A Genome-Wide Association Study and Machine-Learning Algorithm Analysis on the Prediction of Facial Phenotypes by Genotypes in Korean Women

Abstract

Purpose

Changes in facial appearance are affected by various intrinsic and extrinsic factors, which vary from person to person. Therefore, each person needs to determine their skin condition accurately to care for their skin accordingly. Recently, genetic identification by skin-related phenotypes has become possible using genome-wide association studies (GWAS) and machine-learning algorithms. However, because most GWAS have focused on populations with American or European skin pigmentation, large-scale GWAS are needed for Asian populations. This study aimed to evaluate the correlation of facial phenotypes with candidate single-nucleotide polymorphisms (SNPs) to predict phenotype from genotype using machine learning.

Materials and Methods

A total of 749 Korean women aged 30–50 years were enrolled in this study and evaluated for five facial phenotypes (melanin, gloss, hydration, wrinkle, and elasticity). To find highly related SNPs with each phenotype, GWAS analysis was used. In addition, phenotype prediction was performed using three machine-learning algorithms (linear, ridge, and linear support vector regressions) using five-fold cross-validation.

Results

Using GWAS analysis, we found 46 novel highly associated SNPs (p < 1×10⁻⁰⁵): 3, 20, 12, 6, and 5 SNPs for melanin, gloss, hydration, wrinkle, and elasticity, respectively. On comparing the performance of each model based on phenotypes using five-fold cross-validation, the ridge regression model showed the highest accuracy (r² = 0.6422–0.7266) in all skin traits. Therefore, the optimal solution for personal skin diagnosis using GWAS was with the ridge regression model.

Conclusion

The proposed facial phenotype prediction model in this study provided the optimal solution for accurately predicting the skin condition of an individual by identifying genotype information of target characteristics and machine-learning methods. This model has potential utility for the development of customized cosmetics.

Introduction

The skin of Korean women is characterized by decreased moisture, elasticity, and skin gloss, with increased wrinkle depth and hyperpigmentation spots due to aging and external factors.¹ Skin aging and changes are caused by a combination of intrinsic and extrinsic factors, which include increases in skin aging genes, lifestyle (eg, sleep restrictions and fatigue), and environmental exposure (eg, seasonal changes, airborne pollutants, and ultraviolet [UV] light). More recently, alongside resident skin microbiome diversity, skin irritation and changes from prolonged use of face masks (Korea Filter 94 mask) because of coronavirus disease 2019 (COVID-19) have emerged as aging factors.^2–11 Because of individual differences in degree of aging and skin condition, there have been increases in the development of personalized cosmetic technology, such as three-dimensional parametric facial masks and skin diagnosis methods.¹² The skin diagnosis methods for customized cosmetics involve lifestyle questionnaires, clinical evaluations using noninvasive devices, self-diagnosis equipment that converges technology and artificial intelligence (AI), and direct-to-customer genetic tests. Of these, skin condition and degree of aging of an individual can only be predicted using the following three methods: individual genetic analysis, lifestyle questionnaire, and the AI machine-learning algorithm.

Potential causative genes for specific skin-related phenotypes have been identified using genome-wide association studies (GWAS) and machine-learning algorithms.^13–15 There are millions of single-nucleotide polymorphisms (SNPs) in each human genome.¹⁶ GWAS identify SNPs in a population that mark genomic regions that are strongly associated with several complex phenotypes.¹⁷ These genomic regions must contain the variant that is causally associated with the phenotype; however, the SNP that is identified by the GWAS is not necessarily directly causal. Therefore, most SNPs are located in noncoding regions and, thus, are indirectly involved in their phenotype association.¹⁸

In several GWAS on skin phenotypes, widely known genes, such as ASIP, MC1R, and TYR, have been shown to be involved with pigmentation traits, such as hyperpigmentation spots and skin color.^19–22 In a recent study, novel genomic loci were shown to be related to facial pigmented spots in a Korean population.²³ However, most large-scale GWAS have been conducted in individuals with European ancestry, and there are few genetic-based studies that have investigated diverse skin phenotypes, especially Korean skin phenotypes.

Genotype imputation using haplotype information from a genotyped reference sample from a dense genotype array or sequencing infers missing genotypes. This strategy is used because throughout the human genome, there is a correlation structure in the genetic variation of different loci, which is called linkage disequilibrium (LD). The use of this approach can improve GWAS. Reducing the number of missing genotypes can improve the statistical power to detect associations and increase the overall number and density of genomic variants available for association testing.²⁴

Before applying machine learning, data preprocessing is an essential component of many classification and regression models. Appropriate feature selection during the preprocessing process can improve the performance of classification or regression problems with low time complexity.²⁵

Machine-learning approaches adopt sets of complex statistical and computational algorithms to make predictions by mathematically mapping complex associations between risk SNPs and phenotypes²⁶ and can be supervised or unsupervised.²⁷ Although the utility of unsupervised machine-learning methods for nongenetic data in phenotype predictions has been demonstrated,²⁸ our focus was on supervised modeling informed by SNP data. Supervised machine-learning phenotype prediction models are generated by training preset learning algorithms to map relationships between individual sample genotype data and the associated phenotype. Optimal predictive power for the target phenotype is achieved by mapping the pattern of selected features (variables) within the training genotype data.²⁹ At the end of the training stage, models with the maximum predictive power for the training dataset are selected for validation.³⁰ The validation stage is accomplished by evaluating the algorithm on an independent dataset. Cross-validation is a commonly used procedure for validating model performance using the original dataset. We performed five-fold cross-validation. The method divides the data into five sets, each of which is analyzed as a test set, and the data except for the test set are used as a training set to evaluate the model. However, external validation using an independent dataset is required to confirm the predictive power of a machine-learning model.

The identification of significant genotype–phenotype associations is essential for developing an effective model. The present study aimed to use machine-learning algorithms to find the optimal solution for personal skin diagnosis by identifying genetic markers that were associated with skin biophysical parameters, defining skin genotype–phenotype correlations, and providing the most appropriate machine-learning method for predicting skin phenotype from genotype. To confirm the feasibility, we performed three types of linear regression analyses as follows: linear regression, ridge regression using L2-regularization, and regression using a support vector.

We recruited 749 Korean women and generated various clinical and genomic skin data from each woman. These were merged with array data and used for GWAS and machine learning. Furthermore, we identified significant markers related to skin phenotype using GWAS level analysis. We built a machine-learning model and used linear regression analysis to evaluate the model’s performance. We expect that our results may be used as an indicator to provide personal skin traits.

Materials and Methods

Subjects

This research was approved by the Korean public institutional review board (IRB), and all participants were fully informed about the study process before obtaining written informed consent. All the study procedures were performed in accordance with the Korean public IRB. A total of 749 Korean women aged 30–50 years (40.8 ± 5.4 years) participated in this study, which was conducted from January 2019 to February 2020 in Kolmar Korea Co. There were 309 women aged <40 years and 440 women aged >40 years. Subjects who had any specific severe skin disease (eg, erythema or infectious disease) or allergy to cosmetics in the area for clinical measurement were excluded. Before the measurement procedure, subjects were required to remove all makeup with a cleanser. Subjects waited in a room that was maintained at constant temperature and humidity conditions (24°C ± 2°C and 50% ± 5%, respectively) for 30 min.

For the GWAS, all subjects provided saliva using the AccuSaliva collection kit (ACN11.01, AccuGene, Korea), which was stored in a preservative solution at room temperature (15°C–25°C) until delivery to the Eone Diagnomics Genome Center. During the 30 min before saliva collection, eating, brushing teeth, drinking, chewing gum, smoking were prohibited. Subjects gargled with water, and saliva was collected after 10 min. Lifestyle and current skin condition information were collected using a questionnaire, which included cosmetic (skin care and makeup products) use, facial washing habits, use of sunscreen, skin sensitivity, concerns about acne, smoking, and hours of sleep.

Evaluation of Skin Biophysical Parameters

To objectively investigate the skin condition of participants, we quantitatively measured five skin biophysical parameters using noninvasive clinical equipment: melanin level, gloss, hydration, wrinkle, and elasticity. We measured these biophysical skin parameters from four distinct facial sites: forehead, cheek, canthus, and chin. Repeated measurements were not exactly in the same location; however, measurements were obtained using the same pressure. The measurements were averaged to be used for group statistics.

Melanin

Melanin level, which contributes to skin pigmentation, was measured using Mexameter MX 18 (Courage & Khazaka, Cologne, Germany), which calculates the amount of light absorbed by the skin. Melanin level was measured three times on hyperpigmented areas of the face, and the averaged melanin index value (arbitrary unit [A.U.]) was used for further analysis.³¹

Gloss

Skin-Glossymeter GL200 (Courage & Khazaka, Cologne, Germany) was used to measure gloss, which is based on light reflection from the skin surface. The cheek area was measured 10 times in a row and averaged to obtain the gloss diffuse scattering correction (DSC) value (A.U.).³²

Hydration

Skin hydration level of the cheek area was evaluated using Corneometer CM 825 (Courage & Khazaka, Cologne, Germany) by measuring the electrical capacitance of the stratum corneum, which is the outermost layer of the skin. Skin capacitance values were converted into A.U. according to the moisture content of skin measured by the device. Five measurements were acquired and averaged (A.U.).³³

Wrinkle

For evaluation of fine wrinkle depth, a replica model was taken according to the method described in previous studies.^34,35 Skin replica images of crow’s feet were obtained using Skin-Visiometer SV 600 (Courage & Khazaka, Cologne, Germany) to obtain mean wrinkle depth (A.U.).

Skin Elasticity

Cutometer Dual MPA 580 (Courage & Khazaka, Cologne, Germany) was used to assess skin elasticity, which evaluates the degree of skin resilience from skin deformed by suction of the probe. R2 (gross elasticity) was used as the main parameter to evaluate overall skin elasticity; the closer the R2 value is to 1 (100%), the better the skin elasticity.³⁶

Genome-Wide SNP Genotyping and Algorithms

Genotyping and Data Quality Control

The Infinium Global Screening Array (GSA) by Illumina GSA Bead Chip (Illumina Inc., San Diego, CA) was used for this study. The GSA chip contains multiethnic genome-wide content, curated clinical research variants, and quality control (QC) markers for precision medicine research. In addition to the 646,824 GSA markers developed by the consortium, we designed 34,136 additional markers, which included pharmacogenomics and Korean-specific content, resulting in a total of 680,960 markers.

The variants with more than 0.2 differences in minor allele frequency (MAF) were compared with 1000G³⁷ East Asian (EAS) and gnomAD³⁸ EAS were removed. For the generic variants, we used the following QC criteria: control for MAF >1%, Hardy–Weinberg equilibrium (p > 0.00001), genotype call rate >98%, and individual missing rate <2%. Our final dataset comprised 375,084 SNPs.

For QC of the sample, six samples with an individual missing rate of >2% were excluded from analyses. Next, data outliers were removed from two analyses. For each phenotype, 10 outliers were removed on the basis of interquartile range (IQR). IQR represents the 25th–75th percentile of the data, and any differences of more than three times the IQR were excluded from analyses. Three further samples were removed on the basis of the principal component analysis (PCA) plot in Supplementary Figure 1.³⁹ PCA is a dimensionality reduction method that is often used to reduce the dimensionality of large data sets by transforming a large set of variables into a smaller set that still contains most of the information. PCA reduces data by geometrically projecting them onto lower dimensions called principal components (PCs) and then uses a limited number of PCs with the goal of finding the best summary of the data. A total of 730 samples were used in the study.

GWAS (Imputation)

Imputation was performed using SHAPEIT v2.r904 and Minimac3 v2.0.1 software. We used 1000G Phase 3 and an imputed genetic dosage with R-squared statistics >0.8 for the association analysis. The association tests of genotyped SNPs were performed using a linear regression model in PLINK v1.90b (with the −linear flag) with age as a covariate. Manhattan plots were generated using the qqman 0.1.4 library in R v3.6 both before and after imputation.^40–42

Feature Selection and Machine-Learning Models

a. Feature selection

First, we selected only potentially associated autosomal variants (GWAS p < 0.05) for machine-learning analyses. To extract the most promising predictors, XGBoost was applied to use the marker chosen with nonzero “gain” coefficients using the Sklearn v0.20.3 package, Xgboost in Python v3.0.

b. Machine-learning models

Initially, the dataset (N = 730) was randomly divided into training (N = 584, 80%) and test (N = 146, 20%) sets. We evaluated the model by five-fold cross-validation with the training set. This procedure was used to robustly evaluate the power of the machine-learning models. All steps of feature selection and training were conducted oLn training sets only, and testing sets were used only for the final evaluation.

The training set analyzed three algorithms using five-fold cross-validation. We used three well-established machine-learning algorithms employing linear regression: linear regression, ridge regression, and linear support vector regression (SVR).

1. Linear regression⁴³

The linear regression algorithm fits a linear model by minimizing the residual sum of squares between the observed yi in the training dataset (i = 1 … N, the sample size) and the values f(xi) predicted by the linear model. The object function is given as shown in the equation below:

• 2. Ridge regression⁴⁴

Ridge regression is a regularized linear model that minimizes the sum of the squared prediction error in the training data and an L2-norm regularization, meaning the sum of the squares of regression coefficients. The advantage of ridge regression over linear regression is that it avoids overfitting the model. Ridge regression still performs well for large multivariate data with more predictors than observations. The object function is given as shown in the equation below:

The tuning parameter λ controls the model’s complexity. If λ = 0, ridge regression becomes a traditional linear regression model.

• 3. Linear support vector regression⁴⁵

Linear SVR aims to find a function f(xi) for which the predictive value deviates by no more than a required accuracy ε from the actual yi for all training data while maximizing the flatness of the function. To implement flatness maximization, an L2-norm regularization that minimizes the squared sum of the regression coefficients is performed. The object function is given as follows:

where l is the quantity of “support vectors,” which are the samples that deviate by more than ε from the actual yi used to fit the model, and C regulates the smoothness of function f(xi).

In all methods applied here, the skin biophysical parameters were used as the phenotype (y), the genotype was used as the predictor s(x), and no additional covariates were included in the regression analysis. Because the phenotype is a continuous value and it is difficult to distinguish two or three consecutive values, only linear regression was used. Accuracy was obtained using r² for the prediction of the subtest set and represented the proportion of variance in the data explained by the respective variables in the model. The result provides the r² value of each fold, the average r² value of five folds, and the r² value of the test set.

We selected one algorithm with a large value with a small deviation in the value of r². Training and testing were performed with the selected algorithms. Correlation was confirmed by plotting the real value and scatter of the prediction value. All computations were performed using the Sklearn v0.20.3 package in Python 3.0. All processes are outlined in Figure 1.

Figure 1.

Flowchart for predicting skin phenotype using genome-wide association study (GWAS) analysis and a GWAS-based machine-learning approach.

Abbreviations: GSA, global screening array; QC, quality control.

Results

Phenotype Measurements

The five phenotype measurement data are presented as means and standard deviations in Table 1. Mean values of each phenotype were (1) melanin level of hyperpigmentation areas (chin and cheek) = 147.02 ± 35.33; (2) skin gloss value of the cheek = 4.35 ± 2.04; (3) hydration level of the cheek = 63.02 ± 16.94; (4) fine wrinkles of the canthus = 17.35 ± 4.96; and (4) skin elasticity level of the cheek = 0.67 ± 0.09.

Table 1.

Parameters, Mean Values, and Standard Deviations (SDs) of the Subjects

Parameters	Site	Mean (± SD)
Age (years)	–	40.8 ± 5.4
Melanin index (A.U.)	Hyperpigmentation spots	147.02 ± 35.33
Gloss DSC value (A.U.)	Cheek	4.35 ± 2.04
Hydration (A.U.)	Cheek	63.02 ± 16.94
Wrinkle Depth (A.U.)	Crow’s feet	17.35 ± 4.96
Elasticity (R2)	Cheek	0.67 ± 0.09

Abbreviations: A.U., arbitrary unit; DSC, diffuse scattering correction.

GWAS

Nineteen samples were excluded from 749 samples at QC, which resulted in 730 samples of genotype data for the primary discovery analysis. All five types of skin measurement data were independently analyzed using GWAS. Results are shown in Manhattan plots and indicate the presence of multiple loci with modest effects (Figure 2). Figure 2 shows results before and after imputation to enable comparisons of SNP markers associated with each trait.

Figure 2.

Manhattan plot of study results before and after imputation. Manhattan plot of −log10 (p value) for all tested single-nucleotide polymorphisms against genomic position. Horizontal lines indicate suggestive (blue, p < 1×10⁻⁰⁵) and significant (red, p < 1×10⁻⁰⁸) thresholds. Skin phenotypes: (A and B) melanin, (C and D) gloss, (E and F) hydration, (G and H) wrinkle, and (I and J) elasticity (A, C, E, G, and I are raw data, and B, D, F, H, and J are results after imputation).

For each phenotype, we found the 46 highest association signals (p < 1×10⁻⁰⁵). Of which, 3 were related to melanin, 20 to gloss, 12 to hydration, 6 to wrinkle, and 5 to elasticity (Table 2). Throughout the human genome, a correlation structure exists across genetic variation of different loci. Owing to the LD in GWAS, the significance marker and surrounding markers are also significant. To confirm this, we performed imputation and comparison and indicated markers that were relevant only to one of the results: one melanin (rs117929211), two hydration (rs138684226 and rs887932), two wrinkle (rs12187267 and rs1991506), and three elasticity (rs2446092, rs73499013, and rs112251095), which are denoted in Table 2. The imputed results are provided in the Supplementary Tables 1–5.

Table 2.

Single-Nucleotide Polymorphisms Showing the Highest Association Signals for Each Phenotype

Phenotype	Gene	Chr	SNP	Position	Risk Allele	BETA	p value
Melanin	ID2	2	†rs117929211	8,781,831	A	22.55	9.58 × 10−06
Melanin	TLR3	4	rs7679967	186,935,500	C	−12.69	1.21 × 10−07
Melanin	ELSPBP1	19	rs79353338	48,512,607	T	14.17	9.80 × 10−06
Gloss	MSL3P1	2	rs498696	234,770,688	A	0.8851	4.05 × 10−06
Gloss	MSL3P1	2	rs498790	234,770,722	G	0.9069	2.67 × 10−06
Gloss	MSL3P1	2	rs568906	234,773,193	T	0.8817	4.72 × 10−06
Gloss	MSL3P1	2	rs570800	234,773,403	G	0.8793	4.57 × 10−06
Gloss	MSL3P1	2	rs213543	234,774,025	T	0.8851	4.05 × 10−06
Gloss	MSL3P1	2	rs188492	234,774,076	G	0.8825	4.33 × 10−06
Gloss	MSL3P1	2	rs213544	234,775,675	T	0.8851	4.05 × 10−06
Gloss	MSL3P1	2	rs6727154	234,788,734	A	0.8851	4.05 × 10−06
Gloss	MSL3P1	2	rs17862910	234,790,181	A	0.8851	4.05 × 10−06
Gloss	MSL3P1	2	rs17862913	234,793,402	T	0.8851	4.05 × 10−06
Gloss	MSL3P1	2	rs653844	234,794,286	G	0.8851	4.05 × 10−06
Gloss	MSL3P1	2	rs17863831	234,796,001	G	0.8848	4.15 × 10−06
Gloss	FGF12	3	rs58496729	192,252,112	C	0.5359	1.86 × 10−06
Gloss	TAAR6	6	rs6924201	132,897,029	T	0.5666	1.87 × 10−06
Gloss	PODXL	7	rs2909486	131,421,680	A	0.4407	4.11 × 10−06
Gloss	PODXL	7	rs2333894	131,454,000	T	0.4281	1.31 × 10−06
Gloss	SLC7A1	13	rs9579422	30,176,650	T	0.8917	7.60 × 10−06
Gloss	SLC7A1	13	rs7998250	30,181,027	A	0.9144	4.91 × 10−06
Gloss	FMN1	15	rs2141438	33,055,904	A	0.417	5.85 × 10−06
Gloss	FMN1	15	rs1258726	33,061,916	C	0.4244	3.47 × 10−06
Hydration	CRIPT	2	rs79885808	46,886,833	G	4.677	4.73 × 10−08
Hydration	DTHD1	4	rs7684936	36,330,976	G	3.831	9.94 × 10−06
Hydration	PLEKHG1	6	rs7764084	150,835,885	A	−4.265	4.56 × 10−06
Hydration	PKD1L1	7	rs75043305	47,970,494	C	−9.887	8.16 × 10−07
Hydration	CSMD1	8	rs77547856	6,144,013	C	−9.153	2.67 × 10−06
Hydration	TNKS	8	†rs138684226	9,213,702	T	−19.91	9.84 × 10−07
Hydration	MYC	8	rs73707295	128,757,836	G	6.187	8.81 × 10−07
Hydration	ARHGAP21	10	rs34485271	24,919,321	G	−17.45	2.09 × 10−06
Hydration	FZD10	12	rs11060760	130,659,526	G	−8.512	6.01 × 10−06
Hydration	IL34	16	rs6499328	70,666,722	T	−3.99	8.68 × 10−06
Hydration	MKNK2	19	†rs887932	2,048,598	T	−5.585	7.15 × 10−06
Hydration	TASP1	20	rs149092789	13,397,517	G	−11.58	2.01 × 10−06
Wrinkle	TTC34	1	rs10797342	2,797,856	C	−1.145	4.58 × 10−06
Wrinkle	TAS1R2	1	rs55962532	19,128,884	G	1.905	7.20 × 10−06
Wrinkle	CMBL	5	rs13359758	10,301,295	T	1.49	4.87 × 10−06
Wrinkle	RETREG1	5	†rs12187267	16,491,052	G	1.324	9.53 × 10−06
Wrinkle	GPAM	10	rs77126241	113,967,204	G	1.869	3.82 × 10−06
Wrinkle	JPH3	16	†rs1991506	87,683,948	G	1.145	3.42 × 10−06
Elasticity	LINC00885	3	rs146095627	195,873,438	G	−0.09634	8.20 × 10−06
Elasticity	IMPG1	6	†rs2446092	77,358,648	A	0.08258	6.53 × 10−06
Elasticity	LOC285889	7	†rs73499013	156,065,946	A	0.02522	7.86 × 10−07
Elasticity	ITGA8	10	†rs112251095	15,513,582	G	0.04671	6.16 × 10−06
Elasticity	SGMS1	10	rs182035024	52,190,205	T	0.09563	9.41 × 10−06

Note: ^†SNPs with no significance marker nearby compared with imputed.

Abbreviations: Chr: chromosome; SNP: single-nucleotide polymorphisms.

The role of 46 novel skin genes (SNPs), which were shown to be associated with five skin phenotypes in this study, needs further study to determine the underlying mechanism of each phenotype.

Machine Learning

Cross-validation results from the training portion of the model building process showed that the best performing model for all phenotypes was ridge regression. However, models with poor performance differed depending on phenotype. Linear regression (r² = 0.7982) performed poorer for melanin, when compared with ridge regression (r² = 0.8586) and linear SVR (r² = 0.8468). Linear SVR (r² = 0.5517) performed poorer for gloss, when compared with linear regression (r² = 0.8643) and ridge regression (r² = 0.8668). The performance of the three models was similar for hydration and wrinkle. Elasticity showed similar performance with linear regression (r² = 0.7981) and ridge regression (r² = 0.8309).

Test results performance was poorer than cross-validation results of the train set. This was confirmed in all phenotypes and models. The differences between the ridge regression results, which showed the best performance, and cross-validation results were 0.2164 for melanin, 0.1402 for gloss, 0.1574 for hydration, 0.1962 for wrinkle, and 0.1232 for elasticity.

Finally, phenotype values predicted using the ridge regression model from the test set and the measured phenotype values were compared in a scatter plot (Figure 3). An optimal result is represented by data falling on the trend line; the closer the data are to the trend line, the closer the predicted value is to the measured value. In our study, the results were close to the trend line, which was confirmed by r² in Table 3.

Figure 3.

Scatter plot of measured values and machine-learning predicted values. Each skin phenotype: (A) Melanin, linear regression showed a best fit of y = 0.51x + 72.95, r² = 0.6422; (B) Gloss, linear regression showed a best fit of y = 0.54x + 2.02, r² = 0.8668; (C) Hydration, linear regression showed a best fit of y = 0.57x + 26.24, r² = 0.8630; (D) Wrinkle, linear regression showed a best fit of y = 0.52x + 8.21, r² = 0.8681; and (E) Elasticity, linear regression showed a best fit of y = 0.56x + 0.29, r² = 0.8309.

Table 3.

Summary of Model Performance (Five-Fold Cross-Validation and Test, R²)

Phenotype	Model	Five-Fold Cross-Validation Result						Test Result
		Fold 1	Fold 2	Fold 3	Fold 4	Fold 5	Mean
Melanin	Linear regression	0.8674	0.8864	0.8255	0.5734	0.8383	0.7982	0.4917
Melanin	Ridge regression	0.8745	0.8855	0.855	0.8357	0.8422	0.8586	0.6422
Melanin	Linear SVR	0.8653	0.8738	0.8377	0.8197	0.8375	0.8468	0.6609
Gloss	Linear regression	0.8554	0.8804	0.8757	0.8485	0.8616	0.8643	0.7163
Gloss	Ridge regression	0.8555	0.8793	0.8803	0.8524	0.8664	0.8668	0.7266
Gloss	Linear SVR	0.5502	0.524	0.5505	0.5619	0.572	0.5517	0.4612
Hydration	Linear regression	0.8586	0.8302	0.8569	0.8111	0.8654	0.8445	0.7012
Hydration	Ridge regression	0.8601	0.8583	0.853	0.8761	0.8675	0.863	0.7056
Hydration	Linear SVR	0.8514	0.8294	0.8433	0.8603	0.8485	0.8466	0.6969
Wrinkle	Linear regression	0.8498	0.8558	0.8566	0.7998	0.8604	0.8445	0.6692
Wrinkle	Ridge regression	0.8552	0.8567	0.869	0.8822	0.8773	0.8681	0.6719
Wrinkle	Linear SVR	0.8222	0.8091	0.8	0.8194	0.8084	0.8118	0.631
Elasticity	Linear regression	0.8191	0.6984	0.8258	0.7952	0.8522	0.7981	0.6511
Elasticity	Ridge regression	0.8407	0.82	0.8456	0.7976	0.8507	0.8309	0.7077
Elasticity	Linear SVR	–	–	–	–	–	–	–

Abbreviation: SVR: support vector regression.

Discussion

There are differences in the degree of human skin aging, which depend on individual genes and the environment they encounter. Specifically, studies on clinical skin aging features based on age group in Asians, particularly Koreans, have shown that elasticity decreases between 20 and 40 years, wrinkle depth increases between 40 and 60 years, and age-dependent hyperpigmentation increases occur. Across the world, many cosmetic companies have tried to create products that include a cosmeceutical ingredient to address these aging factors.^46–48 However, the aging process can vary because of the exposome (external and lifestyle factors).^1,9,10,49 Although skin health and aging are affected by both the genome and exposome, an individual’s intrinsic genes do not change. Thus, predicting the possibility of aging through genetic analysis is an important research goal to prevent or slow aging effects. Previous trends have shown that people have used universal cosmetics and functional health foods in the market, but currently, there is a preference for products specifically suited to an individual’s own characteristics and condition. With the development of various technologies such as GWAS, various analyses such as the identification of unique SNPs and microbiomes of individuals have become easier and customized product development has become possible through machine learning and AI prediction. Most GWAS skin phenotyping studies have focused on Europeans and pigmentation; therefore, such analysis among Koreans (Asian) is needed. We performed a GWAS on five skin phenotypes among 749 Korean adult women and identified SNPs reaching genome-wide significant associations.

A recently reported large-scale GWAS study conducted in a Korean population, several genetic markers on pigmented spots, such as CDKN2B-AS1 and Inc01877, were found, which were different from those identified in other ethnicities.²³ In addition, 21 new genes have been discovered, including the already known genes FCRL5 and OCA2 (pigmentation), associated with five cosmetic skin properties: wrinkles, moisture content, pigmentation, oil content, and sensitivity.⁵⁰

In our study, we found 46 novel genetic markers that were significantly correlated with five skin traits of 749 Korean women. Although our result is based on smaller sample sizes than others reported in recent large-scale GWAS studies, this is considered scientifically significant because it was obtained from approximately 750 subjects and identified new related SNPs unique to Koreans. In the case of genes related to hydration and melanin among the identified candidate SNPs, biological correlations have been found in previous researches. Toll-like receptor 3 (TLR3) is the first sensor for UV exposure, and its activity is associated with barrier recovery. Moreover, TLR3 stimulation promotes melanogenesis, transfer of melanosome in melanocytes, and absorption of melanosomes by keratinocytes.⁵¹ The Cub and Sushi multiple domains 1 (CSMD1) gene has been identified as a candidate gene in psoriasis as well as cancer and smoking in numerous human genetics studies.^52–54 The Myc gene has been shown to affect the sebocyte differentiation pathway; overexpression of the c-Myc gene results in an increase in sebaceous gland size and number.⁵⁵ Given that maintaining skin health is associated with parameters such as sebum, acidic pH, and hydration, the Myc gene is thought to interact with skin hydration.⁵⁶ The biological role of our other novel candidate genes will be examined in future studies.

Because the present findings were not compared with those of other ethnic groups, further research in larger samples is needed to gain complete understanding of genomic characteristics. Nevertheless, to the best of our knowledge, this study is the first to altogether show new candidate genes for five skin traits in a Korean cohort. It may be possible to accurately diagnose or predict an individual’s skin condition with only genotype information. This application model of skin trait prediction potentially can be applied to the development of customized cosmetics that are perfect for individuals based on individual genes. To estimate personal skin condition, DNA analysis and a ridge regression machine-learning model can be used without the need for noninvasive clinical devices. Although our results are specific to Asians, they are valuable in regard to the discovery of SNPs unique to Koreans and for comparisons with previous research results.

Taken together, our study procedures using saliva-derived genetic DNA analysis and machine-learning algorithms can not only predict an individual’s skin characteristics but also replace clinical noninvasive measurements.

Conclusion

All people have different facial characteristics due to various intrinsic and extrinsic factors such as life style patterns and genomic factors. Therefore, recently, some technologies such as skin diagnosis and genetic test related to personalized product that meets individual characteristics have been developed. Given that large-scale GWAS researches are aimed at Europeans, the discovery of unique SNPs in Korean population is meaningful. However, future studies are needed to elucidate the role of genes in determining facial phenotypes. Skin aging is mainly affected by age, environmental factors, and genetic factors. Of these factors, genetic factors of a person do not change; therefore, personalized cosmetics can be developed by exploiting and studying these factors. In summary, skin types of Korean women can be estimated through DNA analysis without a clinical device.

Funding Statement

This study was supported by Kolmar Korea Co. and Eone Diagnomics Genome Center Co. equally.

Data Sharing Statement

The datasets used and/or analyzed during the current study are available from the corresponding author (Byoung-Jun Park; A2001@kolmar.co.kr) upon reasonable request.

Ethics Approval and Informed Consent

The study followed the Declaration of Helsinki and was approved by the Korean public IRB of Korea national institute for bioethics policy (Approval number: 2018-1695-004; P01-201811-23-024). All participants were fully informed about the study process before obtaining written informed consent.

Disclosure

Hye-Young Yoo, Ji-Eun Woo, Sung-Ha Park, and Byoung-Jun Park are employees of Kolmar Korea Co., Ltd. Ki-Chan Lee, Sunghoon Lee, Joungsu Joo, Jin-Sik Bae, and Hyuk-Jung Kwon are employees of Eone Diagnomics Genome Center Co., Ltd. The authors report no other potential conflicts of interest in this work.