A molecular marker associated with backfat thickness and drip loss traits in pork

Abstract

The present invention discloses a molecular marker related to the backfat thickness of pigs and the drip loss trait of pork. Specifically, at 70905841bp of the ANGPTL4 gene, there is a G / C gene. When it is a GG genotype, the backfat thickness of such pigs is significantly lower than that of other genotypes, and when it is a CC genotype, the drip loss of pork of such pigs is significantly lower than that of other genotypes; at 70912840bp of the ANGPTL4 gene, there is an A / G gene. When it is an AA genotype, the backfat thickness of such pigs is significantly lower than that of other genotypes, and when it is a GG genotype, the drip loss of pork of such pigs is significantly lower than that of other genotypes. This molecular marker significantly affects the backfat thickness trait and the drip loss trait of pork. By determining the genotype of the polymorphism, it is possible to effectively identify whether it is a genotype that can produce low backfat thickness and low drip loss, providing a detection technology segment for early breeding and improving breeding efficiency.

Description

Technical Field

[0001] The invention relates to the field of molecular technology, and in particular to a molecular marker associated with pig backfat thickness and pork drip loss traits. Background Art

[0002] As the main source of meat for Chinese people, the pig industry has always been a very important part of my country's animal husbandry. In China, pork production has always accounted for about 60% of the total meat production. With the development of science and technology, the market demand for pork has gradually increased, prompting the breeding industry to excessively pursue pig growth rate, feed conversion rate and lean meat rate, resulting in a decline in pork quality and poor flavor. How to solve the contradiction between pork quality, flavor and breeding efficiency is also a problem that the industry continues to solve. The key indicators of pork quality, such as meat color, tenderness, flavor, juiciness, water holding capacity, etc. are mainly related to intramuscular fat deposition and sugar metabolism type that determine the muscle tissue structure. For example, intramuscular fat content is positively correlated with pork color score and muscle brightness (L*), redness (a*), and yellowness (b*). Among them, the greatest impact on brightness (L*) is mainly because white fat cells can increase the brightness of meat and increase the L* value measured by the colorimeter. Intramuscular fat can directly affect muscle pH and water content, and indirectly affect drip loss by affecting muscle pH. Watanabe and other scientists found that there is a significant negative correlation between intramuscular fat content and water content. In order to increase pork production and meet market demand, it is urgent to identify some important molecular markers for the genetic improvement of meat quality traits, and then select and improve related traits through marker-assisted selection or genomic selection. Summary of the invention

[0003] The object of the present invention is to provide a molecular marker related to the backfat thickness of pigs and the drip loss traits of pork.

[0004] To achieve the above object, the present invention provides a molecular marker related to pig backfat thickness and pork drip loss, characterized in that:

[0005] At 70905841bp of the ANGPTL4 gene, there is a G / C gene. When it is the GG genotype, the back fat thickness of this type of pig is significantly lower than that of other genotypes. When it is the CC genotype, the drip loss of pork from this type of pig is significantly lower than that of other genotypes.

[0006] At 70912840bp of the ANGPTL4 gene, there is the A / G gene. When it is the AA genotype, the back fat thickness of this type of pigs is significantly lower than that of other genotypes. When it is the GG genotype, the drip loss of pork from this type of pigs is significantly lower than that of other genotypes.

[0007] The present invention also provides a method for detecting pig backfat thickness and pork drip loss, characterized in that the molecular markers are detected on the pig to be detected so as to determine the pig backfat thickness and pork drip loss.

[0008] The present invention also provides a pig assisted breeding method, characterized in that the molecular markers in the breeding pigs are detected to determine the back fat thickness of the breeding pigs and the drip loss traits of the pork.

[0009] Based on the selected Large White pig breeder population, the present invention conducted a genome wide association study (GWAS) of meat quality traits through whole genome sequencing, conducted an association analysis between key genes and pork quality traits, and identified two SNP molecular markers that affect pork quality. At present, the above results have not been reported, so the discovery of this marker has potential important value for the genetic improvement of pork quality traits.

[0010] The present invention is based on a group of 416 Large White pigs. The intramuscular fat content of the longissimus dorsi muscle of the pigs, as well as the pH, meat color, and drip loss traits of the pork were measured and recorded through slaughter experiments. The SNP typing data obtained by whole genome sequencing was used for association analysis with the target gene ANGPTL4 (Ensembl: ENSSCG00000013599). It was found that 2 SNP sites of the ANGPTL4 gene were significantly correlated with pork quality. The specific sites are as follows:

[0011] (1) At nucleotide position 70905841, there is a C / G polymorphism, and the back fat thickness of individuals with the SNP molecular marker GG genotype is significantly lower than that of other genotypes, and the drip loss of pork of individuals with the CC genotype is significantly lower than that of other genotypes.

[0012] (2) At nucleotide position 70912840, there is a G / A polymorphism, and the back fat thickness of individuals with the SNP molecular marker AA genotype is significantly lower than that of other genotypes, and the pork drip loss of individuals with the GG genotype is significantly lower than that of other genotypes.

[0013] The molecular markers of the present invention have also been verified in actual breeding.

[0014] The beneficial effects of the present invention are as follows: the SNP molecular markers related to the meat quality traits of pig carcasses provided by the present invention significantly affect the backfat thickness and drip loss traits of pork. By determining the genotype of the polymorphism, it is possible to effectively identify whether it is a genotype that can produce low backfat thickness and low drip loss, providing a detection technology segment for early breeding and improving breeding efficiency. The present invention also provides a reliable molecular marker for the genetic improvement of pork quality traits, which is of great significance to the genetic improvement of pigs. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 It is the frequency distribution diagram, kernel density curve and standard normal curve diagram of meat quality phenotypic values. DETAILED DESCRIPTION

[0016] Embodiments of the present invention are described in detail below, and examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements with the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present invention, and should not be construed as limitations of the present invention. Those who do not indicate specific techniques or conditions in the embodiments are carried out according to the techniques or conditions described in the literature in this area or according to the product specification. Those who do not indicate the manufacturer of the reagents or instruments used are all conventional products that can be obtained commercially. In the following embodiments, if not clearly stated, "%" refers to weight percentage.

[0017] Example 1: Association analysis between SNP sites of key gene ANGPTL4 and pork quality traits

[0018] 1. Test materials

[0019] Large White pigs were used as the research object. A total of 461 pigs (including 289 boars and 172 sows, about 180 days old) were slaughtered and tested. The phenotypic data of all carcass traits and meat quality traits were collected, and the longissimus dorsi muscle tissue was collected for genomic DNA extraction.

[0020] 2. Test methods

[0021] 2.1. Carcass trait determination

[0022] The straight-line distance from the midpoint of the anterior edge of the pubic symphysis to the upper edge of the anterior fossa of the atlas is measured as the carcass length; the average backfat thickness reflects the pig's fat deposition ability. The backfat thickness is measured at three points: the thickest part of the shoulder, the last rib, and the junction of the lumbar spine. The average value of the three points is then taken as the average backfat thickness.

[0023] 2.2. Determination of meat quality traits

[0024] After slaughter, 500g of back muscle was cut from the longissimus dorsi (between 12-13 ribs) for later use. The following indicators were measured:

[0025] (1) Flesh color (L, a, b)

[0026] Meat color was measured within 45 minutes after slaughter using a Minolta CR-410 colorimeter. The colorimeter was calibrated to measure the meat color value of the cut surface of the muscle sample, where the L value represents the light reflectance value, the a value and the b value represent the redness and yellowness values ​​respectively (mainly the L value, the larger the L, the whiter).

[0027] (2) pH

[0028] pH meter (testo 205). After the instrument is calibrated accurately, insert the pH meter electrode into the center of the sample, wait until the electrode is fully inserted and the value stabilizes before reading. Measure the pH value (pH) within 45 minutes after slaughter. The normal pH value of pork is 6.0-6.5 (generally speaking, the pH of local pigs is relatively high).

[0029] (3) Drip loss

[0030] Determine within 2 hours after slaughter. Take the "drip loss" sample of the longissimus dorsi muscle, cut it into a cuboid of 5 cm in length, 3 cm in width and 2 cm in thickness (make sure the muscle sample is a cuboid with the muscle fibers facing downward), weigh it on an electronic balance (w1, weight is about 10-15 g), then put it in a drip loss tube, hang it in a refrigerator at 4°C for 24 hours and weigh it (w2).

[0031] Drip loss = (W1-W2) / W1*100%.

[0032] (4) Intramuscular fat content

[0033] The near infrared measuring instrument is used for direct measurement. The measuring instrument is FOSS FoodScan 2 and a meat grinder. About 50g of the longissimus dorsi muscle is taken and minced in a household meat grinder. The sample is then placed in the sample container of the near infrared measuring instrument and the measurement is started. The results of the intramuscular fat measurement are displayed after about 2 minutes.

[0034] 2.3 DNA extraction

[0035] DNA was extracted using the commonly used phenol-chloroform crude extraction method (for the phenol-chloroform crude extraction method, see Sambrook J, Fritsch EF, Maniatis T. Molecular Cloning Laboratory Guide [M]. 2nd edition. Jin Dongyan, Li Mengfeng. Beijing: Science Press, 1999. 465-467) or other recognized extraction methods with equal effectiveness, which are all reported commonly used methods.

[0036] 2.4. SNP typing of ANGPTL4 gene

[0037] The whole genome sequencing of 461 individuals was performed using the GenoBaits Porcine SNP50K Panel (Porcine 50K Liquid Phase Chip), which contains 52,000 SNPs in the whole genome. After read alignment, sorting, tag duplication, base quality recalibration and variation detection, 45.94 million SNP sites were initially identified. The Plink software was used to calculate the individual deletion rate, SNP site deletion rate, and minimum allele frequency MAF, and quality control standards were established, and finally 45,600 high-quality SNP markers were obtained.

[0038] Based on the genotyping results of the GenoBaits Porcine SNP50K Panel (liquid phase 50K chip, containing 108,923 core SNP and segment SNP sites) of the above-mentioned experimental pig population, the whole genome sequencing data of 1,602 pigs of multiple breeds were used as the reference group to fill the chip data to the sequencing data level. Both the sequencing data and the chip data were aligned to the pig reference genome version 11.1 (Sus scrofa 11.1), and the 18 autosomes (105,477 SNPs) of the chip data were genotyped using Beagle 5.1 software. The number of SNP sites after filling was 42,523,218. The quality control of the filled genotype data was performed using VCFtools 0.1.16 (http: / / vcftools.sourceforge.net) software according to the following principles: (1) SNP sites with a minimum allele frequency (MAF) less than 0.01 were removed; (2) SNP sites with Hardy-Weinberg equilibrium P < 1 × 10 -6 (3) SNP sites with missing genotypes greater than 5% were removed. After quality control, 16,166,580 SNP sites remained for subsequent analysis, and the average DR2 (Dosage R-squared) reported by Beagle software was greater than 0.90.

[0039] 2.5. Association analysis between ANGPTL4 gene and pork quality traits

[0040] The association analysis between the mutation site and backfat thickness, IMF, PH, meat color, and drip loss traits was calculated using the variance analysis function of r. The complete model is as follows:

[0041] (1) Yijkh of AVB traits=μ+BW+Bi+Sj+Mk+Gh+eijkh;

[0042] (2) IMF trait Yijh=μ+BW+Bi+Sj+Gh+eijh;

[0043] (3)DL, pH and meat color traits Yij=μ+Bi+Sj+Gh+eij.

[0044] In the above model, Yijkh, Yijh and Yij are individual phenotypic records; μ is the overall mean; BW is the body weight of the sample; Bi is the batch effect (i = 1, 2, 3, 4, 5); Sj is the sex effect (j = 1, 2); Mk is the measurer effect (k = 1, 2, 3, 4, 5, 6); Gh is the genotype effect (h = 1, 2, 3); eijkh, eijh and eij are random errors. Duncan's test was used to evaluate the significance of the genotype mean, and the phenotypic values ​​were expressed as mean ± SD. The analysis found that two SNPs in ANGPTL4 were associated with backfat thickness and drip loss (Table 1).

[0045]

[0046] Table 1 Correlation between 2 SNPs of ANGPTL4 and backfat thickness and drip loss

[0047] Note: SD: standard deviation. a, b: Means with different superscripts in the same row are significantly different (P<0.05). The numbers shown in brackets are the number of individuals with a specific genotype (Duncan test).

[0048] As can be seen from Table 1, for the ANGPTL4 gene, rs339039482 (g.70905841C>G) is significantly correlated with back fat thickness, and the back fat thickness of pigs with genotype GG is lower than that of pigs with genotype CC and GC. rs705163308 (g.70912840G>A) is also significantly correlated with back fat thickness, and the back fat thickness of pigs with genotype AA is lower than that of pigs with genotype GG and AG.

[0049] Example 2

[0050] 1. Descriptive statistics and normality test of meat quality phenotype data

[0051] Microsoft office Excel 2021 software was used to calculate the mean, standard deviation, coefficient of variation, maximum and minimum values ​​of the measured carcass traits and meat quality phenotypic values. The Shapiro-Wilk test of the R language was used to test the normality of the carcass trait phenotypic values, and the frequency distribution histogram, kernel density curve and standard normal curve of the carcass trait phenotypic values ​​were drawn using the R language. In this example, the R software package was used to calculate the Pearson correlation coefficient between carcass traits and meat quality traits and the correlation between each phenotype.

[0052] The descriptive statistical analysis of meat quality trait parameters such as intramuscular fat content (IMF), 24-hour drip loss (DL24), 48-hour drip loss (DL48), pH value (pH), meat color L value, a value and b value (L, a, b) is shown in Table 1. Among them, the coefficient of variation of IMF, DL24, DL48 and meat color b value is relatively high. The phenotypic values ​​of meat quality traits were tested for normal distribution by Shapiro-Wilk. The results showed that except for the meat color a value, which conformed to the normal distribution (p>0.05), the other traits did not conform to the normal distribution (p<0.05). The frequency distribution diagram, kernel density curve and standard normal curve of the phenotypic values ​​of meat quality traits are shown in Tables 2 and Figure 1 shown. Figure 1 These are the frequency distribution graph, kernel density curve and standard normal curve graph of meat quality phenotypic values, where the red curve is the standard normal curve and the blue curve is the kernel density curve.

[0053] Table 2 Descriptive statistics and normality test table of meat quality phenotypic data

[0054]

[0055]

[0056] 2. Fixed effects and significance tests

[0057] According to the actual situation of the test pigs slaughtered and measured in this embodiment, the effects of factors such as slaughter batch, gender, measurer and carcass weight after slaughter on the carcass traits of Large White pigs are comprehensively considered. The effects of factors such as slaughter batch and gender on meat quality traits are considered. The slaughter batch effect is divided into 5 levels according to the time of slaughter, the gender is divided into 2 levels according to male and female, the measurer effect is divided into 6 levels according to the specific personnel who implement the measurement operation, and the carcass weight after slaughter is considered as a covariate.

[0058] The GLM (Generalized Linear Models) statistical analysis of R software was used to conduct a significance test on the relevant factors affecting pig carcass traits. The analysis model is as follows:

[0059] Y ijkh =μ+B i +S j +M k +W h +e ijkh

[0060] Among them, Y ijkh is the individual carcass trait phenotypic value, μ is the population mean, B i is the batch effect (i=1,2,3,4,5), S j is the gender effect (j=1,2), M kis the measurer effect (k=1,2,3,4,5,6), W h is the individual carcass weight after slaughter, e ijkh In addition, the eye muscle area is measured by a planimeter, and the influence of human factors can be ignored. Therefore, the eye muscle area does not consider the effect of the measurer. Since carcass weight does not affect the number of ribs, the rib number trait does not consider the effect of carcass weight after slaughter.

[0061] The results of the fixed effect significance test for meat quality traits are shown in Table 3. The levels of influence of slaughter batch effect and gender effect on different meat quality traits are not the same. Gender has a significant effect on all traits except meat color a value and b value, and batch effect has a very significant effect on pH value and meat color b value.

[0062] Table 3 Significance test of fixed effect of meat quality traits

[0063] Characteristics batch gender IMF *** DL24 * DL48 *** pH *** * L ** a b ***

[0064] “***” indicates p<0.001, “**” indicates p<0.01, and “*” indicates p<0.05.

[0065] 3. Genetic parameter estimation

[0066] The SNP additive genetic variance was estimated using genome-based restricted maximum likelihood (GREML) analysis using GCTA v1.94.0 software, and the genetic correlation between the two traits was estimated using a bivariate GREML model. The statistical model is as follows:

[0067] (1) Heritability estimation model:

[0068] y=Xβ+Zg+ε,

[0069] Where y is the phenotypic value, β is the fixed effect (slaughter batch, sex, measurer, etc.) and covariate (carcass weight after slaughter), X and Z are the corresponding structure matrices, ε is the random residual, g is the effect of all autosomal SNPs, var(g) = Gσ g 2 , G is the genetic relationship matrix (GRM) estimated by all autosomal SNPs. The heritability estimated based on all autosomal SNPs is h g 2 =σ g 2 / σ p 2 , σ g 2 is the additive genetic variance, σp 2 is the phenotypic variance.

[0070] (2) The bivariate GREML analysis model is as follows:

[0071] Character 1 is

[0072] y1=X1β1+Z1g1+ε1,

[0073] Character 2 is

[0074] y2=X2β2+Z2g2+ε2;

[0075] The variance-covariance matrix V is defined as:

[0076]

[0077] Where I is the identity matrix, is the covariance between g1 and g2.

[0078] The fixed effects used in the above model (1) are the fixed effects (slaughter batch, gender, measurer, etc.) and covariates (carcass weight after slaughter) that have been shown to have significant effects on the corresponding traits by significance tests. The carcass traits (except eye muscle area) in model (2) all consider slaughter batch, gender, measurer, etc. as fixed effects and carcass weight after slaughter as covariates. The eye muscle area trait does not consider the measurer effect, and the meat quality traits consider the slaughter batch and gender effects.

[0079] 3.1 Correlation between phenotypic values ​​of traits

[0080] The correlation coefficients between the phenotypic values ​​of meat quality traits were between -0.42 and 0.77, and the detailed results are shown in Table 4. Among them, the phenotypic correlation between IMF and other meat quality traits was low, with a Pearson correlation coefficient of -0.17 to 0.17; the phenotypic correlation between DL24 and DL48 traits was the highest, with a Pearson correlation coefficient of 0.77; DL24, DL48 and pH trait phenotypes showed moderate negative correlations, with Pearson correlation coefficients of -0.4 and -0.42, respectively; pH ​​trait and meat color L, a and b trait phenotypes showed low negative correlations, with Pearson correlation coefficients of -0.33, -0.22 and -0.17, respectively; among meat color traits, meat color b value had a high phenotypic correlation with meat color L value and a value, with Pearson correlation coefficients of 0.59 and 0.44, respectively.

[0081] Table 4 Pearson correlation coefficients (phenotypic correlation) between meat quality traits

[0082] Pearson IMF DL24 DL48 pH L a b IMF 1 DL24 -0.1* 1 DL48 -0.17*** 0.77*** 1 pH 0.03 -0.4*** -0.42*** 1 L 0.1* 0.33*** 0.3*** -0.33*** 1 a 0.17*** 0.29*** 0.24*** -0.22*** 0.06 1 b 0.14** 0.38*** 0.37*** -0.17*** 0.59*** 0.44*** 1

[0083] “***” indicates p<0.001, “**” indicates p<0.01, and “*” indicates p<0.05.

[0084] 3.2 Heritability

[0085] The results of the analysis of the genomic heritability of meat quality traits are shown in Table 5. The genomic heritability of each trait ranged from 0.12 to 0.54, among which pH had a low heritability (0.2 <h 2 ) traits, DL24, DL48 and b have medium heritability (0.2 <h 2 <0.4, IMF, L and a are of high heritability (h 2 >0.4) traits. IMF has the highest heritability, which is 0.54.

[0086] Table 5 Genomic heritability of meat quality traits

[0087]

[0088]

[0089] 3.3 Genetic correlation between traits

[0090] The genetic correlation coefficients between the meat quality traits are shown in Table 6. Among them, the genetic correlation between DL24 and DL48 was the highest, with a correlation coefficient of 0.85; the genetic correlation between IMF and DL24 was the lowest, with a correlation coefficient of 0.03, and the genetic correlation coefficients with other meat quality traits were -0.33 to 0.45; DL24 showed a high negative genetic correlation with pH, ​​with a correlation coefficient of -0.77, and the correlation coefficients with meat color traits were 0.24 to 0.36; DL48 showed a high negative genetic correlation with pH, ​​with a correlation coefficient of -0.68, and the genetic correlation coefficients with meat color traits were 0.24 to 0.36. The genetic correlation is low, with a correlation coefficient of 0.04 to 0.17; there is a high negative genetic correlation between pH and meat color b value, with a correlation coefficient of -0.63, and a negative genetic correlation with meat color L value, with a correlation coefficient of -0.39, and a low genetic correlation with meat color a value, with a correlation coefficient of 0.07; meat color L value has a high negative genetic correlation with a value, with a correlation coefficient of -0.60, and a high genetic correlation with meat color b value, with a correlation coefficient of 0.80; meat color a value and b value are negatively correlated, with a correlation coefficient of -0.40.

[0091] Table 6 Genomic genetic correlation coefficients among meat quality traits

[0092]

[0093] Note: The data in brackets are standard errors.

[0094] Although the embodiments of the present invention have been shown and described above, it is to be understood that the above embodiments are exemplary and are not to be construed as limitations on the present invention. A person skilled in the art may change, modify, replace and modify the above embodiments within the scope of the present invention without departing from the principles and intent of the present invention.

Claims

1. A method for detecting pig backfat thickness and pork drip loss, characterized in that: The following molecular markers of the pigs to be tested were tested to determine the backfat thickness and drip loss of the pork: At rs3390339482 of the ANGPTL4 gene, there is a G / C gene. When it is the GG genotype, the back fat thickness of this type of pig is significantly lower than that of other genotypes. When it is the CC genotype, the drip loss of pork from this type of pig is significantly lower than that of other genotypes. At rs705163308 of the ANGPTL4 gene, there is an A / G gene. When it is the AA genotype, the back fat thickness of this type of pig is significantly lower than that of other genotypes. When it is the GG genotype, the drip loss of pork from this type of pig is significantly lower than that of other genotypes. Wherein, the pig is a Large White pig.

2. A pig assisted breeding method, characterized in that: Detect the following molecular markers in breeding pigs to determine the backfat thickness and drip loss traits of pork; At rs3390339482 of the ANGPTL4 gene, there is a G / C gene. When it is the GG genotype, the back fat thickness of this type of pig is significantly lower than that of other genotypes. When it is the CC genotype, the drip loss of pork from this type of pig is significantly lower than that of other genotypes. At rs705163308 of the ANGPTL4 gene, there is an A / G gene. When it is the AA genotype, the back fat thickness of this type of pig is significantly lower than that of other genotypes. When it is the GG genotype, the drip loss of pork from this type of pig is significantly lower than that of other genotypes. Wherein, the pig is a Large White pig.

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