KR20210081191A - Biomarker for predicting skin sensitivity risk and use thereof - Google Patents

Abstract

In one aspect of the present invention, provided are a composition for predicting a skin sensitivity risk, a microarray, and a method for providing information on a method for predicting a skin sensitivity risk of an individual using the same. According to this, not only a skin sensitivity risk of an individual can be predicted based on clear criteria, but also a personalized sensitive skin care method can be provided based on the same.

Description

Biomarker for predicting skin sensitivity risk and use thereof

A composition for predicting sensitivity risk using a biomarker for skin sensitivity risk prediction, a microarray, and a method for predicting an individual's skin sensitivity risk using the same.

The sensitivity of the skin refers to the degree of reaction of the skin to external factors such as the environment, and the sensitive skin generally refers to the skin that responds quickly and sensitively to changes in the environment.

The sensitivity of the skin varies from person to person, and it also varies depending on the type of skin commonly referred to as oily or dry. In the case of sensitive skin, symptoms such as redness, itching, and rash can occur easily, so caution is required.

Currently, the development of cosmetics for sensitive skin is mainly developed for the function of soothing the skin through cosmetics, but the effect on this is individual and if used in the wrong way, it may not penetrate into the skin or interfere with the absorption of other skin products. could be a problem. In addition, in the case of a sensitive cosmetic composition, there is only a difference in the characteristics of each product, and there is no customized product according to the skin type based on clear criteria. Although the demand for provision is rapidly increasing, research on this is insufficient, and the standards and rationale for customized solutions are not clear.

Therefore, the present inventors measure the sensitivity of about 1,000 subjects, and by encoding and analyzing the measured values, provide a clear standard for predicting the risk of skin sensitivity and a method of 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 skin sensitivity using a biomarker.

A kit or microarray for measuring skin sensitivity risk using biomarkers is provided.

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

One aspect provides a composition for measuring the risk of sensitization, comprising an agent that detects one or more single nucleotide polymorphisms (SNPs) selected from the group consisting of rs7334780 and rs41308.

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 rs7334780 may be correlated with the risk for skin sensitivity. Also, in rs7334780, the 106182099th base of the human chromosome from the 13th N-terminus may be T or C. The rs7334780 is 5 to 100, 10 to 100, 10 to 80, 10 to 60, 10 to 40, 10 to 20 consecutive DNA sequences including the base 106182099. It may be a polynucleotide consisting of or a complementary nucleotide.

The rs41308 may be correlated with the risk for skin sensitivity. The rs41308 may be present in intron 4 of the CREB5 gene. Also, in rs41308, the 28636081th base of the human chromosome from the 7th N-terminus may be T or C. The rs41308 is 5 to 100, 10 to 100, 10 to 80, 10 to 60, 10 to 40, 10 to 20 consecutive DNA sequences including the 28636081th base. It may be a polynucleotide consisting of or a complementary nucleotide.

In one embodiment, the rs7334780 or rs41308 may be one in which C or T is detected.

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

Another aspect provides a kit for determining the risk of developing a sensitivity comprising an agent that detects one or more single nucleotide polymorphisms (SNPs) selected from the group consisting of rs7334780 and rs41308.

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 sensitivity by confirming the SNP polymorphic marker through amplification, or by checking the expression level of the SNP polymorphic marker and the expression level of the mRNA. 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 measuring risk of sensitization comprising an agent that detects one or more single nucleotide polymorphisms (SNPs) selected from the group consisting of rs7334780 and rs41308.

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, 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 sensitization risk, comprising detecting one or more single nucleotide polymorphisms (SNPs) selected from the group consisting of rs7334780 and rs41308 from the biological sample.

The rs7334780 and rs41308 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.

The predicting of the skin sensitivity risk 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.

The detecting step comprises a primer comprising a nucleic acid sequence identical or complementary to a nucleic acid sequence comprising a polymorphic site of rs7334780 or a primer comprising a nucleic acid sequence identical or complementary to a nucleic acid sequence comprising a polymorphic site of rs41308. It can be carried out using a primer selected from

In the detecting step, the polynucleotide identical to or complementary to five or more consecutive nucleic acid sequences comprising a polymorphic site thereof in one or more single nucleotide polymorphisms (SNPs) selected from the group consisting of rs7334780 and rs41308 is immobilized on a microarray of the nucleic acid. hybridizing the sample; and analyzing the hybridization results.

The method may further include converting a skin sensitivity 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 sensitization includes when C is detected at a higher frequency than T in rs7334780 or when T is detected at a higher frequency than C in rs41308 or , the risk of skin sensitivity is high. it could be predictable.

In one embodiment, when the Odds Ratio (OR) value of rs7334780 or rs41308 exceeds 1, the risk is high, and when it is less than 1, the risk is predicted to be low.

The OR value may be based on the results of a case control study comparing a patient group and a control group. Also, it may mean Odds of disease occurrence in the patient group 　 Odds 　 / Odds of disease occurrence in the control group.

In the case of OR of the present invention, 1 may not be included in any group based on 1.

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 sensitivity may include deriving a conversion value by the following equation.

[Equation 1]

Converted value = 1.479+(rs7334780)*(-0.104)+(rs41308)*(0.113)

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 (rs7334780) may be rs7334780, 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.

(rs41308) may be rs41308, a value of 0 if the phenotype of T>C is TT, 1 if the phenotype is CT, and 2 if the phenotype is CC.

The method may further include determining the skin sensitivity risk of the individual as any one of a high skin sensitivity 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, in the predicting of the skin sensitivity, the predicting of the skin sensitivity predicts that the risk of skin sensitivity is high when the converted value is greater than 1.488 to 1.705 or less,

It is predicted that the skin sensitivity risk is medium when the converted value is greater than 1.384 and less than or equal to 1.488, and

When the conversion value is 0 or more and 1.384 or less, it may be predicted that the risk of skin sensitivity is low.

For example, if the genotype of rs7334780 C>T of an individual is CC and the genotype of rs41308 T>C is TC, the converted value is calculated as 1.479+(0)*(-0.104)+(1)*(0.113). It is 1.592.

Since the converted value is more than 1.488 and less than or equal to 1.705, it may be classified as 'a high risk of skin sensitivity'.

According to a method of analyzing an individual's SNP to provide a composition for predicting skin sensitivity risk according to an aspect, a microarray, and a method for predicting an individual's skin sensitivity risk using the same, the individual's skin sensitivity risk can be predicted and can be used to improve or prevent sensitivity.

1A shows the distribution of skin phenotypes according to age, and shows the oil content between the forehead (A101).
Figure 1b shows the distribution of skin phenotypes according to age, and shows the oil content of the skin of the right cheek (A102).
2A is an image showing a Manhattan plot of skin sensitivity phenotype.
2B is a graph illustrating a QQ plot of skin sensitivity based on the Manhattan plot.
Figure 3A confirms the expression regulation in skin tissue for the SNP genotype in the GWAS analysis, as a SNP in which the gene expression in the skin tissue is regulated according to the minor allele, rs308971, G> A for SYN2 is a graph showing.
Figure 3B confirms the expression regulation in skin tissue for the SNP genotype in the GWAS analysis, as a SNP in which the gene expression in the skin tissue is regulated according to the minor allele, rs9577919, C> T for TMEM255B It is a graph showing.

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, a strict quality control method was applied to select SNPs to be used in the invention and to 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 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 genetic indicator algorithm

An OR value was derived from the SNP biomarkers for each phenotype derived in Example 3 using logistic regression.

4-2. Conversion formula by phenotype

As a result, when the OR value was more than 1, the risk of skin sensitivity was high, and when the OR value was less than 1, it was shown that the risk of skin sensitivity was small. 1 is not included in any group as a reference value for comparison. This means that the statistic is significant.

In addition, as a result of calculating the weight of the skin sensitivity, as shown in Table 6, it was possible to derive a conversion formula for the skin sensitivity phenotype.

Claims (10)

A composition for predicting skin sensitivity, comprising an agent for detecting at least one single nucleotide polymorphism (SNP) selected from the group consisting of rs7334780 and rs41308. The method according to claim 1, wherein the agent is a primer, a probe, or an oligonucleotide. The composition of claim 1, wherein C or T is detected in rs7334780 or rs41308. A microarray for predicting skin sensitivity comprising an agent detecting one or more single nucleotide polymorphism (SNP) markers selected from the group consisting of rs7334780 and rs41308. obtaining a biological sample from the subject;
A method for predicting skin sensitivity, comprising detecting one or more single nucleotide polymorphisms (SNPs) selected from the group consisting of rs7334780 and rs41308 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 predicting the risk of skin sensitivity further comprises analyzing the frequency of the single nucleotide polymorphism genotype (genotype). The method according to claim 5, wherein the predicting of the skin sensitivity risk is a high risk when the Odds Ratio (OR) value of rs7334780 or rs41308 exceeds 1, and predicts that the risk is low when less than 1. The method according to claim 5, wherein predicting the skin sensitivity comprises deriving a converted value by the following equation,
[Equation 1]
Converted value = 1.479+(rs7334780)*(-0.104)+(rs41308)*(0.113)
In the above formula, (SNP) means a value of 0 if the genotype of each SNP is WILDTYPE, 1 if heterotype, and 2 if mutant type. The method according to claim 9, wherein the predicting of the skin sensitivity predicts that the skin sensitivity risk is high when the converted value is greater than 1.488 to 1.705 or less,
It is predicted that the skin sensitivity risk is medium when the converted value is greater than 1.384 and less than or equal to 1.488, and
The method of predicting that the risk of skin sensitivity is low when the converted value is 0 or more to 1.384 or less.

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