Mapping and annotating genomic loci to prioritize genes and implicate distinct polygenic adaptations for skin color.

Evidence for adaptation of human skin color to regional ultraviolet radiation suggests shared and distinct genetic variants across populations. However, skin color evolution and genetics in East Asians are understudied. We quantified skin color in 48,433 East Asians using image analysis and identified associated genetic variants and potential causal genes for skin color as well as their polygenic interplay with sun exposure. This genome-wide association study (GWAS) identified 12 known and 11 previously unreported loci and SNP-based heritability was 23-24%. Potential causal genes were determined through the identification of nonsynonymous variants, colocalization with gene expression in skin tissues, and expression levels in melanocytes. Genomic loci associated with pigmentation in East Asians substantially diverged from European populations, and we detected signatures of polygenic adaptation. This large GWAS for objectively quantified skin color in an East Asian population improves understanding of the genetic architecture and polygenic adaptation of skin color and prioritizes potential causal genes.

Image-based investigation of lip aging features in a large number of Korean women.

BACKGROUND: The lips play a significant role in shaping facial aesthetics. Due to the distinct attributes of lips in contrast to other facial skin, a unique approach is imperative for managing lip aging. We analyzed lip characteristics (morphology, wrinkles, and color) to investigate visual changes and distinctive attributes of aging lips. METHODS: By utilizing image data processing methods, including facial landmark detection, pattern recognition, and color quantification, we extracted 11 lip characteristic indices (four morphological indices, four wrinkle indices, and three color indices) from high-resolution images of 1000 Korean women aged 20-69. Correlation tests were conducted to assess the relationship between lip characteristic indices and age, and also between lip morphological and wrinkle indices. RESULTS: Lip height significantly decreased, while lip width and lip ratio (lip width divided by the sum of the upper and lower lip height) significantly increased with aging. Lip wrinkles significantly increased with aging, whereas lip colors (redness and yellowness) decreased. The lip wrinkle indices, which are segmented for the first time in this study, exhibited significant correlations with lip width, and three of them additionally were correlated with lip ratio (p < 0.05). The results imply such morphological changes can be associated with wrinkle formation of human lips. CONCLUSION: The indices suggested in this study can be used for assessing lip aging characteristics, and the study results can contribute to deeper understanding of lip aging.

Skin aging-related microbial types separated by Cutibacterium and α-diversity.

BACKGROUND: Studies on the skin microbiome have been conducted to uncover the relationship between skin microbes and the host. However, most of these studies have primarily focused on analyzing individual microbial compositions, which has resulted in a limited understanding of the overall relationship. METHODS: We analyzed the facial skin characteristics and microbial profiles of 100 healthy Korean female volunteers using the V1-V2 region of the 16S ribosomal RNA gene. RESULTS: The two most prominent features of the facial skin microbiome, the proportion of Cutibacterium and α-diversity, were associated with most of the skin characteristics. Based on clustering results, we proposed four types of facial skin microbiome: type C for Cutibacterium, type B for balanced, type CB for those between types C and B, and type O for others. Type C, which has a high proportion of Cutibacterium, showed high levels of pigmentation, wrinkles, pores, and sagging pores, indicating a tendency for severe skin aging. Type B, which has no dominant species and high microbial diversity, had lower values for pigmentation and wrinkles indicating less severe skin aging. Type CB was an intermediate type between type C and type B in terms of microbial composition and the level of skin aging. Type O dominated by microorganisms other than Cutibacterium, had high levels of sebum and pores but low levels of wrinkles. CONCLUSION: We proposed a criterion for classifying facial skin microbial types, each of which showed distinct facial skin aging features. Our simplified microbial types will contribute to a better understanding of facial skin microbial studies.

Longitudinal study of the interplay between the skin barrier and facial microbiome over 1 year.

Skin is a diverse ecosystem that provides a habitat for microorganisms. The skin condition and the skin microbiome interact each other under diverse environmental conditions. This study was conducted on 10 study participants for a one-year, from September 2020 to August 2021, to investigate the variability of skin microbiome and skin biophysical parameters [TEWL, hydration, and elasticity (R5)] according to season, and to understand the interplay between skin microbiome and skin characteristics. We identified that Cutibacterium, Corynebacterium, Staphyloccocus, unclassified genus within Neisseriaceae, and Streptococcus were major skin microbial taxa at the genus level, and fluctuated with the seasons. Cutibacterium was more abundant in winter, while Corynebacterium, Staphylococcus, and Streptococcus were more abundant in summer. Notably, Cutibacterium and skin barrier parameter, TEWL, exhibited a co-decreasing pattern from winter to summer and showed a significant association between Cutibacterium and TEWL. Furthermore, functional profiling using KEGG provided clues on the impact of Cutibacterium on the host skin barrier. This study enhances our understanding of the skin microbiome and its interplay with skin characteristics and highlights the importance of seasonal dynamics in shaping skin microbial composition.

Two newly developed indices evaluating facial skin aging.

BACKGROUND: Skin assessment methodologies have focused mainly on intuitive aging characteristics, including facial wrinkles and pigmented spots, and usually adopt pattern recognition algorithms. Recently, distinct methods of interpreting skin aging, such as the detection of facial landmarks and age prediction using machine learning techniques, have been conducted. MATERIALS AND METHODS: We defined two indices that represent the severity of facial aging. The first index was the ratio of the bizygomatic distance and bigonial distance. The second index was the ratio of the degrees of the near mandible. The indices extracted from two-dimensional frontal face images were intended to show the deformation of the facial skin downward with aging progress. To validate whether these proposed indicators can represent facial aging, we conducted correlation tests with age and facial skin characteristics and performed association tests between the indices and facial skin characteristics, adjusted for age. RESULTS: The indices showed strong correlations with age (r = 0.557 and 0.464, respectively) and facial skin characteristics. Although there were correlations between the indices and facial skin features, the associations between the indices and facial skin characteristics adjusted for age were weak or not significant. This suggests that the newly developed indices are appropriate for evaluating facial skin aging and distinct from typical measurements. CONCLUSION: We suggest two novel indices for evaluating facial aging based on frontal face images. The indices exhibited strong correlations with age and representative facial skin characteristics. The newly developed values can be differentiated indicators of facial aging compared with general skin features.

Comparative analysis of skin characteristics evaluation by a dermatologist and the Janus-III measurement system.

BACKGROUND: The Janus-III measurement system evaluates the overall skin characteristics such as skin pore, wrinkle, sebum, porphyrin, skin pigmentation, and skin color using high-resolution facial images. The values are measured from five different facial areas, namely, the forehead, nose, corner of/skin below the eyes, and cheeks. Owing to its convenience and diverse measuring characteristics, Janus-III has been widely used in skin research and the cosmetic industry in Korea. In our previous study, we revealed the consistency and reliability of the system with repeatedly measured values. Its measuring performance was investigated statistically, but to make it more reliable for academic skin research, additional verification by a professional dermatologist is needed. MATERIALS AND METHODS: In this study, we conducted comparative analysis of three skin characteristics (pigmented spot, skin color, and eye wrinkle) by a dermatologist and the Janus-III measurement system. We utilized 330 image data that were cropped from the whole facial images of 330 different participants to avoid correlation among the three measuring items. Pearson's correlation coefficient exhibited similar patterns between the system and the dermatologist's findings. RESULTS: The main finding of our study was that the measured value of skin characteristics by the Janus-III system showed clear correlation with the values evaluated by a dermatologist, especially in a pigmented spot. CONCLUSION: Therefore, it would be a plausible idea to consider the Janus-III system for specialized research of skin characteristics even with a small sample size.

GWAS Analysis of 17,019 Korean Women Identifies the Variants Associated with Facial Pigmented Spots.

Variation in skin pigmentation can be affected by both environmental factors and intrinsic factors such as age, gender, and genetic variation. Recent GWASs revealed that genetic variants of genes functionally related to a pigmentation pathway were associated with skin pigmentary traits. However, these GWASs focused on populations with European ancestry, and only a few studies have been performed on Asian populations, limiting our understanding of the genetic basis of skin pigmentary traits in Asians. To evaluate the genetic variants associated with facial pigmented spots, we conducted a GWAS analysis of objectively measured facial pigmented spots in 17,019 Korean women. This large-scale GWAS identified several genomic loci that were significantly associated with facial pigmented spots (five previously reported loci and two previously unreported loci, to our knowledge), which were detected by UV light: BNC2 at 9p22 (rs16935073; P-value = 2.11 × 10-46), PPARGC1B at 5q32 (rs32579; P-value = 9.04 × 10-42), 10q26 (rs11198112; P-value = 9.66 × 10-38), MC1R at 16q24 (rs2228479; P-value = 6.62 × 10-21), lnc01877 at 2q33 (rs12693889; P-value = 1.59 × 10-11), CDKN2B-AS1 at 9p21 (rs643319; P-value = 7.76 × 10-9), and MFSD12 at 19p13 (rs2240751; P-value = 9.70 × 10-9). Further functional characterization of the candidate genes needs to be done to fully evaluate their contribution to facial pigmented spots.

Enhanced Permutation Tests via Multiple Pruning.

Big multi-omics data in bioinformatics often consists of a huge number of features and relatively small numbers of samples. In addition, features from multi-omics data have their own specific characteristics depending on whether they are from genomics, proteomics, metabolomics, etc. Due to these distinct characteristics, standard statistical analyses using parametric-based assumptions may sometimes fail to provide exact asymptotic results. To resolve this issue, permutation tests can be a way to exactly analyze multi-omics data because they are distribution-free and flexible to use. In permutation tests, p-values are evaluated by estimating the locations of test statistics in an empirical null distribution generated by random shuffling. However, the permutation approach can be infeasible when the number of features increases, because more stringent control of type I error is needed for multiple hypothesis testing, and consequently, much larger numbers of permutations are required to reach significance. To address this problem, we propose a well-organized strategy, "ENhanced Permutation tests via multiple Pruning (ENPP)." ENPP prunes the features in every permutation round if they are determined to be non-significant. In other words, if the feature statistics from the permuted datasets exceed the feature statistics from the original dataset, beyond a predetermined threshold, the feature is determined to be non-significant. If so, ENPP removes the feature and iterates the process without the feature in the next permutation round. Our simulation study showed that the ENPP method could remove about 50% of the features at the first permutation round, and, by the 100th permutation round, 98% of the features had been removed and only 7.4% of the computation time with the original unpruned permutation approach had elapsed. In addition, we applied this approach to a real data set (Korea Association REsource: KARE) of 327,872 SNPs to find association with a non-normally distributed phenotype (fasting plasma glucose), interpreted the results, and discussed the feasibility and advantages of the approach.

Metagenomic Analysis of Serum Microbe-Derived Extracellular Vesicles and Diagnostic Models to Differentiate Ovarian Cancer and Benign Ovarian Tumor.

We aimed to develop a diagnostic model identifying ovarian cancer (OC) from benign ovarian tumors using metagenomic data from serum microbe-derived extracellular vesicles (EVs). We obtained serum samples from 166 patients with pathologically confirmed OC and 76 patients with benign ovarian tumors. For model construction and validation, samples were randomly divided into training and test sets in the ratio 2:1. Isolation of microbial EVs from serum samples of the patients and 16S rDNA amplicon sequencing were carried out. Metagenomic and clinicopathologic data-based OC diagnostic models were constructed in the training set and then validated in the test set. There were significant differences in the metagenomic profiles between the OC and benign ovarian tumor groups; specifically, genus Acinetobacter was significantly more abundant in the OC group. More importantly, Acinetobacter was the only common genus identified by seven different statistical analysis methods. Among the various metagenomic and clinicopathologic data-based OC diagnostic models, the model consisting of age, serum CA-125 levels, and relative abundance of Acinetobacter showed the best diagnostic performance with the area under the receiver operating characteristic curve of 0.898 and 0.846 in the training and test sets, respectively. Thus, our findings establish a metagenomic analysis of serum microbe-derived EVs as a potential tool for the diagnosis of OC.

Repeated measurements of facial skin characteristics using the Janus-â ¢ measurement system.

BACKGROUND: For personalized skin care, noninvasive quantitative methods to evaluate facial skin characteristics are important. Janus-III is one of the most widely used imaging analysis devices in the skin care industry in Korea. Janus-III generates values for a range of skin characteristics. Due to the convenience of obtaining results for a variety of skin characteristics in a single measurement, the use of Janus-III in cosmetic stores and research institutes has been recently increasing. However, the consistency of skin measurements of Janus-III has not been elucidated yet. MATERIALS AND METHODS: In this study, we repeated skin measurements three times for 70 different subjects and compared each numerical value in order to assess the consistency of the Janus-III. For this purpose, we compared between-sample distances and within-sample distances. RESULTS: We found important patterns for future analyses in terms of consistency. First, the average values of skin measurement categories were more reliable than individual part values of facial segments. Second, center part values such as forehead and nose were more reliable than side part values such as left and right part segments. CONCLUSION: If researchers who use Janus-III for studies of facial characteristics analyze average and center part values first, they can obtain relatively reliable patterns of facial skin characteristics.

Circulating Microbiota-Based Metagenomic Signature for Detection of Hepatocellular Carcinoma.

Circulating microbial dysbiosis is associated with chronic liver disease including nonalcoholic steatohepatitis and alcoholic liver disease. In this study, we evaluated whether disease-specific alterations of circulating microbiome are present in patients with cirrhosis and hepatocellular carcinoma (HCC), and their potential as diagnostic biomarkers for HCC. We performed cross-sectional metagenomic analyses of serum samples from 79 patients with HCC, 83 with cirrhosis, and 201 matching healthy controls, and validated the results in the same number of subjects. Serum bacterial DNA was analyzed using high-throughput pyrosequencing after amplification of the V3-V4 hypervariable regions of 16S rDNA. Blood microbial diversity was significantly reduced in HCC, compared with cirrhosis and control. There were significant differences in the relative abundances of several bacterial taxa that correlate with the presence of HCC, thus defining a specific blood microbiome-derived metagenomic signature of HCC. We identified 5 microbial gene markers-based model which distinguished HCC from controls with an area under the receiver-operating curve (AUC) of 0.879 and a balanced accuracy of 81.6%. In the validation, this model accurately distinguished HCC with an AUC of 0.875 and an accuracy of 79.8%. In conclusion, circulating microbiome-based signatures may be potential biomarkers for the detection HCC.

Effect of Interaction between Early Menarche and Genetic Polymorphisms on Triglyceride.

Early menarche has been associated with increased risk of metabolic syndrome. Therefore, investigating the association of each component of metabolic syndrome with age at menarche, and interactions between them, might lead to a better understanding of metabolic syndrome pathogenesis. In this study, we evaluated age at menarche for risk of metabolic syndrome and associations with its components. As a result, the risk of MetS incidence was significantly increased only at ≤12 years of age at menarche (OR = 1.91, P < 0.05). Women with early menarche (≤12 years) had significantly higher levels of triglycerides (ß coefficient = 37.83, P = 0.02). In addition, hypertriglyceridemia was significantly increased at early menarche with 1.99 (95% CI: 1.16-3.41, P < 0.01). With GWAS-based pathway analysis, we found the type 2 diabetes mellitus, stress-activated protein kinase signaling, and Jun amino-terminal kinase cascade pathways (all nominal P < 0.001, all FDR < 0.05) to be significantly involved with early menarche on triglyceride levels. These findings may help us understand the role of early menarche on triglyceride and interaction between gene and early menarche on triglyceride for the development of metabolic syndrome.

Fuzzy set-based generalized multifactor dimensionality reduction analysis of gene-gene interactions.

BACKGROUND: Gene-gene interactions (GGIs) are a known cause of missing heritability. Multifactor dimensionality reduction (MDR) is one of most commonly used methods for GGI detection. The generalized multifactor dimensionality reduction (GMDR) method is an extension of MDR method that is applicable to various types of traits, and allows covariate adjustments. Our previous Fuzzy MDR (FMDR) is another extension for overcoming simple binary classification. FMDR uses continuous member-ship values instead of binary membership values 0 and 1, improving power for detecting causal SNPs and more intuitive interpretations in real data analysis. Here, we propose the fuzzy generalized multifactor dimensionality reduction (FGMDR) method, as a combined analysis of fuzzy set-based analysis and GMDR method, to detect GGIs associated with diseases using fuzzy set theory. RESULTS: Through simulation studies for different types of traits, the proposed FGMDR showed a higher detection ratio of causal SNPs, compared to GMDR. We then applied FGMDR to two real data: Crohn's disease (CD) data from the Wellcome Trust Case Control Consortium (WTCCC) with a binary phenotype and the Homeostasis Model Assessment of Insulin Resistance (HOMA-IR) data from Korean population with a continuous phenotype. The interactions derived by our method include the pre-reported interactions associated with phenotypes. CONCLUSIONS: The proposed FGMDR performs well for GGI detection with covariate adjustments. The program written in R for FGMDR is available at http://statgen.snu.ac.kr/software/FGMDR .

GxGrare: gene-gene interaction analysis method for rare variants from high-throughput sequencing data.

BACKGROUND: With the rapid advancement of array-based genotyping techniques, genome-wide association studies (GWAS) have successfully identified common genetic variants associated with common complex diseases. However, it has been shown that only a small proportion of the genetic etiology of complex diseases could be explained by the genetic factors identified from GWAS. This missing heritability could possibly be explained by gene-gene interaction (epistasis) and rare variants. There has been an exponential growth of gene-gene interaction analysis for common variants in terms of methodological developments and practical applications. Also, the recent advancement of high-throughput sequencing technologies makes it possible to conduct rare variant analysis. However, little progress has been made in gene-gene interaction analysis for rare variants. RESULTS: Here, we propose GxGrare which is a new gene-gene interaction method for the rare variants in the framework of the multifactor dimensionality reduction (MDR) analysis. The proposed method consists of three steps; 1) collapsing the rare variants, 2) MDR analysis for the collapsed rare variants, and 3) detect top candidate interaction pairs. GxGrare can be used for the detection of not only gene-gene interactions, but also interactions within a single gene. The proposed method is illustrated with 1080 whole exome sequencing data of the Korean population in order to identify causal gene-gene interaction for rare variants for type 2 diabetes. CONCLUSION: The proposed GxGrare performs well for gene-gene interaction detection with collapsing of rare variants. GxGrare is available at http://bibs.snu.ac.kr/software/gxgrare which contains simulation data and documentation. Supported operating systems include Linux and OS X.

EFMDR-Fast: An Application of Empirical Fuzzy Multifactor Dimensionality Reduction for Fast Execution.

Gene-gene interaction is a key factor to explain the missing heritability. Many methods have been proposed to identify gene-gene interactions. Multi-factor dimensionality reduction (MDR) is a well-known method for gene-gene interaction detection by reduction from genotypes of SNP combination to a binary variable with a value of high risk or low risk. This method is widely expanded to own a specific objective. Among those expansions, fuzzy-MDR used the fuzzy-set theory for the membership of high risk or low risk and increase the detection rates of gene-gene interactions. Fuzzy-MDR is expanded by a maximum likelihood estimator as new membership function in empirical fuzzy MDR (EFMDR). However, EFMDR is relatively slow because it is implemented by R script language. Therefore, in this study, we implement EFMDR using RCPP (c++ package) for faster executions. Our implementation for faster EFMDR called EMMDR-Fast is about 800 times faster than EFMDR written by R script only.

An empirical fuzzy multifactor dimensionality reduction method for detecting gene-gene interactions.

BACKGROUND: Detection of gene-gene interaction (GGI) is a key challenge towards solving the problem of missing heritability in genetics. The multifactor dimensionality reduction (MDR) method has been widely studied for detecting GGIs. MDR reduces the dimensionality of multi-factor by means of binary classification into high-risk (H) or low-risk (L) groups. Unfortunately, this simple binary classification does not reflect the uncertainty of H/L classification. Thus, we proposed Fuzzy MDR to overcome limitations of binary classification by introducing the degree of membership of two fuzzy sets H/L. While Fuzzy MDR demonstrated higher power than that of MDR, its performance is highly dependent on the several tuning parameters. In real applications, it is not easy to choose appropriate tuning parameter values. RESULT: In this work, we propose an empirical fuzzy MDR (EF-MDR) which does not require specifying tuning parameters values. Here, we propose an empirical approach to estimating the membership degree that can be directly estimated from the data. In EF-MDR, the membership degree is estimated by the maximum likelihood estimator of the proportion of cases(controls) in each genotype combination. We also show that the balanced accuracy measure derived from this new membership function is a linear function of the standard chi-square statistics. This relationship allows us to perform the standard significance test using p-values in the MDR framework without permutation. Through two simulation studies, the power of the proposed EF-MDR is shown to be higher than those of MDR and Fuzzy MDR. We illustrate the proposed EF-MDR by analyzing Crohn's disease (CD) and bipolar disorder (BD) in the Wellcome Trust Case Control Consortium (WTCCC) dataset. CONCLUSION: We propose an empirical Fuzzy MDR for detecting GGI using the maximum likelihood of the proportion of cases(controls) as the membership degree of the genotype combination. The program written in R for EF-MDR is available at http://statgen.snu.ac.kr/software/EF-MDR .

A novel fuzzy set based multifactor dimensionality reduction method for detecting gene-gene interaction.

BACKGROUND: Gene-gene interaction (GGI) is one of the most popular approaches for finding the missing heritability of common complex traits in genetic association studies. The multifactor dimensionality reduction (MDR) method has been widely studied for detecting GGIs. In order to identify the best interaction model associated with disease susceptibility, MDR compares all possible genotype combinations in terms of their predictability of disease status from a simple binary high(H) and low(L) risk classification. However, this simple binary classification does not reflect the uncertainty of H/L classification. METHODS: We regard classifying H/L as equivalent to defining the degree of membership of two risk groups H/L. By adopting the fuzzy set theory, we propose Fuzzy MDR which takes into account the uncertainty of H/L classification. Fuzzy MDR allows the possibility of partial membership of H/L through a membership function which transforms the degree of uncertainty into a [0,1] scale. The best genotype combinations can be selected which maximizes a new fuzzy set based accuracy measure. RESULTS: Two simulation studies are conducted to compare the power of the proposed Fuzzy MDR with that of MDR. Our results show that Fuzzy MDR has higher power than MDR. We illustrate the proposed Fuzzy MDR by analysing bipolar disorder (BD) trait of the WTCCC dataset to detect GGI associated with BD. CONCLUSIONS: We propose a novel Fuzzy MDR method to detect gene-gene interaction by taking into account the uncertainly of H/L classification and show that it has higher power than MDR. Fuzzy MDR can be easily extended to handle continuous phenotypes as well. The program written in R for the proposed Fuzzy MDR is available at https://statgen.snu.ac.kr/software/FuzzyMDR.

Integrative network analysis for survival-associated gene-gene interactions across multiple genomic profiles in ovarian cancer.

BACKGROUND: Recent advances in high-throughput technology and the emergence of large-scale genomic datasets have enabled detection of genomic features that affect clinical outcomes. Although many previous computational studies have analysed the effect of each single gene or the additive effects of multiple genes on the clinical outcome, less attention has been devoted to the identification of gene-gene interactions of general type that are associated with the clinical outcome. Moreover, the integration of information from multiple molecular profiles adds another challenge to this problem. Recently, network-based approaches have gained huge popularity. However, previous network construction methods have been more concerned with the relationship between features only, rather than the effect of feature interactions on clinical outcome. METHODS: We propose a mutual information-based integrative network analysis framework (MINA) that identifies gene pairs associated with clinical outcome and systematically analyses the resulting networks over multiple genomic profiles. We implement an efficient non-parametric testing scheme that ensures the significance of detected gene interactions. We develop a tool named MINA that automates the proposed analysis scheme of identifying outcome-associated gene interactions and generating various networks from those interacting pairs for downstream analysis. RESULTS: We demonstrate the proposed framework using real data from ovarian cancer patients in The Cancer Genome Atlas (TCGA). Statistically significant gene pairs associated with survival were identified from multiple genomic profiles, which include many individual genes that have weak or no effect on survival. Moreover, we also show that integrated networks, constructed by merging networks from multiple genomic profiles, demonstrate better topological properties and biological significance than individual networks. CONCLUSIONS: We have developed a simple but powerful analysis tool that is able to detect gene-gene interactions associated with clinical outcome on multiple genomic profiles. By being network-based, our approach provides a better insight into the underlying gene-gene interaction mechanisms that affect the clinical outcome of cancer patients.

Effect of Next-Generation Exome Sequencing Depth for Discovery of Diagnostic Variants.

Sequencing depth, which is directly related to the cost and time required for the generation, processing, and maintenance of next-generation sequencing data, is an important factor in the practical utilization of such data in clinical fields. Unfortunately, identifying an exome sequencing depth adequate for clinical use is a challenge that has not been addressed extensively. Here, we investigate the effect of exome sequencing depth on the discovery of sequence variants for clinical use. Toward this, we sequenced ten germ-line blood samples from breast cancer patients on the Illumina platform GAII(x) at a high depth of ~200×. We observed that most function-related diverse variants in the human exonic regions could be detected at a sequencing depth of 120×. Furthermore, investigation using a diagnostic gene set showed that the number of clinical variants identified using exome sequencing reached a plateau at an average sequencing depth of about 120×. Moreover, the phenomena were consistent across the breast cancer samples.