Oligogenic prediction of eye and hair colour in the Danish population

Abstract

Abstract Genetic predictions of external traits are commonplace in current research, but little is known about the performance of such predictions in Nordic populations. Here, we perform eye and hair colour prediction in a sample of 723 Danish high school students using the oligogenic HIrisPlex method. We analysed the genetic structure of the Danish population by use of principal component analysis. Hair and eye colour predictions were carried out with an R script that was based on the HIrisPlex method. We predicted brown, blue and green colour with an accuracy of 92.17%, 98.38% and 0.73%, respectively. We also obtained accuracies of 80.95% for black, 93.72% for blond, 6.16% for brown and 53.33% for red hair colour. Our predictions were overall less accurate than previously reported in the HirisPlex study. The dissimilarities might be explained by differences in genetic ancestry between training and target samples. Due to the complexity eye and hair colour present as traits, a polygenic risk model is more suitable for their prediction.