KR101567020B1 - Novel biomarker and method for predicting back fat traits in pigs - Google Patents

Abstract

The present invention relates to a biomarker, and more particularly to a biomarker comprising a detection means capable of detecting a single nucleotide polymorphism (SNP) with a 205th base of G or G, in a polynucleotide represented by the nucleic acid sequence of SEQ ID NO: A kit for estimating backfill thickness is provided.

Description

TECHNICAL FIELD [0001] The present invention relates to a biomarker for diagnosing the thickness of a pig back ground,

The present invention relates to a biomarker, and more particularly, to a biomarker for diagnosis of a new back ground thickness and a method of selecting the biomarker.

The livestock breeding has been repeatedly carried out a series of processes to select excellent individuals, to produce later generations using the selected livestock, and to test the ability of the later generations to select superior livestock. In the past, it has been established based on the statistical method to estimate the breeding value based on the phenotypic information expressed in the breeding process of the livestock, and to select the breeding value based on this information. Therefore, in order to obtain more efficient livestock for people, good breeding stock to be used to produce the next generation of livestock should be well selected. The quality of the livestock (milk production, weight, etc.) to be improved through selection is mostly a quantitative trait that is affected by a large number of genes. Quantitative traits are generally affected not only by a large number of genes but also by various environmental factors. Thus, in quantitative traits, it is extremely difficult to qualify traits such as individual gene actions or traits that affect them. Therefore, statistical methods have been used as a powerful tool for studying quantitative traits, and studies have been conducted by several scholars to effectively isolate genetic factors affecting quantitative traits from various environmental factors. The use of molecular genetic techniques to improve livestock will enable research on the genetic predisposition of individuals at the DNA level, and it is expected that the gene related to the trait to be improved, that is, the major gene or the locus of the quantitative trait (QTL), or selection of genetic markers associated with quantitative trait loci (QTLs) can provide a tool for realizing a genetic locus. By using molecular genetic information rather than relying solely on phenotypic information, genetic improvement can be accelerated. Information at the DNA level provides important information to select breeders as well as producers for specific key variants. Such DNA information can be used to select a quantitative trait called marker-assisted selection (MAS). In addition, the molecular markers can be used to enhance selection accuracy such as the vertical axis, to select sex-restricted traits, and to be useful for carcass traits such as meat quality. To date, several genes or markers have been used in the swine industry. Up to now, we have used a selection index formula based on phenotype values as an existing method to select breeds. However, in recent years, the use of marker assisted selection (MAS) has been effectively utilized in traits that are controlled by quantitative trait loci (QTL) as well as traits that are controlled by a single gene, such as selection or screening of mutants using molecular markers It is confirmed and reported through simulation. The traits related to productivity, ie, economic traits, are the number of recipients, the number of reasons, the amount of daily gain, the rate of feed demand, the backfat thickness, the rate of carcass, the size of meat, the shape and durability (operability). Traits related to carcass traits and body traits such as backfat thickness are paternal lines, with a high heritability, and the heritability of backfat thickness is 40 to 55%.

However, the development of biomarkers for prediction using molecular markers is still in the early stage. Accordingly, it is an object of the present invention to provide a biomarker for predicting backfat thickness and a method for predicting the backfat thickness using the same. However, these problems are exemplary and do not limit the scope of the present invention.

The present inventors have continued efforts to develop a biomarker for predicting the back thickness of pork which can be used for marker-assisted selection (MAS) in the selection of pig breeds, and as a result, it has been found that the GRIP1 (Glutamate receptor interacting protein 1) We have confirmed that the gene is associated with missense mutation and back fat thickness (BFT).

According to one aspect of the present invention, in a polynucleotide represented by the nucleic acid sequence of SEQ ID NO: 1, a pig backgrowth thickness prediction including detection means capable of detecting a single nucleotide polymorphism (SNP) having a 205th base of G or C A kit is provided.

Wherein the detection means may be a probe capable of detecting the SNP or a pair of primers capable of amplifying the polynucleotide, wherein the primer pair comprises a forward primer represented by the nucleic acid sequence of SEQ ID NO: 2 and a forward primer represented by SEQ ID NO: And a reverse primer represented by the nucleic acid sequence of SEQ ID NO:

According to one aspect of the present invention, a genomic DNA extraction step of extracting genomic DNA from a pig;

Amplifying a genomic DNA molecule encoding GRIP1 through a PCR reaction with the genomic DNA; And

And determining whether the 205th base is G or C in the polynucleotide represented by the nucleic acid sequence of SEQ ID NO: 1.

In the above method, if the 205th base is C, a step of predicting that the backfill thickness is thick may be additionally included.

According to the embodiment of the present invention as described above, since the backfill thickness is one of the important factors for evaluating the current conductor grade, it can contribute greatly to the upbringing of the good piglets. Accordingly, the new biomarker for the diagnosis of pig back ground thickness according to the global trend in breeding is very effective as a genetic DNA marker in selection of pigs. Of course, the scope of the present invention is not limited by these effects.

FIGS. 1A to 1G are graphs showing the results of eight exon single nucleotide polymorphisms (SNPs) in the F2 hybridization group of Landrace and KNP according to an embodiment of the present invention, single nucleotide polymorphism) is compared with the nucleotide sequence disclosed in GenBank registration number XM_005663960.1 which is the reference nucleic acid sequence.
FIGS. 2A to 2C are diagrams showing the results of gene coding for the GRIP1 gene in the F2 hybrid crossbred group of Landrace and KNP according to an embodiment of the present invention. And comparing the amino acid sequence of the GRIP1 protein with the amino acid sequence disclosed in GenPept Accession No. XP_005664017.1 which is the reference amino acid sequence.
FIG. 3 is an amino acid sequence diagram showing a comparison between the amino acid sequence containing the amino acid mutation portion of the GRIP1 protein and the amino acid sequence of the GRIP1 protein of another species according to an embodiment of the present invention. This figure shows the result of multiple alignment of genes.

Hereinafter, the present invention will be described in detail with reference to Examples and Experimental Examples. It should be understood, however, that the invention is not limited to the disclosed embodiments and examples, but may be embodied in many different forms and should not be construed as limited to the exemplary embodiments set forth herein. It is provided to fully inform the scope of the invention to the person.

Example 1 Polymorphism Detection and Genotyping Analysis

In the present invention, the relationship between single base polymorphism (SNP) markers in the exon of GRIP1 and porcine backbone related traits was investigated. Using the F2 hybrid crossbreeding group of Landrace and Korean native pig (KNP) (Positional candidate gene) for evaluating the relationship between backbone thickness traits. Quantitative trait gene locus in SSC5 in the prior study affecting backfat thickness (BFT) transfected by the present inventors (quantitative trait loci; QTL) region (SW1482-SW963) confirmed that includes GRIP1 genes (Yoo et al., Mol. Biol. Rep., 39: 8327-3833, 2012).

 Genomic DNA samples of landrace (n = 5) and KNP (n = 5) used for hybridization of F2 for detection of SNP markers were cloned into the entire genome, including the GRIP1 gene (GenBank Accession No.XM_005663960.1) Sequencing technology (Illumina Inc., USA). As a result, a total of eight exon SNP markers were identified in the cDNA sequence of the porcine GRIP1 gene, and the eight SNP markers were confirmed by a pyrosequencing method (Biotage AB, Sweden) (Fig. 1). Primer sequences for pyrosequencing are shown in Table 1. One of these non-synonymous SNP markers (c.3451 C> G (G1151R), SEQ ID NO: 1) was identified (Fig. 2) and the SNP was registered with the registration number rs342221788 in the NCBI SNP database Lt; RTI ID = 0.0 > SNP. ≪ / RTI > SEQ ID NO: 1 is part of the nucleic acid sequence of exon 23 of the porcine GRIP1 gene. Therefore, the present inventors analyzed the genotyping of the SNP markers on the whole F2 acid (N = 1,105) using a pyrosequencing method. The c.3451 C> G SNP genotype and minor allele frequency are shown in Table 2.

Pyrosequencing primer set for exon sequence modification in GRIP1 gene SNP No. part Base sequence order
number size
(bp) Temperature
(° C)
One
exon23 5'-CCCTGTGCTTGCAGTTAATG-3 ' 2
331
66 5'-biotin-GACTTCGACTGCTGCCTTGTT-3 ' 3 Seq: 5'-CTATAATGTATTAGTGGGTTC-3 ' 4 *2

* 3 exon18

exon18 5'-CCCCAATGACAAATTTCCTG-3 ' 5
239
63
5'-biotin-AAGCCCAGTCAGCATCAAGT-3 ' 6 Seq: 5'-ACCTTGATTCCCATAGCTGG-3 ' 7
4

exon15
5'-TCACACTGCTGGAGGATCTG-3 ' 8
258

63
5'-biotin-ACTGGCAAACAAGGGATTTG-3 ' 9 Seq: 5'-CATGGAGCAATTGTCCAGC-3 ' 10
5

exon13
5'-CTGGCTCCACATCTCCGTAT-3 ' 11
285
63 5'-biotin-TGACTGATGCTCCATTCTTCC-3 ' 12 Seq: 5'-CACTGCTCGGGATGACAG-3 ' 13
6
exon12
5'-CTGTTGTTCCTCCCCCATC-3 ' 14 271
63
5'-biotin-CAACCTGTAACATGTGGTTCTAAA-3 ' 15 Seq: 5'-GACAGAGTGCTGGCCATTA-3 ' 16
7
exon8 5'-TTGCGAAAAGAAGGCACTTT-3 ' 17
273
63 5'-biotin-AGACCGACATCAGGCTGAAC-3 ' 18 Seq: 5'-ACTTCCACTAGGAGTGGCC-3 ' 19
8

exon4
 5'-CCTGGGCTTGTATGCTCATC-3 ' 20
295
63 5'-biotin-GGCAAAAATACGGTGCATCT-3 ' 21 Seq: 5'-AACCTGGCCAAATTCCGCC-3 ' 22 Internal Sequencing Primer
^* SNP No. 2 and 3 were screened using a single internal sequencing primer

Genotype frequency of the missense mutation of the GRIP1 gene and MAF SNP part Genotype frequency (number of animals) MAF c.3163 C > G (G1055R) exon23 CC 219
0.1786 CG 652
0.5318 GG (355)
0.2896 0.3966

Example 2: Association analysis

Four backfat traits (BF traits) were measured in more than 1,010 F2 animals (Table 3). In order to calculate familial relatedness, a mixed-effect model is used in the present invention to determine SNP genotype [c.3451 C> G (G1151R)], sex, parity, ASREML (VSN international, UK) was used to model the use of polygenic animal effects as a random and fixed effect of batch and carcass weights. The relationship between the SNP markers and back - thickness traits was analyzed. The SNP markers ^{BFT (P = 9.814 x 10 -14} ), 4 times with BFT between 5 thoracic ^{(P = 1.474 x 10 -3)} , 11 times and 12 times, between the thoracic BFT (P = 4.739 x 10 ^{- 5} ), and BFT of the last thoracic and lumbar spine (P = 2.031 x 10 ^-12 ) (Table 3). Genotypic and genotypic values and P-values of the GRIP1 gene mutation [c.3451 C> G (G1151R)] and BFT in the F2 hybridization between KNP and Landrace are shown in Table 3.

 Analysis of association between GRIP1 gene mutation and BFT characteristics ^a N Average
(SE) range ^b CC
(SE) ^b CG
(SE) ^b GG
(SE) P-value BFT (mm) ^c 1,014 22.93
(0.22) 6-48 25.00
(0.90) 23.36
(0.85) 21.56
(0.87) 9.81x10 ^-14 BFT between the 4th and 5th thoracic vertebra (mm) 1,046 34.02
(0.24) 7-60 35.23
(0.92) 34.47
(0.86) 33.36
(0.89) 1.47x10 ^-3 BFT between the 11th and 12th thoracic spine (mm) 1,046 27.99
(0.23) 5-55 29.25
(0.97) 28.42
(0.92) 27.13
(0.94) 4.74x10 ^-5 BFT of the last thoracic and lumbar spine (mm) 1,046 26.15
(0.22) 5-49 27.73
(0.89) 26.93
(0.84) 24.63
(0.87) 2.03x10 ^-12 a is the number of animals,
b is the predicted genotype average and SE,
c is back thickness = [(BFT between 11th and 12th thoracic spine) + (BFT of last thoracic spine and first lumbar spine)] / 2.

 As shown in Table 3, the GRIP1 gene [c.3451 C> G (G1151R)] is associated with missense mutation together with the isoflavy trait, especially when the genotype CC is present, I could. Finally, from a functional point of view, the change from glycine (GGA) to arginine (CGA) in exon 23 affects the function of the GRIP1 gene and appears to influence backfat thickness.

In order to evaluate the significance of the SNP markers, the present inventors used human (NP_001171545.1), horse (XP_001492766.3), dog (XP_003431500.1), and ox (XP_603945.5) as well as the amino acid sequence of KNP pig GRIP1. , And XP_003355559.1) were subjected to multiple protein sequence alignment analysis using Clustal X. As a result, as shown in Fig. 3, in the c.3451 C > G (G1151R) hypermutation, R is conserved at a high level in various other species except KNP, and thus the SNP is potentially important for the GRIP1 gene Suggesting that they will play a role.

<110> Industry-Academic Cooperation Foundation Gyeongsang University <120> Novel biomarker and method for predicting back fat traits in pigs <130> PD13-0693 <160> 22 <170> Kopatentin 2.0 <210> 1 <211> 484 <212> DNA <213> Sus scrofa <400> 1 gttaatcacg tccgaacaag agacttcgac tgctgccttg ttgtgcccct catagcagaa 60 tctggtaaca agctggacct ggtaattagc agaaatccac tggcttcaca gaagtccaca 120 gaacagactc taccgggagg agaatggagt gaacagaaca gtgctttttt ccaacagcct 180 agccatggtg gtaatttgga gatacgagaa cccactaata cattatagca atgcttttta 240 taaagcagga caaaagacaa tatctacatg gtgctaaaaa aaaaaatccc tttaagattt 300 ctgtgccatt tgatgcacag ataatcacgt ggcattaact gcaagcacag gggtctttta 360 aatctcatgg ctcatgttca cgtccctttt caagttgaag aggtttcttt gttgacgatc 420 actaaggtat atgacaggca atcccctgcc aagctcaagg gtacagaaaa aaacaaacaa 480 aaaa 484 <210> 2 <211> 20 <212> DNA <213> Artificial Sequence <220> <223> Forward Primer for GRIP1 exon 23 <400> 2 ccctgtgctt gcagttaatg 20 <210> 3 <211> 21 <212> DNA <213> Artificial Sequence <220> <223> Reverse Primer for GRIP1 exon 23 <400> 3 gacttcgact gctgccttgt t 21 <210> 4 <211> 21 <212> DNA <213> Artificial Sequence <220> <223> Primer for sequencing GRIP1 exon 23 <400> 4 ctataatgta ttagtgggtt c 21 <210> 5 <211> 20 <212> DNA <213> Artificial Sequence <220> <223> Forward Primer for GRIP1 exon 18 <400> 5 ccccaatgac aaatttcctg 20 <210> 6 <211> 20 <212> DNA <213> Artificial Sequence <220> <223> Reverse Primer for GRIP1 exon 18 <400> 6 aagcccagtc agcatcaagt 20 <210> 7 <211> 20 <212> DNA <213> Artificial Sequence <220> <223> Primer for sequencign GRIP1 exon 18 <400> 7 accttgattc ccatagctgg 20 <210> 8 <211> 20 <212> DNA <213> Artificial Sequence <220> <223> Forward Primer for GRIP1 exon 15 <400> 8 tcacactgct ggaggatctg 20 <210> 9 <211> 20 <212> DNA <213> Artificial Sequence <220> <223> Reverse Primer for GRIP1 exon 15 <400> 9 actggcaaac aagggatttg 20 <210> 10 <211> 19 <212> DNA <213> Artificial Sequence <220> <223> Primer for sequencing GRIP1 exon 15 <400> 10 catggagcaa ttgtccagc 19 <210> 11 <211> 20 <212> DNA <213> Artificial Sequence <220> <223> Forward Primer for GRIP1 exon 13 <400> 11 ctggctccac atctccgtat 20 <210> 12 <211> 21 <212> DNA <213> Artificial Sequence <220> <223> Reverse Primer for GRIP1 exon 13 <400> 12 tgactgatgc tccattcttc c 21 <210> 13 <211> 18 <212> DNA <213> Artificial Sequence <220> <223> Primer for sequencing GRIP1 exon 13 <400> 13 cactgctcgg gatgacag 18 <210> 14 <211> 19 <212> DNA <213> Artificial Sequence <220> <223> Forward Primer for GRIP1 exon 12 <400> 14 ctgttgttcc tcccccatc 19 <210> 15 <211> 24 <212> DNA <213> Artificial Sequence <220> <223> Reverse Primer for GRIP1 exon 12 <400> 15 caacctgtaa catgtggttc taaa 24 <210> 16 <211> 19 <212> DNA <213> Artificial Sequence <220> <223> Primer for sequencing GRIP1 exon 12 <400> 16 gacagagtgc tggccatta 19 <210> 17 <211> 20 <212> DNA <213> Artificial Sequence <220> <223> Forward Primer for GRIP1 exon 8 <400> 17 ttgcgaaaag aaggcacttt 20 <210> 18 <211> 20 <212> DNA <213> Artificial Sequence <220> <223> Reverse Primer for GRIP1 exon 8 <400> 18 agaccgacat caggctgaac 20 <210> 19 <211> 19 <212> DNA <213> Artificial Sequence <220> <223> Primer for sequencing GRIP1 exon 8 <400> 19 acttccacta ggagtggcc 19 <210> 20 <211> 20 <212> DNA <213> Artificial Sequence <220> <223> Reverse Primer for GRIP1 exon 4 <400> 20 cctgggcttg tatgctcatc 20 <210> 21 <211> 20 <212> DNA <213> Artificial Sequence <220> <223> Forward Primer for GRIP1 exon 4 <400> 21 ggcaaaaata cggtgcatct 20 <210> 22 <211> 19 <212> DNA <213> Artificial Sequence <220> <223> Primer for sequencing GRIP1 exon 4 <400> 22 aacctggcca aattccgcc 19

Claims (5)

1. A kit for predicting the backgrowth thickness of a pig, comprising a detection means capable of detecting a single nucleic acid polymorphism (SNP) having a 205th base of G or C, in a polynucleotide represented by the nucleic acid sequence of SEQ ID NO: 1. The method according to claim 1,
Wherein the detecting means is a pair of probes or primers specific to the polynucleotide. 3. The method of claim 2,
Wherein the primer pair comprises a forward primer represented by the nucleic acid sequence of SEQ ID NO: 2 and a reverse primer represented by the nucleic acid sequence of SEQ ID NO: 3. Genomic DNA extraction step for extracting genomic DNA from pigs;
Amplifying a genomic DNA molecule encoding GRIP1 through a PCR reaction with the genomic DNA; And
And determining whether the 205th base is G or C in the polynucleotide represented by the nucleic acid sequence of SEQ ID NO: 1. 5. The method of claim 4,
Further comprising the step of predicting that if the 205th base is C, the backfill thickness is thick.

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