Main Article Content


Facial skin phenotypes such as pigmentation, wrinkles, and lentigines are determined by genetics and environmental factors. The genetic factor is known as the basic formation of facial skin phenotype variations in different populations. Genetic variations can be used as an approach to understanding genetic influences on the phenotype of interest and may influence molecular and cellular mechanisms. Genome wide-association study (GWAS) is used to identify common genetic variants associated with quantitative traits or complex human diseases. GWAS reveals a single phenotypic which is associated with a large number of SNPs and provides statistical information as significant SNPs. To perform GWAS analysis, bioinformatics tools are used as a rapid genetic data computation for large biological datasets which can report gene/locus with a related biological function viewed in silico. In this review, we summarize a common step-by-step workflow to conduct GWAS using bioinformatics analysis. The step-by-step workflow helps researchers understand how to identify SNPs with phenotypes of interest using bioinformatics approaches. Then, we explore common SNP and gene of facial skin phenotypes from several populations originating from various countries that can provide insights into the genetic contributions to facial skin phenotypes.


GWAS genetics phenotype facial skin

Article Details

How to Cite
Rusma Yulita, Aswin YA, Paramita RI. Genome-Wide Association Study for the Identification of Genetic Variants Associated with Facial Skin Phenotypes. EKSAKTA [Internet]. 2024May18 [cited 2024Jun.21];25(02):164-76. Available from:


  1. Wong, Q. Y. A., & Chew, F. T. (2021). Defining skin aging and its risk factors: a systematic review and meta-analysis. Scientific reports, 11(1), 22075.
  2. Du, Y., Doraiswamy, C., Mao, J., Zhang, Q., Liang, Y., Du, Z., ... & Joshi, M. K. (2022). Facial skin characteristics and concerns in Indonesia: a cross‐sectional observational study. Skin Research and Technology, 28(5), 719-728.
  3. Solanki, V., Dongre, A., & Nayak, C. (2024). A clinico-epidemiological study of different dermoscopic patterns in hyperpigmented facial lesions in a tertiary care centre. Journal of Cutaneous and Aesthetic Surgery, 17(2), 112-123.
  4. Endo, C., Johnson, T. A., Morino, R., Nakazono, K., Kamitsuji, S., Akita, M., ... & Kawashima, M. (2018). Genome-wide association study in Japanese females identifies fifteen novel skin-related trait associations. Scientific reports, 8(1), 8974.
  5. Lu, Y., Wang, G., Liu, D., & Tian, J. (2020). Anti-wrinkle effect of a palmitoyl oligopeptide complex on human keratinocytes and fibroblasts through TGF-β1 pathway. Cell Biol, 8(2), 33.
  6. Parrado, C., Mercado-Saenz, S., Perez-Davo, A., Gilaberte, Y., Gonzalez, S., & Juarranz, A. (2019). Environmental stressors on skin aging. Mechanistic insights. Frontiers in pharmacology, 10, 759.
  7. Lona-Durazo, F., Hernandez-Pacheco, N., Fan, S., Zhang, T., Choi, J., Kovacs, M. A., ... & Parra, E. J. (2019). Meta-analysis of GWA studies provides new insights on the genetic architecture of skin pigmentation in recently admixed populations. BMC genetics, 20, 1-16.
  8. Hamer, M., Pardo Cortes, L., Jacobs, L., Deelen, J., Uitterlinden, A., Slagboom, E., ... & Nijsten, T. (2018). Facial wrinkles in Europeans: a genome-wide association study. The Journal of Investigative Dermatology.
  9. Mitchell, B. L., Saklatvala, J. R., Dand, N., Hagenbeek, F. A., Li, X., Min, J. L., ... & Simpson, M. A. (2022). Genome-wide association meta-analysis identifies 29 new acne susceptibility loci. Nature communications, 13(1), 702.
  10. Beck, T., Shorter, T., & Brookes, A. J. (2020). GWAS Central: a comprehensive resource for the discovery and comparison of genotype and phenotype data from genome-wide association studies. Nucleic acids research, 48(D1), D933-D940.
  11. Hettiarachchi, G., & Komar, A. A. (2022). GWAS to identify SNPs associated with common diseases and individual risk: Genome Wide Association Studies (GWAS) to identify SNPs associated with common diseases and individual risk. In Single Nucleotide Polymorphisms: Human Variation and a Coming Revolution in Biology and Medicine (pp. 51-76). Cham: Springer International Publishing.
  12. Liu, P. H., Chuang, G. T., Hsiung, C. N., Yang, W. S., Ku, H. C., Lin, Y. C., ... & Chang, Y. C. (2022). A genome-wide association study for melatonin secretion. Scientific Reports, 12(1), 8025.
  13. Yoo, H. Y., Lee, K. C., Woo, J. E., Park, S. H., Lee, S., Joo, J., ... & Park, B. J. (2022). A Genome-Wide Association Study and Machine-Learning Algorithm Analysis on the Prediction of Facial Phenotypes by Genotypes in Korean Women. Clinical, Cosmetic and Investigational Dermatology, 433-445.
  14. Sepetiene, R., Patamsyte, V., Valiukevicius, P., Gecyte, E., Skipskis, V., Gecys, D., ... & Barakauskas, S. (2023). Genetical signature—An example of a personalized skin aging investigation with possible implementation in clinical practice. Journal of Personalized Medicine, 13(9), 1305.
  15. Chiarella, P., Capone, P., & Sisto, R. (2023). Contribution of genetic polymorphisms in human health. International Journal of Environmental Research and Public Health, 20(2), 912.
  16. Cano-Gamez, E., & Trynka, G. (2020). From GWAS to function: using functional genomics to identify the mechanisms underlying complex diseases. Frontiers in genetics, 11, 505357.
  17. Visscher, P. M., Yengo, L., Cox, N. J., & Wray, N. R. (2021). Discovery and implications of polygenicity of common diseases. Science, 373(6562), 1468-1473.
  18. Sirugo, G., Williams, S. M., & Tishkoff, S. A. (2019). The missing diversity in human genetic studies. Cell, 177(1), 26-31.
  19. Peterson, R. E., Kuchenbaecker, K., Walters, R. K., Chen, C. Y., Popejoy, A. B., Periyasamy, S., ... & Duncan, L. E. (2019). Genome-wide association studies in ancestrally diverse populations: opportunities, methods, pitfalls, and recommendations. Cell, 179(3), 589-603.
  20. Politi, C., Roumeliotis, S., Tripepi, G., & Spoto, B. (2023). Sample size calculation in genetic association studies: a practical approach. Life, 13(1), 235.
  21. Shankar, R., Dwivedi, V., & Arya, G. C. (2021). Relevance of Bioinformatics and Database in Omics Study. Omics Technologies for Sustainable Agriculture and Global Food Security Volume 1, 19-39.
  22. Lazarenko, V., Churilin, M., Azarova, I., Klyosova, E., Bykanova, M., Ob’edkova, N., ... & Polonikov, A. (2022). Comprehensive Statistical and Bioinformatics Analysis in the Deciphering of Putative Mechanisms by Which Lipid-Associated GWAS Loci Contribute to Coronary Artery Disease. Biomedicines, 10(2), 259.
  23. Mengist, W., Soromessa, T., & Legese, G. (2020). Ecosystem services research in mountainous regions: A systematic literature review on current knowledge and research gaps. Science of the Total Environment, 702, 134581.
  24. Yang, X. X., Zhao, M. M., He, Y. F., Meng, H., Meng, Q. Y., Shi, Q. Y., & Yi, F. (2022). Facial skin aging stages in Chinese females. Frontiers in Medicine, 9, 870926.
  25. Chen, Y., André, M., Adhikari, K., Blin, M., Bonfante, B., Mendoza‐Revilla, J., ... & Ruiz‐Linares, A. (2021). A genome‐wide association study identifies novel gene associations with facial skin wrinkling and mole count in Latin Americans. British Journal of Dermatology, 185(5), 988-998.
  26. Gao, W., Tan, J., Hüls, A., Ding, A., Liu, Y., Matsui, M. S., ... & Wang, S. (2017). Genetic variants associated with skin aging in the Chinese Han population. Journal of dermatological science, 86(1), 21-29.
  27. Yadufashije, D. C., & Samuel, R. (2019). Genetic and environmental factors in skin color determination. African Journal of Biological Sciences, 1(2), 51-54.
  28. Markiewicz, E., & Idowu, O. C. (2022). Evaluation of Personalized Skincare Through in-silico Gene Interactive Networks and Cellular Responses to UVR and Oxidative Stress. Clinical, Cosmetic and Investigational Dermatology, 2221-2243.
  29. Shin, J. G., Leem, S., Kim, B., Kim, Y., Lee, S. G., Song, H. J., ... & Kang, N. G. (2021). GWAS analysis of 17,019 Korean women identifies the variants associated with facial pigmented spots. Journal of Investigative Dermatology, 141(3), 555-562.
  30. Uffelmann, E., Huang, Q. Q., Munung, N. S., De Vries, J., Okada, Y., Martin, A. R., ... & Posthuma, D. (2021). Genome-wide association studies. Nature Reviews Methods Primers, 1(1), 59.
  31. DeStefano, G. M., & Christiano, A. M. (2014). The genetics of human skin disease. Cold Spring Harbor perspectives in medicine, 4(10), a015172.
  32. Alqudah, A. M., Sallam, A., Baenziger, P. S., & Börner, A. (2020). GWAS: fast-forwarding gene identification and characterization in temperate cereals: lessons from barley–a review. Journal of advanced research, 22, 119-135.
  33. Laurie, C. C., Doheny, K. F., Mirel, D. B., Pugh, E. W., Bierut, L. J., Bhangale, T., ... & GENEVA Investigators. (2010). Quality control and quality assurance in genotypic data for genome‐wide association studies. Genetic epidemiology, 34(6), 591-602.
  34. Bruijns, B., Hoekema, T., Oomens, L., Tiggelaar, R., & Gardeniers, H. (2022). Performance of spectrophotometric and fluorometric DNA quantification methods. Analytica, 3(3), 371-384.
  35. Lutz, Í., Miranda, J., Santana, P., Martins, T., Ferreira, C., Sampaio, I., ... & Gomes, G. E. (2023). Quality analysis of genomic DNA and authentication of fisheries products based on distinct methods of DNA extraction. Plos one, 18(2), e0282369.
  36. Kockum, I., Huang, J., & Stridh, P. (2023). Overview of genotyping technologies and methods. Current Protocols, 3(4), e727.
  37. Patel, H., Lee, S. H., Breen, G., Menzel, S., Ojewunmi, O., & Dobson, R. J. (2022). The COPILOT raw Illumina genotyping QC protocol. Current Protocols, 2(4), e373.
  38. Isidro-Sánchez, J., Akdemir, D., & Montilla-Bascón, G. (2017). Genome-wide association analysis using R. Oat: Methods and Protocols, 189-207.
  39. Purcell, S., Neale, B., Todd-Brown, K., Thomas, L., Ferreira, M. A., Bender, D., ... & Sham, P. C. (2007). PLINK: a tool set for whole-genome association and population-based linkage analyses. The American journal of human genetics, 81(3), 559-575.
  40. Zhao, S., Jing, W., Samuels, D. C., Sheng, Q., Shyr, Y., & Guo, Y. (2018). Strategies for processing and quality control of Illumina genotyping arrays. Briefings in bioinformatics, 19(5), 765-775.
  41. Reed, E., Nunez, S., Kulp, D., Qian, J., Reilly, M. P., & Foulkes, A. S. (2015). A guide to genome‐wide association analysis and post‐analytic interrogation. Statistics in medicine, 34(28), 3769-3792.
  42. Marees, A. T., de Kluiver, H., Stringer, S., Vorspan, F., Curis, E., Marie‐Claire, C., & Derks, E. M. (2018). A tutorial on conducting genome‐wide association studies: Quality control and statistical analysis. International journal of methods in psychiatric research, 27(2), e1608.
  43. Bush, W. S., & Moore, J. H. (2012). Chapter 11: Genome-wide association studies. PLoS computational biology, 8(12), e1002822.
  44. De Vries, P. S., Sabater-Lleal, M., Chasman, D. I., Trompet, S., Ahluwalia, T. S., Teumer, A., ... & Dehghan, A. (2017). Comparison of HapMap and 1000 genomes reference panels in a large-scale genome-wide association study. PloS one, 12(1), e0167742.
  45. Adam, Y., Samtal, C., Brandenburg, J. T., Falola, O., & Adebiyi, E. (2021). Performing post-genome-wide association study analysis: overview, challenges and recommendations. F1000Research, 10.
  46. Fachal, L., & Dunning, A. M. (2015). From candidate gene studies to GWAS and post-GWAS analyses in breast cancer. Current opinion in genetics & development, 30, 32-41.
  47. Martin, P. (2011). Bioinformatics Approaches for the Post-GWAS Analysis of Disease Susceptibility Loci.
  48. Rahmouni, M., Laville, V., Spadoni, J. L., Jdid, R., Eckhart, L., Gruber, F., ... & Zagury, J. F. (2022). Identification of New Biological Pathways Involved in Skin Aging From the Analysis of French Women Genome-Wide Data. Frontiers in Genetics, 13, 836581.
  49. Mitchell, B. L., Saklatvala, J. R., Dand, N., Hagenbeek, F. A., Li, X., Min, J. L., ... & Simpson, M. A. (2022). Genome-wide association meta-analysis identifies 29 new acne susceptibility loci. Nature communications, 13(1), 702.
  50. Budu-Aggrey, A., Kilanowski, A., Sobczyk, M. K., 23andMe Research Team, Shringarpure, S. S., Mitchell, R., ... & Paternoster, L. (2023). European and multi-ancestry genome-wide association meta-analysis of atopic dermatitis highlights importance of systemic immune regulation. Nature Communications, 14(1), 6172.
  51. Bejaoui, Y., Witte, M., Abdelhady, M., Eldarouti, M., Abdallah, N. M., Elghzaly, A. A., ... & Ibrahim, S. M. (2019). Genome‐wide association study of psoriasis in an Egyptian population. Experimental Dermatology, 28(5), 623-627.
  52. Liu, Y., Gao, W., Koellmann, C., Le Clerc, S., Hüls, A., Li, B., ... & Wang, S. (2019). Genome-wide scan identified genetic variants associated with skin aging in a Chinese female population. Journal of dermatological science, 96(1), 42-49.
  53. Visconti, A., Duffy, D. L., Liu, F., Zhu, G., Wu, W., Chen, Y., ... & Falchi, M. (2018). Genome-wide association study in 176,678 Europeans reveals genetic loci for tanning response to sun exposure. Nature Communications, 9(1), 1684.
  54. Farage, M. A., Jiang, Y., Tiesman, J. P., Fontanillas, P., & Osborne, R. (2020). Genome-wide association study identifies loci associated with sensitive skin. Cosmetics, 7(2), 49.
  55. Kim, J. O., Park, B., Choi, J. Y., Lee, S. R., Yu, S. J., Goh, M., ... & Hong, K. W. (2021). Identification of the Underlying Genetic Factors of Skin Aging in a Korean Population Study. Journal of cosmetic science, 72(1).
  56. Park, S., Kang, S., & Lee, W. J. (2021). Menopause, ultraviolet exposure, and low water intake potentially interact with the genetic variants related to collagen metabolism involved in skin wrinkle risk in middle-aged women. International Journal of Environmental Research and Public Health, 18(4), 2044.
  57. Shan, M. A., Meyer, O. S., Refn, M., Morling, N., Andersen, J. D., & Børsting, C. (2021). Analysis of skin pigmentation and genetic ancestry in three subpopulations from Pakistan: Punjabi, Pashtun, and Baloch. Genes, 12(5), 733.
  58. Cha, M. Y., Choi, J. E., Lee, D. S., Lee, S. R., Lee, S. I., Park, J. H., ... & Hong, K. W. (2022). Novel Genetic Associations for Skin Aging Phenotypes and Validation of Previously Reported Skin GWAS Results. Applied Sciences, 12(22), 11422.
  59. Liu, F., Hamer, M. A., Deelen, J., Lall, J. S., Jacobs, L., van Heemst, D., ... & Gunn, D. A. (2016). The MC1R gene and youthful looks. Current Biology, 26(9), 1213-1220.
  60. Flood, K. S., Houston, N. A., Savage, K. T., & Kimball, A. B. (2019). Genetic basis for skin youthfulness. Clinics in dermatology, 37(4), 312-319.
  61. Chang, A. L., Atzmon, G., Bergman, A., Brugmann, S., Atwood, S. X., Chang, H. Y., & Barzilai, N. (2014). Identification of genes promoting skin youthfulness by genome-wide association study. Journal of Investigative Dermatology, 134(3), 651-657.
  62. Del Bino, S., Duval, C., & Bernerd, F. (2018). Clinical and biological characterization of skin pigmentation diversity and its consequences on UV impact. International journal of molecular sciences, 19(9), 2668.
  63. Law, M. H., Medland, S. E., Zhu, G., Yazar, S., Viñuela, A., Wallace, L., ... & MuTHER Consortium. (2017). Genome-wide association shows that pigmentation genes play a role in skin aging. Journal of Investigative Dermatology, 137(9), 1887-1894.
  64. TPCN, P. (2011). Development of lentigines in German and Japanese women correlates with variants in the SLC45A2 gene. Journal of investigative dermatology
  65. Bastiaens, M., ter Huurne, J., Gruis, N., Bergman, W., Westendorp, R., Vermeer, B. J., & Bavinck, J. N. B. (2001). The melanocortin-1-receptor gene is the major freckle gene. Human Molecular Genetics, 10(16), 1701-1708.
  66. Matsui, S., Funahashi, M., Honda, A., & Shimozawa, N. (2013). Newly identified milder phenotype of peroxisome biogenesis disorder caused by mutated PEX3 gene. Brain and Development, 35(9), 842-848.
  67. Wills, M. K., Lau, H. R., & Jones, N. (2017). The ShcD phosphotyrosine adaptor subverts canonical EGF receptor trafficking. Journal of Cell Science, 130(17), 2808-2820.
  68. Farage, M. A., Miller, K. W., Elsner, P., & Maibach, H. I. (2008). Intrinsic and extrinsic factors in skin ageing: a review. International journal of cosmetic science, 30(2), 87-95.
  69. Naval, J., Alonso, V., & Herranz, M. A. (2014). Genetic polymorphisms and skin aging: the identification of population genotypic groups holds potential for personalized treatments. Clinical, cosmetic and investigational dermatology, 207-214.
  70. Gunn, D. (2016). The genetics of skin ageing. Curr Biol, 26(9), 1213-20.

Most read articles by the same author(s)