Human skin color is highly heritable and externally visible with relevance in medical, forensic, and anthropological genetics. Although eye and hair color can already be predicted with high accuracies from small sets of carefully selected DNA markers, knowledge about the genetic predictability of skin color is limited. The current study identified a minimal set of 36 highly informative skin color predictive SNPs and developed a statistical prediction model capable of skin color prediction on a global scale. Average cross-validated prediction accuracies expressed as area under the receiver-operating characteristic curve (AUC) +/- standard deviation were 0.97 +/- 0.02 for Light, 0.83 +/- 0.11 for Dark, and 0.96 +/- 0.03 for Dark-Black. When using a 5-category, this resulted in 0.74 +/- 0.05 for Very Pale, 0.72 +/- 0.03 for Pale, 0.73 +/- 0.03 for Intermediate, 0.87+/- 0.1 for Dark, and 0.97 +/- 0.03 for Dark-Black. A comparative analysis in 194 independent samples from 17 populations demonstrated that this model outperformed a previously proposed 10-SNP-classifier approach with AUCs rising from 0.79 to 0.82 for White, comparable at the intermediate level of 0.63 and 0.62, respectively, and a large increase from 0.64 to 0.92 for Black. Overall, this study demonstrates that the chosen DNA markers and prediction model, particularly the 5-category level; enable skin color predictions within and between continental regions for the first time, which will serve as a valuable resource for future applications in forensic and anthropologic genetics. (Publisher abstract modified)
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