KR20120131724A - Markers for porcine meat quality - Google Patents

Abstract

PURPOSE: A gene marker and a method for diagnosing pork quality are provided to remarkably improve genetic ability of a breeding pig and to enhance pig selection accuracy. CONSTITUTION: A marker for diagnosing pork quality is a polynucleotide containing 8-500 continuous nucleotides containing SNP or a complementary polynucleotide thereof among polynucleotides selected sequence numbers 1-17. The SNP exists at 61th base of each sequence. A microarray for diagnosing pork quality contains the polynucleotide, a polypeptide encoded by the polynucleotide, or cDNA of the nucleotide. A method for determining pork quality comprises: a step of providing isolated nucleic acid samples; and a step of determining allele of each SNP of one or more polynucleotides selected from sequence numbers 1-17.

Description

Markers for porcine meat quality

The present invention relates to genetic markers and diagnostic methods for the diagnosis of meat quality in pigs. More specifically, the present invention relates to markers that can be used for marker assisted selection (MAS) that can increase accuracy and maximize genetic capacity when selecting pigs and how to use them.

In selecting the vertical axis, the selection was based on the selection index formula based on the phenotype. Recently, the selection or selection of variants using molecular markers is not only a trait controlled by a single gene, but also a marker-assisted selection of traits controlled by Quantitative Trait Loci (QTL). Simulations show that MAS can be effectively used. In general, molecular markers can be used to measure genetic capacity 25-35% more accurately than the selection effect by phenotypic value, which can dramatically increase heritability. In the case of pig hog and piglet selection, the introduction of DNA markers in the existing method based on phenotypic value can improve the accuracy of selection, thereby maximizing genetic improvement.

The genes or markers currently used in the hog industry are HAL (flesh), ESR (liver number), PRLR (liver number), RBP4 (liver number), KIT (white hair color), MC1R (red / black hair color), MC4R ( Growth, obesity), FUT1 (edema disease), RN (flesh), AFABP (myopathy), HFABP (myopathy), PRKAG3 (flesh), IGF2 (conductor composition), and the like.

Seed income is estimated at 1,800 heads (1,836 heads per year), which is about 4 billion won a year, and about 30% replacement (550 heads) is expected to save about 1 billion won annually.

In recent years, the inclusion of genomic information in the selection of breeders in cattle and chickens is a global trend, and the pig meat-related monobasic polymorphism genotype will be very effectively used as a genetic DNA marker for pig selection.

Under these technical backgrounds, the present inventors have made efforts to develop genetic markers and diagnostic methods for the diagnosis of meat quality in pigs.

After all, it is an object of the present invention to provide a marker for porcine meat diagnosis.

Still another object of the present invention is to provide a microarray for detecting monobasic polymorphism comprising the polynucleotide.

Still another object of the present invention is to provide a kit for detecting monobasic polymorphism comprising the polynucleotide.

Still another object of the present invention is to provide a porcine meat diagnostic method using the polynucleotide.

In order to achieve the above object, the present invention provides a pig meat diagnostic marker including the following process and a diagnostic method using the same.

By using the marker factor according to the present invention, it is possible to measure the genetic ability 25-35% more precisely than the selection effect by the phenotype value, which can drastically increase the genetic capacity improvement of the sows, and the income of the sows is about 1,800 heads (1,836 heads a year). ), It is expected to save about KRW 1 billion annually if it replaces about 30% (550 heads) with imports worth KRW 4 billion annually.

Hereinafter, the present invention will be described in more detail.

According to one aspect of the invention, in the polynucleotide selected from the group consisting of SEQ ID NO: 1 to 17 as a polynucleotide consisting of 8 to 500 consecutive nucleotides containing each SNP position base, or a complementary polynucleotide thereof The position of each SNP is the 61st base of each SEQ ID NO, and the base of each SNP position may be provided with a marker for meat diagnosis of pigs, characterized in that the base of the allele A or a described in Table 1 below.

SEQ ID NO: Allele A Allele a One T C 2 A G 3 A G 4 A G 5 A G 6 T C 7 A G 8 A G 9 A G 10 A C 11 T C 12 A G 13 T G 14 T C 15 T C 16 T C 17 A G

According to another aspect of the invention, there can be provided a microarray for the qualitative diagnosis of swine comprising said polynucleotide, a polypeptide encoded by it or cDNA of said polynucleotide.

According to another aspect of the present invention, a kit for meat diagnosis of swine comprising a polypeptide encoded by the polypeptide or cDNA of the polynucleotide may be provided.

According to another aspect of the invention, a) providing an isolated nucleic acid sample; And b) determining an allele of each SNP position of at least one polynucleotide selected from the group consisting of SEQ ID NOs: 1 to 17.

According to another aspect of the invention, a) providing an isolated nucleic acid sample; b) determining an allele of each SNP position of at least one polynucleotide selected from the group consisting of SEQ ID NOs: 1-10; And it can be provided a method for determining the muscle fat content of pigs comprising the determination as shown in Table 10 according to the determined allelic type.

According to another aspect of the invention, a) providing an isolated nucleic acid sample; b) determining an allele of each SNP position of at least one polynucleotide selected from the group consisting of SEQ ID NOs: 2, 9 and 11-16; And it can be provided a method for determining the back fat thickness of the pig comprising determining as shown in Table 11 according to the determined allelic type.

According to another aspect of the invention, a) providing an isolated nucleic acid sample; b) determining an allele of each SNP position of at least one polynucleotide selected from the group consisting of SEQ ID NOs: 9 and 17; And it can be provided a method of confirming the shear force of the pork comprising determining as shown in Table 12 according to the determined allelic type.

According to one embodiment, step b) comprises allele-specific probe hybridization, allele-specific amplification, sequencing, 5 'nuclease. 5 'nuclease digestion, molecular beacon assay, oligonucleotide ligation assay, size analysis and single-stranded conformation polymorphism It may be carried out by a method selected from the group consisting of.

Hereinafter, the present invention will be described in more detail with reference to Examples. It should be understood, however, that these examples are for illustrative purposes only and are not to be construed as limiting the scope of the present invention.

Example  Pig Intramuscular fat  Content association Cover factor  excavation

DNA was extracted from the blood of 551 pigs with meat-like data using the Wizard Genomic DNA Purification Kit (Promega, Madison, WI, USA), and iSelect consists of 62,163 SNPs. SNP genotyping was performed using Infinium Porcine ArrayChips (Illumina, San Diego, Calif., USA) to identify markers associated with intramuscular fat content in pigs.

All SNP data obtained after genotyping were tested for SNP QCs such as Hardy-Weinberg equilibrium (HWE) and minor allele frequency (MAF) using the R / SNPassoc package. whole genome association analysis

Excluded (eg HWE; P <0.05, MAF; <10%). Single marker regression analysis was used to test the association between color phenotypes and SNP markers for color-related QTL detection, and the additive genetic effect of each SNP on color phenotypes was modeled as shown in Model I. Significant SNPs were analyzed using the R / assoc package. Multiple regression analysis based on all statistically significant SNPs selected in relation to chromosome in order to find the optimal SNP combination of chromosome among statistically significant SNPs selected through statistical model I Was carried out as in Model II in ASREML.

Where y is the phenotype vector for fixed effects including gender and age at carcass, X is the incidence matrix for SNP, b is the regression coefficient vector for single SNP genotype, and Z is for animal effect The incidence matrix, e, represents the vector of the residuals.

Where y is a phenotype vector, is a regression coefficient for fixed effects, a is a random animal effect, q is all significant SNPs (QTL) detected in model I, and e is a random residual effect.

For statistical inference, the false discovery rate (FDR) suggested by Benjamini and Hochberg (1995) was used for the complex hypothesis test, and all significant values were calculated at the 5% chromosome-wise FDR level. .

As shown in Table 2, porcine 60K SNP chip analysis and correlation analysis between QTL and SNP using DNA extracted from Berkshire tissues containing 62,163 SNPs in the total swine genomes. 10 markers associated with the intramuscular fat content of were identified (chromosome-wise P <0.05).

Table 2 shows the results of the single marker regression of the pig muscle fat content.

SEQ ID NO: SNP Base sequence One H3GA0006088 TCAAAGATGTAACTTCAAATGGAGCCTCTCATCCAGCACAGTAATGGTTTTCCTTGGCAA [T / C] AACAATACAGCGCACAGGGAGGCCCTGTTCTAAAACCAGTGCCTTGGGTGTCCTGGGAAA 2 M1GA0010485 CGTATGGAGATTCCCAGGCTAGGGCTCAAATCAGAGCTGCAGCTGCCGGTCTATGGCATA [A / G] CCACAGCAACACTAGATCCAGGTCGCTTCTTCAGCCTAAGCCACATCTCAATGCAACACA 3 ALGA0042253 CTTGCAGACCAGGCCCTGCTGGGTTCCCTGTGCTGTCCTGCCCCTGACAGAAGGCACTAG [A / G] AAACCAGAGCTGAAAGAGTCCCAGTCCATCTGCCAGAGCACAAGCCAGGGGCGAGGGAGG 4 MARC0056863 GGGTCTGATGGATGGCTAAACCTGTGTTATTTTAATTATCTCACACTGTCCCCGAATTCA [A / G] AGCAATCAGTCTTCAAATTTGCTGTCCCTT 5 ASGA0009568 TAAACAGGTAAATTTCTCAACATCCAAGAGTTTGGCCAAGTGCCTTTTCACAGCTTAGGT [A / G] GTGGGTGCAGAGGGGGGCTGTCTACCCAGGCACTGCCTGTGAAAATGGAATCAAGGCAGC 6 H3GA0036993 TATGACTGCTTTCATGCCACAGGGGCAGGGTTGAGTAGTTGTGATAGAGACCATATGGCT [T / C] GCAAAATCTAAAATATTTACTCCCTGGCCCTTTGCAAAAAATAGTTTTGCCAATGCCTGC 7 DIAS0003803 GTTCAGCTGGATTTCCATTTCATTGAGGTCTCCCTCCATCTTCTTCTTGAGCCTAATGGC [A / G] TCATTCCTACTCCTGATCTCAGCATCCAGCGTGCTCTGCATGGACTCCACGATTCTAATG 8 H3GA0051522 ACACCCTGTGACTCTGGTTCCCCAGCACCAGCGGCTTGTGGGCCAGGGCAGTGCTAGGGA [A / G] GAAAGGGGATCTCTGTAGTCGGTGCATTCTGGGAAGCCCCCATCCTGGACGTCAACCAGG 9 H3GA0056106 CCACTCTGATCGTCAAAGGAGTGGGAATGGAATACAACAGCAGATGAAAGGCCAGCAGTC [A / G] GAAAGTACCCATATCCAGTTATCTTCTCACCTCCCCTTGACTCCAATTAGTCATAAACAC 10 MARC0024047 AACCATAATCCCCCCCTTGAAAATCTTGACAGTAACAGGGGGAAAAGAGGGGATTATATA [A / C] ATGATAAAACCTAGGGTGCTGATACATGAAATCCTGGATCTACTTGTCAATTTACCRACT

Table 3 shows the results of the single marker regression of the pig muscle fat content.

SEQ ID NO: SNP chromosome location Measure
(estimates) p-value R2 One H3GA0006088 2 16027956 0.79 1.66e-08 0.04 5 MASGA0009568 2 23458627 0.35 6.04e-08 0.05 10 MARC0024047 7 17921951 0.32 1.22e-07 0.04 4 MARC0056863 7 21892554 0.34 5.91e-08 0.04 3 ALGA0042253 7 62506406 0.34 3.25e-08 0.04 2 M1GA0010485 7 83091771 -0.54 2.29e-08 0.05 9 H3GA0056106 10 55831593 -0.42 9.12e-08 0.04 7 DIAS0003803 12 58264325 -0.52 6.49e-08 0.05 6 H3GA0036993 13 86440376 -0.52 6.32e-08 0.04 8 H3GA0051522 X 10872249 0.44 8.87e-08 0.05

Referring to Table 3, the estimates for each SNP can significantly determine how high and low the intramuscular fat content in any allele is relative to the mean. For example, in the case of H3GA0006088 of SEQ ID NO: 1, as shown in Table 2, T is SNP converted to C, and it can be determined that the increase is 0.79 compared with the case where it is not converted. Therefore, a negative value in the measurement of Table 3 indicates SNPs that are associated with traits that significantly reduce muscle fat.

More specifically, the influence values described in the present invention are obtained by regression analysis of the additive effects of genes on AA, AG, and GG types through genotyping of each individual, and may be considered as a slope of a regression equation. Therefore, heterotypes have a moderate effect between both homotypes, and both homotypes can be said to have an effect of + or as much as the measurement value.

Table 4 shows the results of the multimarker regression analysis on the pig muscle fat content.

SEQ ID NO: SNP chromosome location Measure
(estimates) p-value R2 One H3GA0006088 2 16027956 0.49 0.00153 ** 0.18 5 ASGA0009568 2 23458627 0.18 0.01634 ^* 0.18 4 MARC0056863 7 21892554 0.17 0.04109 ^* 0.18 3 ALGA0042253 7 62506406 0.18 0.01519 ^* 0.18 8 H3GA0051522 X 10872249 0.32 0.00026 ^*** 0.18

Example  2. Pig Back room  Associative thickness Cover factor  excavation

Using DNA extracted from Berkshire tissues with backfat thickness trait data, eight markers related to pig backfat thickness were identified by porcine 60K SNP chip analysis and correlation analysis between QTL and SNP. Table 5 shows the sequence information for each SNP associated with the backfat thickness.

SEQ ID NO: SNPs Base sequence 2 M1GA0010485 CGTATGGAGATTCCCAGGCTAGGGCTCAAATCAGAGCTGCAGCTGCCGGTCTATGGCATA [A / G] CCACAGCAACACTAGATCCAGGTCGCTTCTTCAGCCTAAGCCACATCTCAATGCAACACA 11 ALGA0041880 GTTTGCAGTTGGAGAAACTGAGGCTTAAAGAATGAAAGCTGGAGGTCCCATCATGGCTCA [T / C] TGGGTCAAAAACCCAACTAGTACCCATGAGGATGTGGGCTCCATCTCTGGCCTCGATTAA 12 ASGA0033314 AGAAGAGTGTTTGCTTTGCCACACATCCCGTTCCAGCTCGAATTTGTCGCTCGCGCTCAA [A / G] AATCTCAGTATGTGCAAGATGCCGTGTATCTGATTAAAGTTCAGGTGAATAAGTCCTTTT 13 ASGA0091915 NNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNCTGCTTTTCTTCCT [T / G] TTCCTGAGCCTTTTGAGGTTTCTGTCCTGTGAGCCTCAACTGCAGTCAGAGAAATCAGTT 9 H3GA0056106 CCACTCTGATCGTCAAAGGAGTGGGAATGGAATACAACAGCAGATGAAAGGCCAGCAGTC [A / G] GAAAGTACCCATATCCAGTTATCTTCTCACCTCCCCTTGACTCCAATTAGTCATAAACAC 14 H3GA0023120 AAACCAAAGTGTTTCAAGAGGGAAGGAGCAGCCAGCTGAGTTACAGGCCCTGAGAGGTCA [T / C] GTATGATGAAATCTTGGAAAGATCCCATAAATTCACCAGCTTGGATGTCACTGATGACAA 15 ALGA0088577 TAGCCTATAACGTAGACATTTTTGACTCTGACATTAGAGCTGCTGTTCCCCAACTTTTAT [T / C] AAACCTCCCAGACAATATTCGGGATGGGAATCCCTGAGTAAATGGTAGGTGCTGTTGGTA 16 ALGA0088592 ATCTGTCTCTTCTGGGGATATGTATGGATTAATACTGTTAGCACAAATTATTCTTATACA [T / C] GAACACTAGTAGCTACAGTTAAGGAACATAAGACTTCTCTAACTGTCAAGGACATAGACT

Table 6 shows the results of single marker regression on pig backfat thickness.

SEQ ID NO: SNPs chromosome location Measure p-value R2 12 ASGA0033314 7 47936117 -2.36 9.5e-15 0.08 11 ALGA0041880 7 55461746 -2.4 8.14e-15 0.09 2 M1GA0010485 7 83091771 -2.55 1.9e-15 0.1 14 H3GA0023120 7 118419576 1.02 2.07e-09 0.05 9 H3GA0056106 10 55831593 -1.73 1.0e-10 0.06 15 ALGA0088577 16 2303850 3.75 2.04e-08 0.04 13 ASGA0091915 - - -2.36 9.5e-15 0.08 16 ALGA0088592 - - 3.75 2.07e-08 0.04

Referring to Table 6, the estimates for each SNP can significantly determine how high and low in alleles compared to the average backfat thickness. For example, in the case of ASGA0033314 of SEQ ID NO: 12, as shown in Table 5, A is a SNP which is converted to G, and it can be determined that the backfat thickness is 2.36 lower than the case of having A.

Table 7 shows the results of the multimarker regression analysis on the thickness of the pig back fat.

SEQ ID NO: SNP chromosome location Measure p-value R2 14 H3GA0023120 7 118419576 1.00 4.96e-08 *** 0.17 15 ALGA0088577 16 2303850 2.68 9.10e-05 *** 0.17

3. Pig Shear force  Relation Cover factor  excavation

Two markers related to pig shear force were identified by porcine 60K SNP chip analysis and correlation analysis between QTL and SNP using DNA extracted from Berkshire tissues with shear force trait data. Table 8 shows the sequencing information of SNPs associated with swine shear force.

SEQ ID NO: SNP Base sequence 9 H3GA0056106 CCACTCTGATCGTCAAAGGAGTGGGAATGGAATACAACAGCAGATGAAAGGCCAGCAGTC [A / G] GAAAGTACCCATATCCAGTTATCTTCTCACCTCCCCTTGACTCCAATTAGTCATAAACAC 17 ASGA0090311 NNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNCTGAGCCACTG [A / G] TTTGAGGTGGGATTTCTCTAGGATGAGATTTCTGTAGGATGGGGCTTCTGTTTGGATGAT

Table 9 shows the results of the single marker regression for swine shear force.

SEQ ID NO: SNP chromosome location Measure p-value R2 9 H3GA0056106 One 55831593 0.14 1.73e-07 0.04 17 ASGA0090311 6 36332838 0.12 5.78e-07 0.04

Referring to Table 9, the estimates for each SNP can significantly determine how high and low the shear force in the allele is relative to the mean. For example, in the case of H3GA0056106 of SEQ ID NO: 9, as shown in Table 8, A is a SNP to be converted to G, and it can be determined that it is 0.14 lower than the unconverted case.

By using the labeling factors according to the present invention as described above, it is possible to measure the genetic ability 25-35% more accurately than the selection effect by the phenotype value, which can dramatically increase the genetic capacity improvement of the sows, and the income of the sows is about 1,800 heads ( It is expected to save about 1 billion won annually if it replaces 30% (550 heads) with imports worth about 4 billion won annually with 1,836 heads per year).

While the present invention has been particularly shown and described with reference to specific embodiments thereof, those skilled in the art will appreciate that such specific embodiments are merely preferred embodiments and that the scope of the present invention is not limited thereby. something to do. It is therefore intended that the scope of the invention be defined by the claims appended hereto and their equivalents.

                         SEQUENCE LISTING <110> RURAL DEVELOPMENT ADMINISTRATION   <120> Markers for porcine meat quality <130> NPF18886 <160> 17 <170> PatentIn version 3.2 <210> 1 <211> 121 <212> DNA <213> Sus scrofa <400> 1 tcaaagatgt aacttcaaat ggagcctctc atccagcaca gtaatggttt tccttggcaa 60 taacaataca gcgcacaggg aggccctgtt ctaaaaccag tgccttgggt gtcctgggaa 120 a 121 <210> 2 <211> 121 <212> DNA <213> Sus scrofa <400> 2 cgtatggaga ttcccaggct agggctcaaa tcagagctgc agctgccggt ctatggcata 60 accacagcaa cactagatcc aggtcgcttc ttcagcctaa gccacatctc aatgcaacac 120 a 121 <210> 3 <211> 121 <212> DNA <213> Sus scrofa <400> 3 cttgcagacc aggccctgct gggttccctg tgctgtcctg cccctgacag aaggcactag 60 aaaaccagag ctgaaagagt cccagtccat ctgccagagc acaagccagg ggcgagggag 120 g 121 <210> 4 <211> 91 <212> DNA <213> Sus scrofa <400> 4 gggtctgatg gatggctaaa cctgtgttat tttaattatc tcacactgtc cccgaattca 60 aagcaatcag tcttcaaatt tgctgtccct t 91 <210> 5 <211> 121 <212> DNA <213> Sus scrofa <400> 5 taaacaggta aatttctcaa catccaagag tttggccaag tgccttttca cagcttaggt 60 agtgggtgca gaggggggct gtctacccag gcactgcctg tgaaaatgga atcaaggcag 120 c 121 <210> 6 <211> 121 <212> DNA <213> Sus scrofa <400> 6 tatgactgct ttcatgccac aggggcaggg ttgagtagtt gtgatagaga ccatatggct 60 tgcaaaatct aaaatattta ctccctggcc ctttgcaaaa aatagttttg ccaatgcctg 120 c 121 <210> 7 <211> 121 <212> DNA <213> Sus scrofa <400> 7 gttcagctgg atttccattt cattgaggtc tccctccatc ttcttcttga gcctaatggc 60 atcattccta ctcctgatct cagcatccag cgtgctctgc atggactcca cgattctaat 120 g 121 <210> 8 <211> 121 <212> DNA <213> Sus scrofa <400> 8 acaccctgtg actctggttc cccagcacca gcggcttgtg ggccagggca gtgctaggga 60 agaaagggga tctctgtagt cggtgcattc tgggaagccc ccatcctgga cgtcaaccag 120 g 121 <210> 9 <211> 121 <212> DNA <213> Sus scrofa <400> 9 ccactctgat cgtcaaagga gtgggaatgg aatacaacag cagatgaaag gccagcagtc 60 agaaagtacc catatccagt tatcttctca cctccccttg actccaatta gtcataaaca 120 c 121 <210> 10 <211> 122 <212> DNA <213> Sus scrofa <400> 10 aaccataatc ccccccttga aaatcttgac agtaacaggg ggaaaagagg ggattatata 60 acatgataaa acctagggtg ctgatacatg aaatcctgga tctacttgtc aatttaccra 120 ct 122 <210> 11 <211> 121 <212> DNA <213> Sus scrofa <400> 11 gtttgcagtt ggagaaactg aggcttaaag aatgaaagct ggaggtccca tcatggctca 60 ttgggtcaaa aacccaacta gtacccatga ggatgtgggc tccatctctg gcctcgatta 120 a 121 <210> 12 <211> 121 <212> DNA <213> Sus scrofa <400> 12 agaagagtgt ttgctttgcc acacatcccg ttccagctcg aatttgtcgc tcgcgctcaa 60 aaatctcagt atgtgcaaga tgccgtgtat ctgattaaag ttcaggtgaa taagtccttt 120 t 121 <210> 13 <211> 121 <212> DNA <213> Sus scrofa <220> <221> misc_feature (222) (1) .. (46) <223> n is a, c, g, or t <400> 13 nnnnnnnnnn nnnnnnnnnn nnnnnnnnnn nnnnnnnnnn nnnnnnctgc ttttcttcct 60 tttcctgagc cttttgaggt ttctgtcctg tgagcctcaa ctgcagtcag agaaatcagt 120 t 121 <210> 14 <211> 112 <212> DNA <213> Sus scrofa <400> 14 aaaccaaagt gtttcaagag ggaaggagca gccagctgag ttacaggccc tgagaggtca 60 tgtatgatga aatcttggaa agatcccata aattcaccag cttggatgtc ac 112 <210> 15 <211> 121 <212> DNA <213> Sus scrofa <400> 15 tagcctataa cgtagacatt tttgactctg acattagagc tgctgttccc caacttttat 60 taaacctccc agacaatatt cgggatggga atccctgagt aaatggtagg tgctgttggt 120 a 121 <210> 16 <211> 121 <212> DNA <213> Sus scrofa <400> 16 atctgtctct tctggggata tgtatggatt aatactgtta gcacaaatta ttcttataca 60 tgaacactag tagctacagt taaggaacat aagacttctc taactgtcaa ggacatagac 120 t 121 <210> 17 <211> 121 <212> DNA <213> Sus scrofa <220> <221> misc_feature (222) (1) .. (49) <223> n is a, c, g, or t <400> 17 nnnnnnnnnn nnnnnnnnnn nnnnnnnnnn nnnnnnnnnnnn nnnnnnnnnc tgagccactg 60 atttgaggtg ggatttctct aggatgagat ttctgtagga tggggcttct gtttggatga 120 t 121

Claims (8)

A polynucleotide consisting of 8 to 500 contiguous nucleotides comprising each SNP position base in a polynucleotide selected from the group consisting of SEQ ID NOs: 1 to 17, or a complementary polynucleotide thereof, wherein the position of each SNP is A 61st base, and the base of each SNP position is a base for diagnosing porcine meat, characterized in that the base of the allele A or a shown in Table 1 below.
[Table 1]

A microarray for the qualitative diagnosis of swine comprising the polynucleotide of claim 1, the polypeptide encoded by the polynucleotide or the cDNA of the polynucleotide.
A kit for meat diagnosis of pigs comprising the polynucleotide of claim 1, a polypeptide encoded by the polynucleotide, or a cDNA of the polynucleotide.
a) providing an isolated nucleic acid sample; And
b) determining an allele of each SNP position of at least one polynucleotide selected from the group consisting of SEQ ID NOs: 1-17
Pork meat quality determination method comprising a.
a) providing an isolated nucleic acid sample;
b) determining an allele of each SNP position of at least one polynucleotide selected from the group consisting of SEQ ID NOs: 1-10; And
Intramuscular fat content determination method of pig comprising the determination as shown in Table 2 according to the determined allelic type.
TABLE 2

a) providing an isolated nucleic acid sample;
b) determining an allele of each SNP position of at least one polynucleotide selected from the group consisting of SEQ ID NOs: 2, 9 and 11-16; And
Method of determining the back fat thickness of a pig comprising determining as shown in Table 3 according to the determined allelic type.
TABLE 3

a) providing an isolated nucleic acid sample;
b) determining an allele of each SNP position of at least one polynucleotide selected from the group consisting of SEQ ID NOs: 9 and 17; And
Shear force confirmation method of pork comprising the determination as shown in Table 4 according to the determined allelic type.
TABLE 4

5. The method of claim 4, wherein step b) comprises allele-specific probe hybridization, allele-specific amplification, sequencing, 5 'nuclease. 5 'nuclease digestion, molecular beacon assay, oligonucleotide ligation assay, size analysis and single-stranded conformation polymorphism Pig meat quality determination method characterized in that performed by the method selected from the group consisting of.