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

    March 2022 in “ Clinical Cosmetic and Investigational Dermatology ”
    Hye-Young Yoo, Ki‐Chan Lee, Ji‐Eun Woo, Sung‐Ha Park, Sunghoon Lee, Joungsu Joo, Jin-Sik Bae, Hyuk‐Jung Kwon, Byoung‐Jun Park
    TLDR The model accurately predicts skin conditions in Korean women using genetic information, aiding personalized skincare.
    The study conducted on 749 Korean women aimed to predict skin phenotypes from genotypes using genome-wide association studies (GWAS) and machine-learning algorithms. Researchers identified 46 novel single-nucleotide polymorphisms (SNPs) significantly associated with skin traits such as melanin, gloss, hydration, wrinkles, and elasticity. Ridge regression emerged as the best-performing model for predicting these phenotypes. The study highlighted the potential of using genetic information to predict skin conditions and develop personalized cosmetics, although it noted the need for further research to fully understand the genetic influences. Despite the smaller sample size compared to other GWAS studies, the findings provided valuable insights specific to the Korean population.
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