Model-based prediction of human hair color using DNA variants

Abstract

Predicting complex human phenotypes from genotypes is the central concept of widely advocated personalized medicine, but so far has rarely led to high accuracies limiting practical applications. One notable exception, although less relevant for medical but important for forensic purposes, is human eye color, for which it has been recently demonstrated that highly accurate prediction is feasible from a small number of DNA variants. Here, we demonstrate that human hair color is predictable from DNA variants with similarly high accuracies. We analyzed in Polish Europeans with single-observer hair color grading 45 single nucleotide polymorphisms (SNPs) from 12 genes previously associated with human hair color variation. We found that a model based on a subset of 13 single or compound genetic markers from 11 genes predicted red hair color with over 0.9, black hair color with almost 0.9, as well as blond, and brown hair color with over 0.8 prevalence-adjusted accuracy expressed by the area under the receiver characteristic operating curves (AUC). The identified genetic predictors also differentiate reasonably well between similar hair colors, such as between red and blond-red, as well as between blond and dark-blond, highlighting the value of the identified DNA variants for accurate hair color prediction.

Fig. 1

Accuracy of hair color prediction using DNA variants in a Polish sample. AUC was plotted against the number of SNPs included in the multinomial logistic model for predicting 4 (a) and 7 (b) hair color categories. SNP annotation and prediction ranks are provided in Table 2

Fig. 2

ROC curves for the final model including 13 DNA predictors for 4 (a) and 7 (b) hair color categories in a Polish sample