- Research article
- Open Access
Genetic associations and shared environmental effects on the skin microbiome of Korean twins
© Si et al. 2015
- Received: 17 June 2015
- Accepted: 22 October 2015
- Published: 23 November 2015
The skin is the outermost layer of the human body and one of the key sites for host-microbe interactions. Both environmental and host genetic factors influence microbial communities in distinct anatomical niches, but little is known about their interplay in shaping the skin microbiome. Here, we investigate the heritable components of the skin microbiome and their association with host genetic factors.
Based on our analysis of the microbiota from 45 individuals including monozygotic and dizygotic twins aged 26–55 years and their mothers, we found that skin microbial diversity was significantly influenced by age and skin pigmentation. Heritability analysis revealed genetic and shared environmental impacts on the skin microbiome. Furthermore, we observed a strong association between the abundance of Corynebacterium jeikeium and single nucleotide polymorphisms (SNPs) in the host FLG gene related to epidermal barrier function.
This study reveals an intimate association of the human skin microbiome and host genes, and increases our understanding of the role of human genetic factors in establishing a microbial ecosystem on the body surface.
- Skin microbiota
- Twin study
- Host genetics
- Common environment effect
The skin provides a protective barrier against invading microbial pathogens, while also serving as a habitat for a plethora of commensal bacteria. The composition of skin microbiota varies among individuals, but a core set of microbes can be found in all subjects for each anatomical location [1–3]. Recent advances in high-throughput sequencing technologies have enabled rapid analysis of microbial communities indigenous to specific niches in the human body . There is significant inter-individual variation in the skin microbiota, which is attributable to genetic differences and environmental factors. Colonization of the host body by bacteria occurs immediately after birth, with more complex microbial communities developing in distinct anatomical niches afterwards . Succession of the microbiota within individual hosts is also driven by host-intrinsic factors, such as age, gender, genotype and health status, as well as environmental and lifestyle factors, such as climate, light exposure, and detergent use [6, 7]. Previous studies have quantitatively explored host genetic effects on the gut microbiome composition using animal models and humans [8–11]. These studies revealed a significant influence of host genetic factors on the microbiota. However, the impact of host gene expression on skin microbial diversity in human subjects has not been fully investigated.
The majority of complex human traits are governed by multiple gene interactions . These sophisticated interactions differ among individuals based on environmental factors. Studies on twins are therefore a useful method for estimating the genetic and environmental effects that a given factor has on human health . Twin heritability and linkage studies segregate the genetic and environmental effects based on the assumption that monozygotic twins are genetically identical, and so their phenotypic differences are of environmental origin. Additionally, the shared environmental effects can be determined from monozygotic (MZ) and dizygotic (DZ) twins, as well as parent-offspring pairs . Our knowledge of individual differences in the skin microbiota and genetic markers associated with its composition remains limited. In this study, we analyzed the skin microbiota of 16 MZ and 8 DZ twins between 26 and 55 years of age and their mothers (n = 16) to identify the heritable components of the skin microbiome and their association with host genetic factors. To this end, we focused our analysis on host genes related to key dermatological conditions, including sebum production, skin humidity, pigmentation, epidermal barrier function, and hair follicle development. Using the microbial composition as quantitative traits, we identified one human single nucleotide polymorphisms (SNPs) strongly associated with the abundance of Corynebacterium jeikeium on the skin.
Study population and composition of the human skin microbiome
Summary of the study population (N = 45)
Age, mean (SD), y
Humidity (SD), AUbc
Pigmentation (SD), AUbd
To explore the skin microbe-host interaction further, we used PICRUSt , an algorithm that detects the functional capabilities of a community by comparing its metagenome with reference genomes using microbial communities identified from all subjects. PICRUSt classified 200 functional pathways from the 16S rRNA sequences of human skin microbiota.
Functional traits determined based on 16S rRNA genes showed categorical similarity with a recent skin metagenomic study (Fig. 1b), with a large proportion of the functions associated with carbohydrate, amino acid, vitamin, and nucleotide metabolism . Discrepancies in the relative contribution of each pathway between the studies could be due to use of different sampling sites and analytical methods. The previous metagenomic study reflects the microbial functions from two male subjects at mid-twenties with samples from five different body parts. Additionally, 16S rRNA based predictions of the functional traits by PICRUSt could possibly cause the discrepancies. Detailed categories of the functional traits and Nearest Sequenced Taxon Index (NTSI) values are provided in Additional file 1: Table S1 and Additional file 1: Table S2, respectively.
Diversity of the human skin microbiome
Heritability of the skin microbiota
Heritability and household effects of the skin microbiota after adjustment for age and gender
Genetic Effect (95 % CI)
Common Environment Effect (95 % CI)
Individual Effect (95 % CI)
Genetic association of skin microbiotas
Associations between the skin microbiota and SNPs of targeted human genes after adjustment for age and gender
95 % CI
LDA effect sizec
Skin barrier function
In this study, we describe host genetic factors and other host-intrinsic characteristics that directly influence the composition of the skin microbiota. We further demonstrated a strong association between the composition of skin microbiota and human genetic factors related to skin barrier function. Analysis of genetically identical MZ twins and half-identical DZ twins can be used to identify and discriminate genetic and environmental effects on the microbiome . Investigation of the twin pairs proved that the skin microbiome is shaped both by host genetics and their environmental factors. Additionally, analysis of human genetic traits associated with the skin microbiota was performed using a candidate-gene approach. For this investigation, we chose five representative traits that affect dermatologic conditions; namely, sebum production, pigmentation, skin humidity, skin barrier function, and hair follicle formation. To confirm the results of the association analysis, the bacterial abundance associated with host alleles was further analyzed using LEfSe. One SNP, located in a gene related to skin barrier function, was significantly associated with C. jeikeium. C. jeikeium abundance was lower in subjects containing the minor allele of FLG, which encodes filaggrin, a structural protein in the cornified envelop of the stratum corneum critical for skin barrier function.
Although we have explored intrinsic characteristics of the human skin microbiome in aspect of host genetics and environmental factors, our study has statistical limitations from a small sample size. In genetic association studies, a sufficient number of samples are critical to detect causality between genes and phenotypes. Furthermore, the collective size of bacteria identified by microbiome analysis creates more stringent p-values to achieve asking for increased sample size to identify additional links between host genetic factors and the composition of the skin microbiota. To overcome such limitations, we tried to assess the genetic impacts put on the skin microbiome using different analytical approaches and the results are supportive of our findings. Yet, more expansive genome-wide association studies with increased sample size are warranted to identify additional links between host genetic factors and the composition of the skin microbiota. Our results provide insight into skin traits associated with individual’s microbiome and potential genomic and microbial targets for skin healthcare.
Study population and sample collection
Study subjects were either MZ or DZ twins, and their mothers were recruited for the Healthy Twin Study as part of the Korean Genome Epidemiology Study  between September 2010 and August 2011. Zygosity of twins was confirmed using either 16 short tandem repeat (STR) markers (15 autosomal markers and 1 sex-determining marker) (67 %) or a self-administered zygosity questionnaire (33 %), which showed >90 % accuracy . A total of 51 trios were initially selected, but 6 subjects were excluded due to intake of antibiotics within 3 months of sample collection. Thus, the final sample size was 45 individuals including 8 MZ twin pairs, 4 DZ twin pairs, and 21 family members; the members were composed of 5 parent-offspring pairs and 11 mothers of the twin pairs. Age of the mothers ranged from 49–79 years, and twin children and singletons ranged from 26–55 years. Two subjects exhibited mild symptoms of atopy, but were not excluded as they were only reported to have such symptoms and not prescribed any medication. Dermatological phenotypes such as pigmentation and humidity were available from 32 participants. The participants provided their written informed consent to participate in this study. All experiments involving human subjects were approved by the Korea Centers for Disease Control and the Institutional Review Board of the Seoul National University (IRB No. 144–2011–07–11).
The inner wrist of the right arm was swabbed with two sterile cotton swabs moistened with ST solution (0.15 M NaCl with 0.1 % Tween 20) . The number of strokes was 4–5 times per sampling with gentle pressure. The heads of the cotton swabs were stored in 100 μl of ST solution at −80 °C until use. Swab samples were collected after 3-hr medical checkup securing the time from contact with water and soap. Additionally, skin phenotype—including pigmentation and humidity—were determined using different probes with a C + K multi probe adapter MPA 9 (Courage + Khazaka Electronic GmbH, Köln, Germany) on the same date. Sample collection and visiting was done at set times strictly. Skin pigmentation was measured from the flexor surface of right arm using a Mexameter® MX 18, as described previously . The device quantifies the ratio of light emitted and reflected by skin chromophores and calculates the amount of melanin. Melanin levels were expressed as the melanin index, which ranged between 0 and 150 arbitrary units (AU) for light skin tone, and 150 and 250 AU for mid-tone, skin pigmentation. For skin humidity, capacitance measurement was performed using a Corneometer®CM825 . Each phenotype was measured three times at 18–23 °C and 40–60 % relative humidity. Corneometry values of greater than 45 AU indicate sufficiently moisturized skin, and values less than 45 AU were considered dry skin. The categories of each trait followed the manufacturer’s instructions. The average value of the repeated measurements was used unless the measurements differed by more than 10 %. In this case, the median of the three measurements was taken.
DNA extraction and 454 pyrosequencing
Total genomic DNA was extracted following the bead-beating extraction protocol . Briefly, the cotton swab and ST solution were added to 500 μL of extraction buffer (200 mM NaCl, 200 mM Tris, and 20 mM EDTA; pH 8), 500 μL of phenol:chloroform:isoamyl-alcohol (25:24:1; pH 7.9) (Sigma, Steinheim, Germany), 210 μL of 20 % SDS, and 500 μL of zirconia-silica beads (0.1 mm in diameter; Biospec Products Inc., Bartlesville, OK). The mixture was homogenized using a Vortex Adaptor (MoBio Laboratories, Solana Beach, CA) for 2 min at room temperature. DNA extraction was performed with 500 μL of phenol: chloroform: isoamyl-alcohol (25:24:1; pH 7.9), followed by isopropanol precipitation. The nucleic acid solutions were stored at −70 °C until use. The V2 and V3 regions of the 16S rRNA genes were amplified from total DNA, extracted, and pyrosequenced as described previously . The experiments were quality controlled using proper negative and positive controls at the stage of DNA extraction and PCR amplification, respectively. The cotton swabs without swabbing the inner arm were used as the negative controls. PCR amplification of these controls did not amplify any bacterial DNA, thus they were not used for subsequent sequence analysis. The positive controls used were DNA from E.coli at the stage of PCR amplification. The sequence data have been submitted to the EMBL databases under accession number PRJEB5864 (http://www.ebi.ac.uk/ena).
Bioinformatic analysis using 16S rRNA sequences
Sequence data were analyzed using the QIIME software package (version 1.5.0) . Before the sequence analysis, quality filtering was performed including removal of low-quality sequences (<200 bp) and ambiguous reads and end-trimming. Subsequently, homopolymers were removed by denoising the sequence data set in the QIIME pipeline . Representative sequence sets were chosen using UCLUST and clustered at the 97 % sequence similarity level. Processed sequences were aligned using PyNAST , and taxonomy was assigned using the ribosomal database project (RDP) classifier , and the Greengenes Database (gg_97_otus_4feb2011.fasta) was used as the reference . The minimum confidence score for the taxonomy assignment to sequences was 0.8. Chimera sequences (10.55 %) were excluded from downstream analyses prior to the generation of phylogenic trees or OTU tables using ChimeraSlayer algorithm . Bacterial diversity both within and between samples was assessed through alpha using Chao1 measure  and beta diversity using weighted and unweighted UniFrac distances . Phylogenetic tree was generated using the FastTree method . UniFrac metrics were also used to identify differences between sample pairs. Analysis of similarity (ANOSIM) was performed to test the statistical significance in the differences. Functional traits were determined from 16S-rRNA-based sequences using PICRUSt-0.9.1(http://picrust.github.io/picrust/) . Unclassified pathways were excluded from the functional analysis.
Heritability estimates of each skin microbe were calculated by variance component methods using Sequential Oligogenic Linkage Analysis Routines (SOLAR, version 6.6.2; Southwest Foundation for Biomedical Research, San Antonio, TX, USA) . Skin bacterial abundances at all taxonomic levels were used as quantitative traits. As the bacterial abundances were not normally distributed, inverse normal transformation was applied to the traits before the heritability analysis. Additionally, the monozygotic twins were separately categorized from the rest of participants. SOLAR uses a maximum-likelihood method, which allows incorporation of fixed covariate effects (age, gender, and interaction between age and gender). Also, the software allows working with extended families and complicated pedigree of different age and sex. Variance component analysis decomposes the total variation (σp2) into additive genetic effects (σa2) and residual non-genetic variance (individual effect; σe2), which can be further specified to unmeasured common environmental effects (σc2). We fitted the variance component model into additive genetic and non-genetic components and added the common environmental variance when the effects (σc2) were present (σp2 = σa2 + σc2 + σe2). Thus, the heritability was estimated as the ratio of variance attributed to the additive genetic components and the total variance (σa2 /σp2).
Skin traits associated with SNPs
For SNP genotyping, venous blood from each subject was collected and genomic DNA was extracted using the i-genomic Clinic DNA Extraction Kit (Intron, Seongnam, South Korea). Genotyping was performed on Affymetrix Genome Wide Human SNP Array 6.0 (Affymetrix, Inc., Santa Clara, CA). Exclusion criteria included SNPs with Minor Allele Frequency < 0.01, Hardy Weinberg Equilibrium < 0.001, low call rate (<95 %), Mendelian error, and non-Mendelian error. Mendelian and non-Mendelian errors were identified using the PEDSTATS 0.6.12  and Merlin 1.1.2  software.
Association analysis of skin microbiotas was performed using Plink 1.07 (http://pngu.mgh.harvard.edu/purcell/plink/) , as described previously . Briefly, the QFAM (family-based test of association for quantitative traits) model was used to analyze SNPs obtained from skin-trait related genes (Additional file 1: Table S4). Candidate genes were selected based on their roles in regulatory functions or skin metabolism. The list of SNPs was obtained from the Single Nucleotide Polymorphism Database (dbSNP; Build 137) . Skin bacterial abundances at all taxonomic levels were used as quantitative traits. QFAM uses a simple linear regression and corrects for family structure based on adaptive permutation; in this study, 100,000 permutations were performed. This adaptive permutation also alleviates the assumption about normality of data set. Prior to analysis, the bacterial abundances were adjusted for age and gender by fitting to a regression model in R version 3.0.2 , as QFAM does not allow for covariates. For MZ twins, only one individual from each pair was analyzed. The alleles of the significant SNPs were examined with the bacterial abundances using LEfSe (linear discriminant analysis [LDA] coupled with effect size measurements) software . The linear discriminant analysis (LDA) effect size greater than 2 was used as the threshold for discriminative bacteria.
Species richness and UniFrac distances were analyzed by the Wilcoxon rank sum test (two-tailed) using R version 3.0.2 . Correlation test of skin pigmentation was performed by the Pearson correlation with the SPSS software, ver. 21 (Armonk, NY, US). Association of host genotype with skin humidity was tested using the Kruskal-Willis test.
This work was supported by a National Research Foundation of Korea (NRF) grant funded by the Korean government (MEST) (No. 2010–0029113).
Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
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