KR102297561B1 - Biomarker for predicting skin wrinkle risk and use thereof - Google Patents

Abstract

A composition for predicting the risk of wrinkle formation according to an aspect, a microarray, and a method for predicting the risk of skin wrinkles of an individual using the same are provided. According to this, it is possible to predict the risk of skin wrinkles of an individual based on clear criteria.

Description

Biomarker for predicting skin wrinkle risk and use thereof

A composition for predicting the risk of wrinkling using a biomarker for predicting the risk of skin wrinkles, a microarray, and a method of predicting the risk of skin wrinkles of an individual using the same.

Wrinkles in the skin are caused by various causes, but mainly those caused by aging such as degeneration and atrophy of elastic fibers, connective tissue fibers, and muscle fibers in the dermis are called wrinkles. At first, fine wrinkles appear, but it is common to gradually become large and deep wrinkles, and wrinkles caused by degeneration, degeneration, and atrophy of the elastic fibers or connective fibers of the dermis are usually not healed. The causes of wrinkles include intrinsic aging accompanying aging of other organs and extrinsic aging due to external factors.

Wrinkles are a natural phenomenon of aging, which cannot be completely avoided, but UV protection, the use of antioxidant cosmetics or food intake, improvement of skin nutrition, avoidance of mental or physical overwork, attention to excessive facial expressions, Through the maintenance of moisture of the skin through the cosmetic composition, it is possible to alleviate the wrinkle phenomenon of the skin. However, wrinkle-improving cosmetics that have been developed so far have been developed and marketed mainly for the supply of nutrients to the skin. In addition, with respect to the service that provides an individual customized solution for the current skin wrinkle, it is common to use only a survey or genetic test in most cases, and a more clear personalized service is lacking.

As interest in beauty increases, the demand for providing a customized solution or beauty product for alleviating or preventing wrinkle generation is rapidly increasing, but research on this is insufficient, and the standards and rationale for the customized solution are also not clear.

Therefore, the present inventors measure the wrinkle state of about 1,000 subjects and encode and analyze the measured values, thereby providing a clear standard for predicting the risk of skin wrinkling and providing a customized solution in a form that meets individual needs developed to solve the above problems. These inventions help to improve the integrated quality of life.

Provided is a composition for measuring the risk of wrinkle formation using a biomarker.

Provided is a kit for measuring the risk of wrinkle formation using a biomarker.

A method for predicting skin wrinkle risk by detecting biomarkers is provided.

One aspect provides a composition for measuring the risk of wrinkle formation, comprising an agent for detecting one or more single nucleotide polymorphisms (SNPs) selected from the group consisting of rs117381658, rs1961184, rs1929013, and rs7042102.

The polymorphic site refers to a site exhibiting single-nucleotide polymorphism in a nucleic acid sequence. The term "single nucleotide polymorphism (SNP)" refers to a genetic change or variation that exhibits a difference of one nucleotide in a nucleic acid sequence. It may be a position in which two or more allele sequences present at a frequency of about 1% or more, or about 5% or more, 2% to 4.5%, 3 to 4%, 2.5% to 3.4% in the population occur.

The rs117381658 may be correlated with the risk for skin wrinkles. rs117381658 may be one that exists downstream of the FCRL5 gene and affects genes related to the inflammatory response and NF-κB by forming significant SNP clusters around chronic inflammatory conditions. In addition, in rs117381658, the 157353684th base from the N-terminus of human chromosome 1 may be T or C. rs117381658 consists of 5 to 100, 10 to 100, 10 to 80, 10 to 60, 10 to 40, 10 to 20 consecutive DNA sequences including the 157353684 base It may be a polynucleotide or a complementary nucleotide.

The rs1961184 may be correlated with the risk for skin wrinkles. In rs1961184, base 63733371 from the N-terminus of human chromosome 10 may be T or C. rs1961184 consists of 5 to 100, 10 to 100, 10 to 80, 10 to 60, 10 to 40, 10 to 20 consecutive DNA sequences including base 63733371 It may be a polynucleotide or a complementary nucleotide.

The rs1929013 may be correlated with the risk for skin wrinkles. In rs1929013, the 244230708th base from the N-terminus of human chromosome 1 may be G or C. rs1929013 consists of 5 to 100, 10 to 100, 10 to 80, 10 to 60, 10 to 40, 10 to 20 consecutive DNA sequences including base 244230708. It may be a polynucleotide or a complementary nucleotide.

The rs7042102 may be related to the risk for skin wrinkles, and may be a mutation present downstream of the SPTLC1 gene. In addition, in rs7042102, base 92001508 from the N-terminus of human chromosome 9 may be T or C, and 5 to 100, 10 to 100, 10 to 80, 10 including the 92001508 base It may be a polynucleotide or complementary nucleotides consisting of from 60 to 60, from 10 to 40, from 10 to 20 consecutive DNA sequences.

The detecting agent may include an agent capable of amplifying a single nucleotide polymorphism. In addition, the detecting agent may be a primer, a probe, an oligonucleotide, or a combination thereof.

The term "primer" refers to a single-stranded polynucleotide capable of serving as an initiation point in the polymerization of nucleotides by a polymerase. For example, the primer can serve as an initiation point for template-directed DNA synthesis in the presence of four different nucleoside triphosphates and a polymerase at a suitable temperature and in a suitable buffer under suitable conditions. It may be a single-stranded polynucleotide. A suitable length of a primer may depend on a variety of factors, such as temperature and the use of the primer. The primer may be 15 to 30 nucleotides in length. For example, the shorter the length of the primer, the more stable a hybridization complex can be formed with the template at a low annealing temperature. The length of the primer is about 5 to about 100 nucleotides (hereinafter referred to as 'nt'), about 10 to about 80 nt, about 10 to about 60 nt, about 10 to about 50 nt, about 10 to about 40 nt, about 10 to about 30 nt, or about 10 to about 20 nt.

The term “probe” refers to an oligonucleotide capable of specifically binding to a target nucleic acid. The probe may be bound to a labeling substance. The probe can be about 50 nt to about 400 nt, about 100 nt to about 350 nt, about 150 nt to about 300 nt, or about 200 nt to about 250 nt in length.

The term "oligonucleotide" is a short DNA, RNA molecule or oligomer used in genetic testing or research. The oligonucleotide may be a single-stranded molecule having a user-specified sequence.

The probe oligonucleotide or primer may be chemically synthesized using a phosphoramidite solid support method or other well-known methods. Such nucleic acid sequences may also be modified using a number of means known in the art. Non-limiting examples of such modifications include methylation, "encapsulation", substitution of one or more homologs of natural nucleotides, and modifications between nucleotides, such as uncharged linkages (eg, methyl phosphonates, phosphotriesters, phosphoroamidates, carbamates, etc.) or charged linkages (eg phosphorothioates, phosphorodithioates, etc.).

The skin wrinkle risk refers to the risk that skin wrinkles may occur. The risk may be calculated by analyzing the genotype associated with stress, inflammation, fat metabolism, sugar metabolism, melanin synthesis, keratinogenesis, skin oil, skin moisture, facial expression, or a combination thereof.

Another aspect provides a kit for determining the risk of wrinkle formation comprising an agent detecting one or more single nucleotide polymorphisms (SNPs) selected from the group consisting of rs117381658, rs1961184, rs1929013, and rs7042102.

The kit may be an RT-PCR kit or a DNA chip kit, and the kit may further include reagents necessary for the polymerization reaction, for example, dNTPs, a polymerase, and a coloring agent.

The kit can diagnose the risk of skin wrinkles by confirming the SNP polymorphic marker through amplification or by checking the expression level of the SNP polymorphic marker and the mRNA expression level. For example, a kit for measuring the mRNA expression level of a diagnostic marker for each skin phenotype may be a kit including essential elements necessary for performing RT-PCR. In addition to each primer pair specific for the gene of a diagnostic marker for each skin phenotype, the RT-PCR kit includes a test tube or other suitable container, reaction buffer (pH and magnesium concentration vary), deoxynucleotides (dNTPs) ), enzymes such as Taq-polymerase and reverse transcriptase, DNase, RNAse inhibitors, DEPC-water, sterile water, and the like. It may also include a pair of primers specific for a gene used as a quantitative control. Also preferably, the kit may be a diagnostic kit for each skin phenotype including essential elements necessary for performing a DNA chip. A DNA chip kit is a method in which nucleic acid species are attached in a gridd array to a generally flat solid support plate, typically a glass surface no larger than a microscope slide, and the nucleic acids are uniformly arranged on the chip surface, and the DNA chip It is a tool that allows multiple hybridization reactions to occur between the nucleic acids on the chip surface and the complementary nucleic acids contained in the solution treated on the chip surface, enabling massively parallel analysis.

Another aspect provides a microarray for determining the risk of wrinkle formation comprising an agent that detects one or more single nucleotide polymorphisms (SNPs) selected from the group consisting of rs117381658, rs1961184, rs1929013, and rs7042102.

The microarray means that the polynucleotide is immobilized at a high density in a divided region on the surface of a substrate. In the microarray, the area may be arranged on a substrate at a density of, for example, 400 pieces/cm 2 or more, 103 pieces/cm 2 or more, or 104 pieces/cm 2 or more.

The polynucleotide immobilized on the microarray may be DNA or RNA. The polynucleotide may be single-stranded or double-stranded. The polynucleotide may include natural nucleotides, analogs of natural nucleotides, nucleotides in which sugar, base or phosphate sites of natural nucleotides are modified, peptide nucleic acid (PNA), and combinations thereof. The polynucleotide may have a detectable label (eg, a Cy3 or Cy5 fluorescent substance) attached to the 3'-end, the middle, or the 5'-end thereof.

The polynucleotide immobilized on the microarray has a length of about 5 to about 100 nt, about 10 to about 80 nt, about 10 to about 60 nt, about 10 to about 50 nt, about 10 to about 40 nt, about 10 to about 30 nt, or about 10 to about 20 nt.

Another aspect comprises the steps of obtaining a biological sample from a subject; It provides a method for predicting skin wrinkle risk, comprising detecting one or more single nucleotide polymorphisms (SNPs) selected from the group consisting of rs117381658, rs1961184, rs1929013 and rs7042102 from the biological sample.

The rs117381658, rs1961184, rs1929013, rs7042102, and single nucleotide polymorphisms are the same as described above.

The obtaining of the biological sample may further include obtaining a nucleic acid sample from the biological sample of the subject. The subject may be a mammal including a human. The biological sample refers to a sample obtained from a living organism. The biological sample may be, for example, tissue, urine, mucus, saliva, tears, blood, plasma, serum, sputum, spinal fluid, pleural fluid, nipple aspirate, lymph fluid, airway fluid, intestinal fluid, genitourinary fluid, breast milk, lymphatic fluid, semen , cerebrospinal fluid, intratracheal fluid, ascites, cystic tumor fluid, amniotic fluid, or a combination thereof. The step of obtaining a nucleic acid sample from the biological sample may be performed by a conventional DNA isolation method. For example, the target nucleic acid is subjected to polymerase chain reaction (PCR), ligase chain reaction (LCR), transcription amplification, or realtime-nucleic acid It can be obtained by amplifying it through sequence based amplification: NASBA) and purifying it.

The detecting may include amplifying the single nucleotide polymorphism. The agent used for the detection may be a primer, a probe, an oligonucleotide, or a combination thereof.

Predicting the risk of skin wrinkles may further include analyzing the frequency of the single nucleotide polymorphism genotype.

The detecting may be a polymerase chain reaction (PCR), a molecular beacon, a primer extension, or the like.

In the detecting step, a primer comprising a nucleic acid sequence identical or complementary to a nucleic acid sequence comprising a polymorphic site of rs117381658, a primer comprising a nucleic acid sequence identical or complementary to a nucleic acid sequence comprising a polymorphic site of rs1961184, a primer comprising a nucleic acid sequence of rs1929013 A primer comprising a nucleic acid sequence identical or complementary to a nucleic acid sequence comprising a polymorphic site, or a primer comprising a nucleic acid sequence identical or complementary to a nucleic acid sequence comprising a polymorphic site of rs7042102. Performed using a primer selected from the group consisting of: can be

In the detecting step, at least one single nucleotide polymorphism (SNP) selected from the group consisting of rs117381658, rs1961184, rs1929013 and rs7042102, 5 or more contiguous nucleic acid sequences containing a polymorphic site thereof identical to or complementary to a polynucleotide immobilized on a micro hybridizing the nucleic acid sample to an array; and analyzing the hybridization results.

The method may further include converting a skin wrinkle risk score by calculating a weight for each single nucleotide polymorphism according to the single nucleotide polymorphism detection result.

In one embodiment, predicting the risk of skin wrinkle is when C is detected with a higher frequency than T in rs117381658, rs1961184 or rs7042102, or when G is detected with a higher frequency than C in rs1929013, the risk of skin wrinkles may be predicted to be high.

In one embodiment, the predicting of the risk of skin wrinkle may include predicting that the risk is high when the regression analysis beta of rs117381658, rs1961184, rs1929013 or rs7042102 is positive, and that when it is negative, the risk is low. .

The regression analysis beta value may represent the resulting coefficient. The phenotype of the SNP allele may represent the result of regression analysis, including coding 0 for wildtype, 1 for heterotype, and 2 for mutant type. More specifically, the beta value of the regression analysis may mean how much a characteristic of an individual changes when the number of minor alleles is increased.

In an embodiment, predicting the risk of skin wrinkles may include deriving a conversion value by the following equation.

[Equation 1]

Scale = 7.45+(rs117381658)*(0.857)+(rs1961184)(0.699)*(rs1929013)(-0.339)+(rs7042102)*(0.323).

In the above equation, (SNP) means a value of 0 if the genotype of each SNP is WILDTYPE, 1 if it is a heterotype, and 2 if it is a mutant type.

The wild type may refer to a phenotype composed of major alleles of a gene, the heterotype may refer to a phenotype composed of major and minor alleles of a gene, and the mutant type may refer to a phenotype composed of minor alleles of a gene.

The (rs117381658) may be rs117381658, a value of 0 if the phenotype of C>T is CC, 1 if the phenotype is CT, and 2 if the phenotype is TT.

(rs1961184) may have a value of 0 if the phenotype of rs1961184 G>T is GG, 1 if GT, and 2 if TT.

(rs1929013) may have a value of 0 if rs1929013, C>G phenotype is CC, 1 if CG, and 2 if GG.

The above (rs7042102) may be rs7042102, a value of 0 if the phenotype of C>T is CC, 1 if the phenotype is CT, and 2 if the phenotype is TT.

The method may further include determining the skin wrinkle risk of the individual as any one of a high risk group, an intermediate group, and a low group according to the converted value. The higher the conversion value, the higher the risk of the inspection item may be determined.

In one embodiment, the predicting of the risk of skin wrinkles predicts that the risk of skin wrinkles is high when the converted value is greater than 7.773 to 10.509 or less,

If the converted value is more than 7.434 to 7.773 or less, it is predicted that the risk of skin wrinkles is medium, and

When the converted value is 0 or more to 7.434 or less, it may be predicted that the risk of skin wrinkles is low.

For example, if the genotype of rs1173881658 C>T of an individual is CC, the genotype of rs1961184 G>T is TT, the genotype of rs1929013 C>G is GG, and the genotype of rs7042102 C>T is TT, the converted value is 7.45+( It is calculated as 0)*(0.857)+(2)*(0.699)+(2)*(-0.339)+(2)*(0.323), which is 8.816.

Since the conversion value is more than 7.773 and less than or equal to 10.509, it may be classified as 'a high risk of skin wrinkles' with respect to wrinkles.

According to a method of analyzing an individual's SNP to provide a composition, a microarray for predicting the risk of wrinkling according to an aspect, and a method of predicting the risk of skin wrinkling of an individual using the same, the risk of skin wrinkle of an individual can be predicted and it is possible to improve or prevent wrinkles by using it.

1A shows the distribution of skin phenotypes according to age, and shows the average roughness of the skin around the right eye (W101).
1B shows the distribution of skin phenotypes according to age, and shows the maximum depth of wrinkles in the skin around the right eye (W102).
1C shows the distribution of skin phenotypes according to age, and shows the average roughness of the skin between the forehead (A101).
1D shows the distribution of skin phenotypes according to age, and shows the maximum depth of wrinkles in the skin between the glabellar areas (A102).
1E shows the distribution of skin phenotypes according to age, and shows the average roughness of the skin around the right eye (W101).
1f shows the distribution of skin phenotypes according to age, and shows the maximum depth of wrinkles in the skin around the right eye (W102).
1g shows the distribution of skin phenotypes according to age, and shows the average roughness of the skin between the forehead (A101).
1h shows the distribution of skin phenotypes according to age, and shows the maximum depth of wrinkles in the skin between the forehead (A102).
2A is an image showing a Manhattan plot of skin folds.
2B is a graph illustrating a QQ plot of skin wrinkles based on the Manhattan plot.
3 is a graph showing rs7042102 C> SPTLC1 as a SNP in which gene expression in skin tissue is regulated according to a minor allele, confirming the expression regulation in skin tissue for the SNP genotype in GWAS analysis.

Hereinafter, the present invention will be described in more detail through examples. However, these examples are for illustrative purposes of the present invention, and the scope of the present invention is not limited to these examples.

Example 1. Conversion of skin wrinkle measurement index

1-1. sample population

The sample population consisted of a total of 1,079 Korean women recruited from P&K Skin Research Center (Seoul) from January 2019 to November 2019. All participants had no skin-related diseases and the mean age was 40.81 years. All participants gave written informed consent to the study. The population was approved by the institutional review board of Theragen Etex Bio Institute (IRB No.: 700062-20190819-GP-006-01).

1-2. Derivation of measurement values for each skin phenotype using skin measurement equipment

To measure skin properties, various measuring devices were used.

For skin wrinkles, the average roughness of the skin around the eyes, the maximum wrinkle depth of the skin around the eyes, the average roughness of the skin between the eyebrows, and the maximum wrinkle depth of the skin between the eyes were measured using a Primos CR (Canfield Scientific, Parsippany, NJ, USA) device.

For skin moisture, the moisture content of the glabellar and right cheek skin was measured using a Corneometer® CM-825 (EnviroDerm Services Ltd., Hedworth, Grange Court, UK) device.

For pigmentation, melatonin and skin brightness were measured using a Mexameter and a CM-700d device, respectively .

For skin oil content, the oil content of the glabellar and right cheek skin was measured using a Sebumeter® SM 815 (Courage+Khazaka electronic GmbH., Kφln, Germany) device.

In the case of skin sensitivity, the skin was treated with 10% (v/v) lactic acid without a measuring device and measured according to the degree of reaction.

As a result, it was possible to obtain raw data of skin measurements of the sample population.

In addition, as shown in Table 1, based on the skin measurement data, the name of the variable for each skin measurement value, the measurement site, the description of the variable value, etc. were compiled into a codebook.

1-3. Conversion of metrics

In the case of measurement by phenotype according to 1-2 above, since different measurement equipment was used for each phenotype, it was necessary to uniformly convert each measurement value before performing GWAS analysis.

Therefore, the present inventors assigned a code to each measurement item, divided into three groups 1, 2, and 3 according to the size of each measurement value, and gave a score of 1 point, 2 points, and 3 points to each group.

As a result, as shown in Table 2, codes were assigned to each measurement site.

In addition, as shown in Table 3, each code was divided into an upper group, a middle group, and a lower group, and 3 points were sequentially assigned to the upper group and 1 point to the lower group for quantification.

As a result of quantification, in the case of skin wrinkles, a total of 4 to 12 points were converted according to the measured values, and 4 to 6 points were classified into a low group, 7 to 9 points into a middle group, and 10 to 12 points into a high group. The measurements could be quantified.

As a result of quantification, in the case of skin moisturizing, a total of 2 to 6 points were converted according to the measured value, and 2 points were classified into a low group, 3 to 5 points into a middle group, and 6 points into a high group to quantify the measured values. Could.

As a result of quantification, in the case of skin pigmentation, a total of 4 to 12 points were converted according to the measured values, and 4 to 6 points were classified into a low group, 7 to 8 points into a middle group, and 9 to 12 points into a high group. The measurements could be quantified.

As a result of quantification, in the case of skin oil, a total of 2 to 6 points were converted according to the measured values, 2 points were classified into a low group, 3 to 4 points were classified as a middle group, and 5 to 6 points were classified into a high group. could be quantified.

As a result of quantification, in the case of skin sensitivity, a total of 1 or 2 points were converted according to the measured value, and 1 point was classified as a low group and 2 points as a middle group, so that the measured value could be quantified.

1-4. Measuring distribution by age

In addition, the present inventors confirmed the distribution of measurements of each code in order to understand the trend of skin phenotype change according to age.

As a result, it was found that the skin phenotype showing the difference in age-related changes was wrinkles and skin pigmentation. More specifically, as shown in FIGS. 1A to 1H , in the skin wrinkle phenotype, 'mean roughness of skin around the eyes (code: W101)' and 'maximum depth of wrinkle around the eyes (code: W102)' increased with age, and the difference between the glabellar The average roughness and the maximum depth of the glabellar wrinkle showed similar trends (codes: W103, W104). In the case of skin pigmentation, there was a significant difference in age between the 'skin brightness pigmentation point (code: R201)' and the 'non-skin brightness pigmentation point (code: R202)'. Also, there was a significant correlation between the oil content of the forehead and cheeks, which decreased with age. On the other hand, in the case of skin moisture content, the individual variance was larger than the age-related difference, and although pigmentation also tends to increase with age, it was confirmed that the individual variance was also large.

2. Obesity risk analysis method through genotyping

2-1. Preparation for screening of genetic polymorphic markers

In order to select a genetic polymorphism marker for the skin phenotype measured and quantified in Example 1, the present inventors obtained a sample from the oral cavity of the sample population with a cotton swab, and DNA using ExgeneTM Tissue SV (GeneAll, Seoul, Korea) was detected. All DNA samples were amplified into 25-125 bp fragments, randomly fractionated, and the DNA was collected from the Theragen Precision Medicine Research Array (Theragen PMRA), a customized analysis based on the Asian Precision Medicine Research Array (Thermofisher Scientific, Waltham, Massachusetts, USA). assay) were sequentially purified, resuspended and hybridized. After hybridization, the DMA was washed under stringent conditions to remove background to minimize noise. Next, 820,000 SNPs were analyzed using Thaeragen PMRA analysis as instructed. In order to reduce errors that may occur due to association, strict quality control methods were applied to select SNPs to be used in the invention and control the data set. In addition, quality control procedures were performed for 820,000 SNPs. The SNP set was filtered based on the genotype call rates (≥ 0.95) and MAF (≥ 0.10), and Hardy-Weinberg equilibrium (HWE) was calculated for each SNP.

As a result, all SNPs showed HWE p values > 0.01, and after the filtering, 560,795 polymorphic SNPs in chromosomes 1-22 were analyzed.

2-2. GWAS analysis of skin phenotypes

Genome-wide association study (GWAS) analysis was performed on the sample population. As a result, a total of 23 SNPs were selected, and the SNPs were stored in a reference database (KRGDB, http://coda.nih go.kr/coda/KRGDB/index.jsp; Ensembl DB, https://asia.ensembl.org) ^{Significant p-values (P<1.0x10 -5} ) were shown by GWAS analysis using the skin phenotype ( FIG. 2 ) consistent with the minor allele frequency (MAF) of .

As a result, as shown in FIGS. 2A to 2B , Manhattan plots and graphs for SNP analysis were derived.

More specifically, in the skin wrinkle phenotype, we found four SNPs: rs117381658, rs1961184, rs1929013 and rs7042102, among which rs117381658 had the highest correlation with the skin phenotypic change for wrinkles (β = 0.952, P = 1.52). x10-8), and 5 SNPs near ±100kb showed a significant correlation of P<0.05 in GWAS analysis.

In addition, six SNPs were found in the skin pigmentation phenotype. In particular, rs74653330 showed a high significance (β = -1.092, P = 1.04x10-8) correlation in the GWAS data, and 6 SNPs with P < 0.05 corresponding to the SNP cluster were also found.

In the water content phenotype, 6 SNPs were identified, among which rs9873353 showed a high correlation (β = -0.567, P = 1.47x10-6).

We also found 5 SNPs in the oil content phenotype and 2 SNPs in skin sensitivity. rs308971 (β = -0.325, P = 4.60x10-6) and rs7334780 (OR = 0.635, P = 2.82x10-6) were found to be associated with oil content and skin sensitivity, respectively.

2-3. Skin Tissue eQTL for SNPs on GTEx Portal

As a result of searching the eQTL database (GTEx Portal, https://gtexportal.org/) for SNPs related to skin phenotypes discovered through GWAS analysis, it was confirmed that 5 SNPs with different expression levels according to the genotype of the skin tissue were found. . This included rs7042102 C>T, rs34466224 G>A, rs4653497 T>C, rs308971 G>A and rs9577919 C>T.

As shown in Figure 3, in the skin tissue not exposed to the sun, rs7042102 C>T (P = 5.2x10-12), rs34466224 G>A (P = 1.1x10-7) and rs308971 G>A (P = 1.0) x10-13) showed significant genotype-dependent expression differences, and rs4653497 T > C (P = 1.7x10-4) and rs9577919 C > T (P = 4.9x10-5) were found in non-sun-exposed skin tissues. It was confirmed that the expression level of the genotype was different.

The eQTL expression level for each SNP was gradually changed according to the minor allele homozygous genotype. The expression of rs7042102 C> T, rs308971 G> A and rs9577919 C> T increased slowly, and the expression of rs4653497 T> C decreased slowly. rs34466224 G > A appears to be statistically significant because there is a difference in expression change depending on the genotype, although there is a difference.

2-4. Identification of SNP genes

To identify the gene corresponding to the highest p-value of SNP association in each analysis, SNP locus data were obtained from the UCSC Genome Browser (Genome Bioinformatics Group, UCSC, USA).

Gene annotations from the UCSC database and the GTEx database (GTEx Analysis Release v.8, http://www.gtexportal.org/) were also used to predict the effect of a mutation on the transcriptome.

As a result, as shown in Table 5 below, as a result of GWAS analysis, it was possible to obtain SNPs and their information related to each phenotype. SNP cluster is the number of SNPs satisfying P<0.05 in ±100 kb around the SNP that showed the highest signal in GWAS analysis.

2-5. statistical analysis

As shown in Examples 1-3 above, scores were quantitatively assigned to each skin phenotype, and a total score was calculated by integrating the quantitative scores to classify groups. Next, association analysis was performed by linear regression between the total score for 'target phenotype of GWAS' and genetic variation, and the results of this analysis were adjusted according to age. Most statistical analyzes were performed using PLINK version 1.9 and the SPSS program. P-values were not adjusted for multiple tests. Statistical significance was determined based on P <1.0 x 10 ^{-5 .}

3. Genetic polymorphisms by skin phenotype

3-1. Skin wrinkle-related gene polymorphism

In relation to skin wrinkles, single nucleotide polymorphisms (SNPs) of rs117381658, rs1961184, rs1929013, and rs7042102 were selected, and the selected single nucleotide polymorphisms are shown in Table 4 below.

Aging accumulates B-cell receptors and increases the risk of inducing chronic inflammation through them, which is known to induce the transcription of FCRL5, which triggers an inflammatory response (Damdinsuren et al. 2016).

Consequently, in the case of rs117381658, it exists downstream of the FCRL5 gene and could affect FCRL5 by forming a significant SNP cluster around chronic inflammatory conditions. In addition, the expression of FCRL5 can affect the inflammatory response and the NF-κb pathway, suggesting that it can destroy tissue constancy modulators and affect skin aging.

In addition, another SNP, rs7042102, is a mutation present downstream of the SPTLC1 gene, and it was confirmed that the expression difference according to the genotype of the skin tissue appeared in the eQTL database.

3-2. Gene polymorphism related to skin moisture

Regarding skin moisturizing, single-nucleotide polymorphisms of rs9873353, rs34567709, rs1362404, rs7853290, and rs143938096 were selected, and the selected single-nucleotide polymorphisms are shown in Table 4 below.

The internal factors related to skin moisture content are moisture content due to oil in the stratum corneum, natural moisturizing factors, and external factors, and it is known that differences appear due to the above factors (Iizaka 2017).

According to Table 4, six SNPs of rs9873353, rs34567709, rs1362404, rs7853290, rs143938096 and rs12955989 that are related to skin moisture content were identified. These SNPs are suggested to be associated with the CEMIP2 (TMEM2) and CTSH genes, respectively.

SNP-dependent expression differences in the eQTL database show that only adipose tissue is presented to single-tissue eQTLs, whereas multi-tissue eQTLs show that differences in expression of CEMIP2 by genotype of rs7853290 are also expressed in skin tissues. These results suggest that the SNPs can moisturize and functionally control the barrier.

3-3. Skin pigment-related gene polymorphisms

Regarding skin pigmentation, single-nucleotide polymorphisms of rs74653330, rs34466224, rs11685354, rs4653497, rs59784607, and rs76548385 were selected, and are shown in Table 4 below.

As a result, the present inventors discovered the key gene OCA2 as well as the candidate genes TSN1, RUFY4, NCLN and CDC42BPA.

In particular, the amino acid substitution (His615Arg) in Asian races is highly related to skin whitening and pigmentation changes, and the rs74653330 is one of the missense mutations (Ala481Thr), which was associated with pigmentation in the East Asian population.

3-4. Skin oil-related gene polymorphism

Regarding the skin oil content, the single base polymorphisms of rs308971, rs151209785, rs9577919, rs147804495, rs8107564 and rs6490805 were analyzed and are shown in Table 4 below.

The potential functions of rs308971, rs9577919, rs8107564 and rs6490805 can change the skin. More specifically, rs9577919 was located in intron 1 of the GAS6 gene, a gene that mediates the inflammatory response and influences the development of psoriasis. In addition, rs8107564 was located downstream of the INSR gene. In addition, the analysis results of rs308971 and rs6490805 also suggested a correlation with the skin oil content.

In addition, as shown in Table 3, each code was divided into an upper group, a middle group, and a lower group, and 3 points were sequentially assigned to the upper group and 1 point to the lower group for quantification.

As a result of quantification, in the case of skin wrinkles, a total of 4 to 12 points were converted according to the measured values, and 4 to 6 points were classified into a low group, 7 to 9 points into a middle group, and 10 to 12 points into a high group. The measurements could be quantified.

As a result of quantification, in the case of skin moisturizing, a total of 2 to 6 points were converted according to the measured value, and 2 points were classified into a low group, 3 to 5 points into a middle group, and 6 points into a high group to quantify the measured values. Could.

As a result of quantification, in the case of skin pigmentation, a total of 4 to 12 points were converted according to the measured values, and 4 to 6 points were classified into a low group, 7 to 8 points into a middle group, and 9 to 12 points into a high group. The measurements could be quantified.

As a result of quantification, in the case of skin oil, a total of 2 to 6 points were converted according to the measured values, 2 points were classified into a low group, 3 to 4 points were classified as a middle group, and 5 to 6 points were classified into a high group. could be quantified.

As a result of quantification, in the case of skin sensitivity, a total of 1 or 2 points were converted according to the measured value, and 1 point was classified as a low group and 2 points as a middle group, so that the measured value could be quantified.

1-4. Measuring distribution by age

In addition, the present inventors confirmed the distribution of measurements of each code in order to understand the trend of skin phenotype change according to age.

As a result, it was found that the skin phenotype showing the difference in age-related changes was wrinkles and skin pigmentation. More specifically, as shown in FIGS. 1A to 1H , in the skin wrinkle phenotype, 'mean roughness of skin around the eyes (code: W101)' and 'maximum depth of wrinkle around the eyes (code: W102)' increased with age, and the difference between the glabellar The average roughness and the maximum depth of the glabellar wrinkle showed similar trends (codes: W103, W104). In the case of skin pigmentation, there was a significant difference in age between the 'skin brightness pigmentation point (code: R201)' and the 'non-skin brightness pigmentation point (code: R202)'. Also, there was a significant correlation between the oil content of the forehead and cheeks, which decreased with age. On the other hand, in the case of skin moisture content, the individual variance was larger than the age-related difference, and although pigmentation also tends to increase with age, it was confirmed that the individual variance was also large.

2. Obesity risk analysis method through genotyping

2-1. Preparation for screening of genetic polymorphic markers

In order to select a genetic polymorphism marker for the skin phenotype measured and quantified in Example 1, the present inventors obtained a sample from the oral cavity of the sample population with a cotton swab, and DNA using ExgeneTM Tissue SV (GeneAll, Seoul, Korea) was detected. All DNA samples were amplified into 25-125 bp fragments, randomly fractionated, and the DNA was collected from the Theragen Precision Medicine Research Array (Theragen PMRA), a customized analysis based on the Asian Precision Medicine Research Array (Thermofisher Scientific, Waltham, Massachusetts, USA). assay) were sequentially purified, resuspended and hybridized. After hybridization, the DMA was washed under stringent conditions to remove background to minimize noise. Next, 820,000 SNPs were analyzed using Thaeragen PMRA analysis as instructed. In order to reduce errors that may occur due to association, strict quality control methods were applied to select SNPs to be used in the invention and control the data set. In addition, quality control procedures were performed for 820,000 SNPs. The SNP set was filtered based on the genotype call rates (≥ 0.95) and MAF (≥ 0.10), and Hardy-Weinberg equilibrium (HWE) was calculated for each SNP.

As a result, all SNPs showed HWE p values > 0.01, and after the filtering, 560,795 polymorphic SNPs in chromosomes 1-22 were analyzed.

2-2. GWAS analysis of skin phenotypes

Genome-wide association study (GWAS) analysis was performed on the sample population. As a result, a total of 23 SNPs were selected, and the SNPs were stored in a reference database (KRGDB, http://coda.nih go.kr/coda/KRGDB/index.jsp; Ensembl DB, https://asia.ensembl.org) ^{Significant p-values (P<1.0x10 -5} ) were shown by GWAS analysis using the skin phenotype ( FIG. 2 ) consistent with the minor allele frequency (MAF) of .

As a result, as shown in FIGS. 2A to 2B , Manhattan plots and graphs for SNP analysis were derived.

More specifically, in the skin wrinkle phenotype, we found four SNPs: rs117381658, rs1961184, rs1929013 and rs7042102, among which rs117381658 had the highest correlation with the skin phenotypic change for wrinkles (β = 0.952, P = 1.52). x10-8), and 5 SNPs near ±100kb showed a significant correlation of P<0.05 in GWAS analysis.

In addition, six SNPs were found in the skin pigmentation phenotype. In particular, rs74653330 showed a high significance (β = -1.092, P = 1.04x10-8) correlation in the GWAS data, and 6 SNPs with P < 0.05 corresponding to the SNP cluster were also found.

In the water content phenotype, 6 SNPs were identified, among which rs9873353 showed a high correlation (β = -0.567, P = 1.47x10-6).

We also found 5 SNPs in the oil content phenotype and 2 SNPs in skin sensitivity. rs308971 (β = -0.325, P = 4.60x10-6) and rs7334780 (OR = 0.635, P = 2.82x10-6) were found to be associated with oil content and skin sensitivity, respectively.

2-3. Skin Tissue eQTL for SNPs on GTEx Portal

As a result of searching the eQTL database (GTEx Portal, https://gtexportal.org/) for SNPs related to skin phenotypes discovered through GWAS analysis, it was confirmed that 5 SNPs with different expression levels according to the genotype of the skin tissue were found. . This included rs7042102 C>T, rs34466224 G>A, rs4653497 T>C, rs308971 G>A and rs9577919 C>T.

As shown in Figure 3, in the skin tissue not exposed to the sun, rs7042102 C>T (P = 5.2x10-12), rs34466224 G>A (P = 1.1x10-7) and rs308971 G>A (P = 1.0) x10-13) showed significant genotype-dependent expression differences, and rs4653497 T > C (P = 1.7x10-4) and rs9577919 C > T (P = 4.9x10-5) were found in non-sun-exposed skin tissues. It was confirmed that the expression level of the genotype was different.

The eQTL expression level for each SNP was gradually changed according to the minor allele homozygous genotype. The expression of rs7042102 C> T, rs308971 G> A and rs9577919 C> T increased slowly, and the expression of rs4653497 T> C decreased slowly. rs34466224 G > A appears to be statistically significant because there is a difference in expression change depending on the genotype, although there is a difference.

2-4. Identification of SNP genes

To identify the gene corresponding to the highest p-value of SNP association in each analysis, SNP locus data were obtained from the UCSC Genome Browser (Genome Bioinformatics Group, UCSC, USA).

Gene annotations from the UCSC database and the GTEx database (GTEx Analysis Release v.8, http://www.gtexportal.org/) were also used to predict the effect of a mutation on the transcriptome.

As a result, as shown in Table 5 below, as a result of GWAS analysis, it was possible to obtain SNPs and their information related to each phenotype. SNP cluster is the number of SNPs satisfying P<0.05 in ±100 kb around the SNP that showed the highest signal in GWAS analysis.

2-5. statistical analysis

As shown in Examples 1-3 above, scores were quantitatively assigned to each skin phenotype, and a total score was calculated by integrating the quantitative scores to classify groups. Next, association analysis was performed by linear regression between the total score for 'target phenotype of GWAS' and genetic variation, and the results of this analysis were adjusted according to age. Most statistical analyzes were performed using PLINK version 1.9 and the SPSS program. P-values were not adjusted for multiple tests. Statistical significance was determined based on P <1.0 x 10 ^{-5 .}

3. Genetic polymorphisms by skin phenotype

3-1. Skin wrinkle-related gene polymorphism

In relation to skin wrinkles, single nucleotide polymorphisms (SNPs) of rs117381658, rs1961184, rs1929013, and rs7042102 were selected, and the selected single nucleotide polymorphisms are shown in Table 4 below.

Aging accumulates B-cell receptors and increases the risk of inducing chronic inflammation through them, which is known to induce the transcription of FCRL5, which triggers an inflammatory response (Damdinsuren et al. 2016).

Consequently, in the case of rs117381658, it exists downstream of the FCRL5 gene and could affect FCRL5 by forming a significant SNP cluster around chronic inflammatory conditions. In addition, the expression of FCRL5 can affect the inflammatory response and the NF-κb pathway, suggesting that it can destroy tissue constancy modulators and affect skin aging.

In addition, another SNP, rs7042102, is a mutation present downstream of the SPTLC1 gene, and it was confirmed that the expression difference according to the genotype of the skin tissue appeared in the eQTL database.

3-2. Gene polymorphism related to skin moisture

Regarding skin moisturizing, single-nucleotide polymorphisms of rs9873353, rs34567709, rs1362404, rs7853290, and rs143938096 were selected, and the selected single-nucleotide polymorphisms are shown in Table 4 below.

The internal factors related to skin moisture content are moisture content due to oil in the stratum corneum, natural moisturizing factors, and external factors, and it is known that differences appear due to the above factors (Iizaka 2017).

According to Table 4, six SNPs of rs9873353, rs34567709, rs1362404, rs7853290, rs143938096 and rs12955989 that are related to skin moisture content were identified. These SNPs are suggested to be associated with the CEMIP2 (TMEM2) and CTSH genes, respectively.

SNP-dependent expression differences in the eQTL database show that only adipose tissue is presented to single-tissue eQTLs, whereas multi-tissue eQTLs show that differences in expression of CEMIP2 by genotype of rs7853290 are also expressed in skin tissues. These results suggest that the SNPs can moisturize and functionally control the barrier.

3-3. Skin pigment-related gene polymorphisms

Regarding skin pigmentation, single-nucleotide polymorphisms of rs74653330, rs34466224, rs11685354, rs4653497, rs59784607, and rs76548385 were selected, and are shown in Table 4 below.

As a result, the present inventors discovered the key gene OCA2 as well as the candidate genes TSN1, RUFY4, NCLN and CDC42BPA.

In particular, the amino acid substitution (His615Arg) in Asian races is highly related to skin whitening and pigmentation changes, and the rs74653330 is one of the missense mutations (Ala481Thr), which was associated with pigmentation in the East Asian population.

3-4. Skin oil-related gene polymorphism

Regarding the skin oil content, the single base polymorphisms of rs308971, rs151209785, rs9577919, rs147804495, rs8107564 and rs6490805 were analyzed and are shown in Table 4 below.

The potential functions of rs308971, rs9577919, rs8107564 and rs6490805 can change the skin. More specifically, rs9577919 was located in intron 1 of the GAS6 gene, a gene that mediates the inflammatory response and influences the development of psoriasis. In addition, rs8107564 was located downstream of the INSR gene. In addition, the analysis results of rs308971 and rs6490805 also suggested a correlation with the skin oil content.

3-5. Skin sensitization-related gene polymorphisms

Regarding skin sensitivity, single-nucleotide polymorphisms of rs7334780 and rs41308 were selected, and the selected SNPs are shown in Table 5 below.

Example 4. Derivation of a genetic polymorphism conversion formula for each skin phenotype

4-1. Skin phenotype prediction user index algorithm

SNP biomarkers for each phenotype derived in Example 3 were subjected to linear regression analysis using the SPSS statistical program. In the linear correlation analysis, the dependent variable is the phenotype of each skin, and the independent variable is the SNP marker of each phenotype, the age of the individual, and the sex. Weight constants for each phenotype marker were derived through the plastic regression analysis.

4-2. Conversion formula by phenotype

As a result, as shown in Table 6, it was possible to derive a conversion formula for the skin wrinkle phenotype.

Claims (10)

rs117381658, rs1961184, rs1929013, and rs7042102 A composition for measuring the risk of wrinkle formation, comprising an agent for detecting one or more single nucleotide polymorphisms (SNPs) selected from the group consisting of. The composition of claim 1, wherein the agent is a primer, a probe, or an oligonucleotide. The composition according to claim 1, wherein rs117381658, rs1961184 and rs7042102 is C or T is detected, and rs1929013 is G or C is detected. rs117381658, rs1961184, rs1929013 and rs7042102 A microarray for measuring the risk of wrinkle generation, comprising an agent detecting one or more single nucleotide polymorphism (SNP) markers selected from the group consisting of. obtaining a biological sample isolated from the subject;
A method of predicting skin wrinkle risk, comprising detecting one or more single nucleotide polymorphisms (SNPs) selected from the group consisting of rs117381658, rs1961184, rs1929013 and rs7042102 from the biological sample. The method of claim 5 , wherein the detecting comprises amplifying the single nucleotide polymorphism. The method according to claim 5, wherein the method of predicting the risk of skin wrinkles further comprises analyzing the frequency of the single nucleotide polymorphism (SNP). The method according to claim 5, wherein the method of predicting the risk of skin wrinkles further comprises the step of predicting that the risk is high when the regression analysis beta of rs117381658, rs1961184, rs1929013 or rs7042102 is positive, and the risk is low when negative. how to do it. The method of claim 5, wherein the method of predicting the risk of skin wrinkles comprises deriving a converted value by the following equation.
[Equation 1]
Converted = 7.45+(rs117381658)*(0.857)+(rs1961184)(0.699)*(rs1929013)(-0.339)+(rs7042102)*(0.323).
In the above formula, rs117381658, rs1961184, rs1929013 or rs7042102 is 0 if the genotype of each SNP is WILDTYPE, 1 if heterotype, and 2 if mutant type, the method. The method according to claim 9, wherein the step of deriving the converted value predicts that the risk of skin wrinkles is high when the converted value is greater than 7.773 to 10.509 or less,
If the converted value is more than 7.434 to 7.773 or less, it is predicted that the risk of skin wrinkles is medium, and
The method of predicting that the risk of skin wrinkles is low when the converted value is 0 or more to 7.434 or less.

Images