Exploring Eye, Hair, and Skin Pigmentation in a Spanish Population: Insights from Hirisplex-S Predictions

Abstract

Background/objectives: Understanding and predicting human pigmentation traits is crucial for individual identification. Genome-wide association studies have revealed numerous pigmentation-associated SNPs, indicating genetic overlap among pigmentation traits and offering the potential to develop predictive models without the need for analyzing large numbers of SNPs. Methods: In this study, we assessed the performance of the HIrisPlex-S system, which predicts eye, hair, and skin color, on 412 individuals from the Spanish population. Model performance was calculated using metrics including accuracy, area under the curve, sensitivity, specificity, and positive and negative predictive value. Results: Our results showed high prediction accuracies (70% to 97%) for blue and brown eyes, brown hair, and intermediate skin. However, challenges arose with the remaining categories. The model had difficulty distinguishing between intermediate eye colors and similar shades of hair and exhibited a significant percentage of individuals with incorrectly predicted dark and pale skin, emphasizing the importance of careful interpretation of final predictions. Future studies considering quantitative pigmentation may achieve more accurate predictions by not relying on categories. Furthermore, our findings suggested that not all previously established SNPs showed a significant association with pigmentation in our population. For instance, the number of markers used for eye color prediction could be reduced to four while still maintaining reasonable predictive accuracy within our population. Conclusions: Overall, our results suggest that it may be possible to reduce the number of SNPs used in some cases without compromising accuracy. However, further validation in larger and more diverse populations is essential to draw firm conclusions and make broader generalizations.