SNP molecular marker related to goose meat quality traits

Abstract

Disclosed in the present invention is a single nucleotide polymorphism (SNP) molecular marker related to goose meat quality traits. In order to screen and identify SNP related to goose meat quality traits, genome-wide association study (GWAS) is performed on 70-day-old male anser cygnoides domesticus to screen SNP in a goose genome. A matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDITOF MS) method is used to test genotypes of selected SNPs associated with goose meat. The results show that 43 SNPs are significantly correlated with these features. 28 annotation genes are tested as potential candidate genes of five meat quality traits. Gene enrichment analysis within the range of 1 Mb near SNPs shows significant enrichment on a protein digestion and absorption pathway and a glycolipid metabolic pathway.

Description

A SNP molecular marker associated with goose meat quality traits Technical Field

[0001] The present invention relates to the field of gene sequencing, and in particular to a SNP molecular marker associated with goose meat quality traits. Background Art

[0002] Goose meat is a nutritious meat considered a healthy food due to its high protein, low fat, and low cholesterol levels. It is considered a desirable meat product and an important source of meat in countries around the world. According to the World Health Organization, in 2021, China's goose meat production reached a staggering 570 million heads, accounting for 99% of Asia's total production and 94% of global production. Furthermore, China's goose meat production reached 1.77 million tons. The Sichuan White Goose (Anser cygnoides domesticus), renowned for its exceptional meat quality, is a unique local breed in China. Its characteristic soft and delicate texture contributes to its high reputation and significant market value. In recent years, market demand for goose meat has steadily increased. This surge can be attributed to the growing focus on healthy lifestyles, highlighting significant opportunities for the goose meat industry. Goose meat quality is a multifaceted attribute shaped by numerous genes, both primary and secondary. The complex and multifaceted regulatory mechanisms governing this trait add to its complexity. Previous studies have extensively documented the influence of factors such as breed, age, nutrition, environment, and disease on goose meat quality traits. Therefore, identifying and screening molecular markers and key genes associated with these traits has great potential to improve breeding efficiency and enhance the overall quality of goose meat. Meat quality evaluation relies primarily on multiple meat indicators, including meat color brightness (L*), redness (a*), yellowness (b*), pH, shear force (SF), cooking loss rate (CLR), and coarse fat content (CFC). A methionine-rich diet has been shown to positively impact breast muscle yield, increase pH, enhance meat color, and reduce muscle shear. The presence of fatty acids in the diet directly influences meat flavor and intramuscular fat production. Studies have shown that higher levels of soy lecithin in the diet increase the fat content of breast and leg muscles, thereby improving binding force, reducing drip loss, and minimizing cooking loss within the muscle. Furthermore, an increase in crude fat content (CFC) promotes intramuscular fat deposition and weakens the bonds between muscle fibers. As a result, shear force is reduced and meat tenderness is improved.

[0003] Genome-wide association studies (GWAS), which investigate the relationship between population phenotypes and genotypes, have yielded important discoveries in identifying genetic variants associated with meat quality traits in livestock and poultry. Advances in whole-genome resequencing technologies have enabled the accurate screening of various types of genetic variation, including single nucleotide polymorphisms (SNPs), insertion / deletion sites (InDels), structural variants (SVs), copy number variations (CNVs), and presence / absence variants (PAVs). To date, no single nucleotide polymorphisms (SNPs) associated with meat quality traits have been successfully detected in the Sichuan goose genome using whole-genome resequencing.

[0004] Summary of the Invention

[0005] To solve the above technical problems, the present invention provides a SNP molecular marker related to goose meat quality traits, conducts GWAS analysis on goose meat quality traits, and identifies 43 SNPs that are significantly associated with these traits, which helps to fully understand the genetic mechanism of goose meat quality traits.

[0006] The technical solution adopted by the present invention to solve the technical problem is: a SNP molecular marker related to goose meat quality traits, the SNP molecular marker is as follows:

[0007] Preferably, the 11 SNPs genotypes include the following:

[0008] Related to b*meat yellowness:

[0009] chr13:25432644,

[0010] ctg930:11921,

[0011] chr36:5611038,

[0012] ctg2092:176333,

[0013] chr29:8278595,

[0014] chr9:33911228,

[0015] chr1:24867798,

[0016] chr7:16992800,

[0017] Related to SF meat:

[0018] chr12:31782870,

[0019] chr12:31781825,

[0020] chr2:36812553.

[0021] Preferably, the MALDI-TOF MS primer sequence is:

[0022] Preferably, the frequencies and genotypes of the selected goose meat quality-related SNPs are:

[0023] Beneficial effects of the present invention:

[0024] This study conducted a GWAS analysis of goose meat quality traits and identified 43 SNPs significantly associated with these traits. This will help to fully understand the genetic mechanisms of goose meat quality traits and lay a solid foundation for future research in this field. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only three of the drawings of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0026] FIG1 is a schematic diagram of SNP density within a 1 Mb window size of a goose genome according to an embodiment of the present invention;

[0027] Figure 2 shows the Manhattan plot and QQ plot of different meat quality traits of male Sichuan white geese according to the embodiment of the present invention. Note: The following traits are shown in the figure: CFC (A, B); CLR (C, D); L* (brightness of meat) (E, F); b* (yellowness of meat) (G, H); SF (I, J). The significance threshold is set to 1×10 -7 ;

[0028] FIG3 is a schematic diagram of functional analysis of genes within 1 Mb within SNPs associated with goose meat quality traits according to an embodiment of the present invention; DETAILED DESCRIPTION

[0029] In order to deepen the understanding of the present invention, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. The embodiments are only used to explain the present invention and do not limit the scope of protection of the present invention.

[0030] Example

[0031] 1 Materials and Methods

[0032] 1.1 Experimental animals and phenotypic traits

[0033] Male Sichuan White geese were selected from a shared hatchery batch at the Anfu Waterfowl Breeding Base in Chongqing, China (latitude: 105.478°N, longitude: 29.343°E). Geese received a standardized diet, temperature, lighting, and unrestricted access to water during their rearing period. When the geese reached 70 days of age, 215 healthy individuals were randomly selected as experimental subjects. Blood samples were collected from the wings of the geese using vacuum tubes containing the anticoagulant EDTA and subsequently stored at −20°C for future experimental use. Meat color parameters (L*, a*, and b*) were measured at three fixed points on right breast muscle samples from 215 geese 2 hours after slaughter using an automated colorimeter and a portable pH meter. Muscle samples were standardized for thickness and weight (M0) and then immersed in a constant-temperature water bath at 80°C (model SW 22, Julabo GmbH, Seelbach, Germany). Once the core temperature of the samples reached 70°C, they were incubated for 30 minutes. Subsequently, the sample was removed from the heat source and cooled to room temperature.The sample (M1) was then weighed and the cooking loss ratio (CLR) was calculated as CLR = (M0-M1) / M0*100%.

[0034] The cooled pectoral muscles were fixed perpendicular to the muscle fibers into strips with a cross-sectional area of ​​1 cm × 1 cm and sheared using a tenderness meter (CLM-3) 5 times to obtain the average shear force (SF) of these samples. 100 g of fresh meat samples were freeze-dried into powder and the powder was weighed (Mp). The moisture content of the freeze-dried powder (MCFD) was calculated as MCFD = (100-Mp) / 100*100%. Fat was extracted from 1 g of freeze-dried powder using petroleum benzene, and the remaining material (M R Dry at 105°C for 2 hours. The crude fat content (CFC) is then calculated as CFC = (1-M R *Mp / 100*100%.

[0035] 1.2 Whole-genome resequencing and SNP calling

[0036] We extracted genomic DNA from goose whole blood samples using the Tiangen Biotechnology DP332 genomic DNA extraction kit. We subsequently assessed the quality of the extracted DNA by measuring the OD260 / 280 ratio (ranging from 1.8 to 1.89). For Illumina sequencing, the original DNA samples were diluted to a concentration of 50 ng / μL. We used 100 μL from each sample. Whole-genome sequencing (WGS) was performed using the Illumina HiSeq X Ten platform. After generating the WGS data, we performed quality control procedures using FastQC and NGS QC Toolkit software, which involved removing adapter sequences, residual primers, and trimming low-quality bases. Filtered reads were aligned to the goose genome (version ASM1303099v1) using BWA software, followed by SNP calling using GATK software. The phenotypic datasets in this study consisted only of individual-level samples with genotype data completeness exceeding 95%. SNP filtering was performed using Plink at the SNP level using the following parameters: --genome 0.1 –hwe 0.0000001 –maf 0.05 –mind 0.1. Furthermore, individuals with fewer than 10% missing genotypes were retained. A total of 203 individuals were retained for further analysis. Principal component analysis (PCA) was then performed using a model implemented in Plink to assess goose population stratification.

[0037] GWAS analysis

[0038] We used GEMMA software to conduct genome-wide association studies (GWAS) to identify single nucleotide polymorphisms (SNPs) associated with various goose meat quality traits, including CF, MCFD, CLR, SF, and meat color (L*, a*, b*). GWAS analyses employed mixed linear models, represented by the equation y = Wα + xβ + ε, where y represents the phenotypic values ​​for all individuals. W is the covariance matrix, controlling for population structure, and α is the coefficient vector, including the intercept. The variable x corresponds to the genotype of the SNP or haplotype marker, and β represents the effect size of the SNP or haplotype marker on the phenotype. Random residuals are represented by the vector ε. We used the Wald test statistic to assess the significance of the association between the SNP and the phenotype, and applied the Bonferroni correction to adjust the association analysis results. Manhattan plots, depicting the associations, were generated using the GAP package from https: / / cran.r-project.org / web / packages / gap / . In addition, QQ plots illustrating the distribution of p-values ​​were created using the qqman package in the R project software https: / / cran.r-project.org / web / packages / qqman / . To assess whether there were false positive signals in the obtained results, we used the GenABEL package and calculated the genomic inflation factor (k).

[0039] To identify SNP genotypes significantly associated with meat quality traits in Sichuan white geese through GWAS analysis, we designed amplification and extension primers to amplify the target sequence, promote hybridization of the fragment, and extend the specific nucleotide of interest. To assess differences in meat quality traits between genotypes for the selected SNPs, we used a general linear model in JMP software (version 13.0). We then calculated the least squares mean for each genotype and performed a Bonferroni test to assess differences between genotypes.

[0040] 1.4 SNP annotation

[0041] In this study, we used BEDTools software to extract genetic information from the 1-Mb region upstream and downstream of each potential SNP in the goose genome. Annovar software (SnpEff, Annovar, VEP, Oncotator) was used for SNP annotation. Functional annotation analysis of candidate genes was performed using the Metascape website (https: / / metascape.org / ).

[0042] 2 Results

[0043] 2.1 Phenotypic description of goose population

[0044] Meat goose farming generally utilizes rapid and efficient fattening methods. Geese are generally marketable after 70 days of age, and the meat quality of 70-day-old geese holds significant economic value in production practices. The table below shows the muscle quality characteristics of 70-day-old male Sichuan White geese breast meat, including crude fat content (CFC), meat color (L*, a*, and b*), shear stress (SF), freeze-dried moisture (MCFD), and cooking loss rate (CLR).

[0045] Table 1 Descriptive statistics of meat quality traits of 70-day-old male Sichuan white geese

[0046] 2.2 Whole-genome resequencing and SNP calling

[0047] We obtained 2,608.44 Gb of WGS data from 215 male Sichuan White geese, with an average depth of coverage of 10.89×. After screening, 2,601.19 Gb of high-quality sequencing data were obtained. After alignment with the goose reference genome sequence, the average mapping efficiency was 97.88% (97.72%-98.10%). The average Q20 and Q30 values ​​were 96.77% and 91.90%, respectively. The average GC content was calculated to be 43.63%. Subsequent analyses focused on the 10,072,006 SNPs identified in the goose genome (version ASM1303099v1) (Figure 1).

[0048] 2.3 Goose population structure and GWAS analysis

[0049] PCA analysis indicated no significant population stratification (Figure 2). Therefore, we performed a genome-wide association study (GWAS) of meat quality traits using a mixed linear model using the GEMMA package. We identified a total of 43 SNPs located in 28 genes (Table below) that showed significant associations with meat quality traits (Figure 3).

[0050] Table 2 Summary of 43 SNP markers associated with five meat quality traits of Sichuan white geese Note: The significance threshold is set to 1×10 -7ACKR3: atypical chemokine receptor 3; BMF: Bcl-2-modifying factor; CATC: dipeptidyl peptidase 1; CHIN: N-chimeric protein; DOCK1: donor of cytokinesis protein 1; EXTL3: exosome-like 3; GOGA7: Golgin subfamily A member 7; GSS: glutamine synthetase; GULP1: PTB domain-containing phagocytic adaptor protein 1; LIN1: LINE-1 reverse transcriptase homolog; MRPS2: 28S ribosomal protein S2, mitochondrial; MSI1H: RNA-binding protein Musashi homolog 1; NAD-ME: NAD-dependent malic enzyme, mitochondrial; NCOA2: nuclear receptor coactivator 2; NEMP2: nuclear integral membrane protein 2; NO40: nucleolar protein of 40 kDa; PP4R1: serine / threonine protein phosphatase 4 regulatory subunit 1; PRSS55: serine protease 55; PTP4A1: protein tyrosine phosphatase IVA Type 1; PUM1: Pumilio homolog 1; RRAGC: Ras-related GTP-binding protein C; RXRA: retinoic acid receptor RXR-α; SLCO5A1: solute carrier organic anion transporter family member 5A1; SMAD6: mother-opposing decapentaplegic homolog 6; SYTC2: possible threonine-tRNA ligase 2, cytoplasmic; TMEM19: transmembrane protein 19; TMEM65: transmembrane protein 65; None: no annotated gene was found in the specified region of the genome.

[0051] 2.4 Genotypes of selected SNPs

[0052] We detected 11 SNPs genotypes and compared the meat quality traits of different genotypes identified by GWAS (Tables 3-4), including 8 SNPs (chr13:25432644, ctg930:11921, chr36:5611038, ctg2092:176333, chr29:8278595, chr9:33911228, chr1:24867798 and chr7:16992800) that were significantly associated with b* meat yellowness, and 3 SNPs (chr12:31782870, chr12:31781825 and chr2:36812553) that were significantly associated with SF meat quality traits.

[0053] Table 3 MALDI-TOF MS primer sequences

[0054] Table 4 Frequency and genotype of selected goose meat quality-related SNPs Note: Different letters in the same column indicate significant differences among haplotypes (P<0.05).

[0055] 2.5 Gene annotation of significant SNPs within 1Mb

[0056] We used the KEGG database to annotate genes within 1 Mb of the genome that were significantly associated with goose meat quality traits. Gene enrichment analysis showed that genes were enriched in Cushing's syndrome (ko04934, p-value = 0.001797367), human papillomavirus infection (ko05165, p-value = 0.003221264), ECM-receptor interaction (ko04512, p-value = 0.003526523), protein digestion and absorption (ko04974, p-value = 0.00521013), and glycerolipid metabolism (ko00561, p-value = 0.008786627) (Figure 4).

[0057] 3 Discussions

[0058] The Sichuan White goose is a dual-purpose breed of Chinese poultry, renowned for its meat and egg production. It represents a valuable genetic resource that has made significant contributions to the poultry industry, bringing substantial economic and nutritional benefits to China. Meat quality in the poultry industry is a key factor influencing consumer acceptance and, ultimately, the economic success of the sector. Poultry breeders use selective breeding and genetic improvement programs to enhance meat quality traits, aiming to produce poultry that meets consumer preferences and demands. In this study, we investigated meat quality traits in 70-day-old male Sichuan White geese. Using GWAS, we screened and identified a total of 43 single-nucleotide polymorphisms (SNPs) associated with the CFC, CLR, meat color, and SF traits in 215 Chinese local geese (Sichuan White geese). GWAS analysis confirmed significant associations between these SNPs and the aforementioned traits.

[0059] Sample size is a key factor in study design that affects the validity and reliability of research results. A larger sample size can improve results by reducing sampling error, increasing statistical power, and improving generalizability and precision. In this study, principal component analysis indicated no significant stratification in the population (Figure 2), and the QQ plot showed a good fit between the observed and predicted values, indicating the absence of any significant population stratification. In addition, we used MALDI-TOP MS to identify 11 SNP loci associated with meat quality SNPs and analyzed the frequencies of candidate SNPs, which corresponded to the results of the GWAS analysis. In summary, the results of this study are valid and reliable, and SNPs can be used as breeding markers for meat geese.

[0060] Crude fat content (CFC) of meat is a key indicator of its quality and has been shown to be associated with several other quality traits. In this study, we investigated the genetic determinants of CFC in geese and identified two significant single-nucleotide polymorphisms (SNPs) located in the transmembrane protein 65 (TMEM65) and maternal antidecapentaplegic homolog 6 (SMAD6) genes. TMEM65 is a mitochondrial inner membrane protein that plays a crucial role in oxidative stress response, oxygen consumption, and citrate synthase activity. Because CFC is closely related to fat metabolism, which is influenced by mitochondrial oxidative function, we hypothesized that TMEM65 might be involved in fat oxidation metabolism and indirectly influence CFC levels. Fatty acid metabolism significantly influences flavor and intramuscular fat production, which in turn influences meat tenderness, water retention, and flavor presentation. Therefore, the identification of genetic markers associated with CFC, such as the SNPs identified in TMEM65 in this study, may be important for selecting and breeding animals with desirable meat quality traits.

[0061] Flesh color (L * 、a * and b * ) reflects changes in muscle physiology, biochemistry, and microbiology and is an important indicator for evaluating meat appearance. Meat color can affect the functional properties and quality of processed meat products. Our study (Table 2) identified 30 SNPs significantly associated with meat color (MC). Glutamine synthetase (GSS), one of the identified candidate genes, plays a crucial role in multiple cellular processes. Its primary function is to produce glutathione, protect cells from oxidative damage, and facilitate amino acid transport. Furthermore, GSS is involved in the detoxification of xenobiotics. The presence of GSS as a candidate gene suggests its importance in protecting cells and maintaining cellular homeostasis. Further exploration of the role of GSS in these processes will provide valuable insights into its contribution to cellular health and its potential impact on various biological functions. Ras-related GTP-binding protein C (RRAGC) and solute carrier organic anion transporter family member 5A1 (SLCO5A1) play crucial roles in cellular responses to amino acid availability. RRAGC, along with three other Rag proteins, plays a crucial role in the activation of the amino acid-sensitive mTORC1 pathway, regulating metabolic and physiological processes such as lipid metabolism, autophagy, and muscle protein synthesis. After slaughter, cellular oxygen supply decreases, leading to accelerated breakdown of adenosine triphosphate (ATP), an increase in free radicals, lactate accumulation, and changes in pH. These redox reactions promote protein denaturation and increase fluid loss, leading to changes in meat color and water retention. Consequently, changes in meat color and water retention occur. Therefore, we hypothesized that GSS, RRAGC, and SLCO5A1 may be responsible for carcass color development through oxidative metabolism.

[0062] Shear force (SF), a key parameter for assessing meat tenderness, is influenced by multiple factors, including muscle fiber properties (e.g., diameter, density, type, and integrity), connective tissue composition (e.g., content, type, and cross-linking status), myogenic fibrinolytic enzyme properties, and intramuscular fat content. In this study, we identified seven single-nucleotide polymorphisms (SNPs) located in Bcl-2 modifier factor (BMF), nuclear integral membrane protein 2 (NEMP2), and mitochondrial NAD-dependent malic enzyme (NAD-ME) that were significantly associated with SF in geese. MALDI-TOF mass spectrometry results further validated the associations. In this study, we confirmed a significant association between the genotypes of a SNP in BMF (chr2:36812553) and two SNPs in NAD-ME (chr12:31782870, chr12:31781825) and shear force (SF) in goose meat. BMF, a member of the Bcl2 protein family, is an intracellular damage sensor that triggers apoptosis and may mediate myofiber atrophy. NAD-ME participates in various metabolic pathways, providing the reducing power NADPH for the synthesis of various cellular components. In poultry, NAD-ME serves as an energy source for the synthesis of long-chain fatty acids in the liver. We hypothesized that BMF may directly affect myofiber structure, while NAD-ME may influence oxidoreductase activity and intramuscular lipogenesis in muscle, ultimately affecting SF.

[0063] There are three reasons why the SNPs included in the GWAS analysis had not been previously reported. First, in this study, we utilized the first chromosome-level goose genome sequence (version ASM1303099v1) as a reference, which improved the genome assembly and annotation compared to previous studies. Furthermore, the lack of similar studies on goose meat quality data and the subtle changes that geese undergo during domestication from wild migratory birds have led to the lack of reporting of many other genes associated with meat quality in this study.

[0064] 4 Conclusion

[0065] In this study, we conducted a GWAS analysis of goose meat quality traits and identified 43 SNPs significantly associated with these traits. Furthermore, we used MALDI-TOP MS to genotype selected SNPs associated with these traits in the same population. Our results revealed 28 annotated genes that showed promise as potential candidate genes associated with the five studied meat quality traits. These genes represent novel genetic markers that could be implemented in marker-assisted selection programs for geese. Importantly, our study contributes to a comprehensive understanding of the genetic mechanisms underlying goose meat quality traits and lays a solid foundation for future research in this field.

[0066] The technical features of the above embodiments can be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0067] The above-described embodiments merely represent several implementation methods of the present application. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that a person skilled in the art could make various modifications and improvements without departing from the spirit of the present application, all of which fall within the scope of protection of the present application. Therefore, the scope of protection of the present patent application shall be determined by the appended claims.

Claims

1. A SNP molecular marker associated with goose meat quality traits, characterized in that: The SNP molecular markers are as follows:

2. The SNP molecular marker associated with goose meat quality traits according to claim 1, characterized in that: The 11 SNPs genotypes include the following: Related to b*meat yellowness: chr13:25432644, ctg930:11921, chr36:5611038, ctg2092:176333, chr29:8278595, chr9:33911228, chr1:24867798, chr7:16992800, Related to SF meat: chr12:31782870, chr12:31781825, chr2:36812553.

3. The SNP molecular marker associated with goose meat quality traits according to claim 1, characterized in that: MALDI-TOF MS primer sequences:

4. The SNP molecular marker associated with goose meat quality traits according to claim 1, characterized in that: Frequency and genotype of selected goose meat quality-related SNPs:

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