as candidate genes related to reproductive traits (see Table 2 for details). Other genes that were not highly related to reproductive traits were not included in the candidate gene list.

[0054] Table 2

[0055]

[0056] Table 3

[0057]

[0058] 2.4 Results of GO and KEGG enrichment analysis

[0059] In this embodiment, the reference genome database of domestic pigs was downloaded from the Ensembl website, and the ClusterProfiler package was used to perform GO and KEGG gene functional enrichment analyses on the annotated candidate genes for reproductive traits. The GO enrichment analysis results showed that in the trait of live piglet count, GO:0007147 (female meiosis II pathway) and DAZL reached significant levels. The KEGG enrichment analysis results showed that in the trait of healthy piglet count, HSD17B1 in the signaling pathway of estrogen secretion reached significant levels.

[0060] The above description is merely a preferred embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for genome-wide association analysis of porcine reproductive traits based on resequencing data, characterized in that, Includes the following steps: Select experimental pig herds, collect tissue samples from the experimental pig herds, and construct DNA libraries based on the tissue samples; The entire genome was resequencing the gene data in the DNA library to screen for several SNP marker sites that meet the preset quality requirements; Based on genome-wide association analysis, several SNP marker sites were analyzed to screen single nucleotide polymorphisms (SNPs) and candidate genes that are significantly associated with reproductive traits. Enrichment analysis was performed on the single nucleotide polymorphisms (SNPs) and candidate genes to obtain candidate genes related to reproductive traits.

2. The method for genome-wide association analysis of porcine reproductive traits based on resequencing data according to claim 1, characterized in that, Farrowing records from three consecutive parities were collected. Based on these records, 21 sows in the high-breeding group and 19 sows in the low-breeding group were selected as the experimental pig herd.

3. The method for genome-wide association analysis of porcine reproductive traits based on resequencing data according to claim 1, characterized in that, Ear tissue samples were collected from each pig in the sample herd for whole-genome resequencing.

4. The method for genome-wide association analysis of porcine reproductive traits based on resequencing data according to claim 1, characterized in that, The reproductive traits include total number of offspring, number of live offspring, and number of healthy offspring.

5. The method for genome-wide association analysis of porcine reproductive traits based on resequencing data according to claim 1, characterized in that, The process of constructing a DNA library based on the tissue sample includes: transporting the tissue sample via cryotubes and dry ice, and using the tissue sample for DNA library construction after it has passed quality inspection.

6. The method for genome-wide association analysis of porcine reproductive traits based on resequencing data according to claim 1, characterized in that, The process of performing full genome resequencing on gene data from a DNA library and screening for several SNP marker sites that meet preset quality requirements includes: Whole-genome sequencing was performed on the tissue sample using an average sequencing depth of 10× to obtain raw sequencing data, and the raw sequencing data was then subjected to quality control. A sow reference genome was constructed, and the raw sequencing data selected through quality control was compared with the sow reference genome. Based on the comparison results, several SNP marker sites that meet the preset quality requirements were selected.

7. The method for genome-wide association analysis of porcine reproductive traits based on resequencing data according to claim 1, characterized in that, The process of screening single nucleotide polymorphisms (SNPs) and candidate genes that are significantly associated with reproductive traits includes: Several SNP marker loci were filtered according to preset conditions. Gemma software was used to perform association analysis on the phenotypic traits of total litter size, number of live piglets, and number of healthy piglets in sows with several SNP marker loci based on genome-wide association analysis model, so as to obtain the association relationship between traits and SNP marker loci.

8. The method for genome-wide association analysis of porcine reproductive traits based on resequencing data according to claim 1, characterized in that, GO and KEGG functional enrichment analyses were performed on several SNP marker sites and their upstream and downstream positions in the genome. Based on the association between traits and SNP marker sites, candidate genes related to reproductive traits were obtained.

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