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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.Electronic supplementary materialThe online version of this article (doi:10.1007/s00439-010-0939-8) contains supplementary material, which is available to authorized users.

Prediction of phenotypes from genetic data is considered to be the first practical application of data gained from association studies, with potential importance for medicine and the forensic sciences. Multiple genes and polymorphisms have been found to be associated with variation in human pigmentation. Their analysis enables prediction of blue and brown eye colour with a reasonably high accuracy. More accurate prediction, especially in the case of intermediate eye colours, may require better understanding of gene-gene interactions affecting this polygenic trait. Using multifactor dimensionality reduction and logistic regression methods, a study of gene-gene interactions was conducted based on variation in 11 known pigmentation genes examined in a cohort of 718 individuals of European descent. The study revealed significant interactions of a redundant character between the HERC2 and OCA2 genes affecting determination of hazel eye colour and between HERC2 and SLC24A4 affecting determination of blue eye colour. Our research indicates interactive effects of a synergistic character between HERC2 and OCA2, and also provides evidence for a novel strong synergistic interaction between HERC2 and TYRP1, both affecting determination of green eye colour.

Pigmentation is a complex physical trait with multiple genes involved. Several genes have already been associated with natural differences in human pigmentation. The SLC45A2 gene encoding a transporter protein involved in melanin synthesis is considered to be one of the most important genes affecting human pigmentation. Here we present results of an association study conducted on a population of European origin, where the relationship between two non-synonymous polymorphisms in the SLC45A2 gene -rs26722 (E272K) and rs16891982 (L374F) -and different pigmentation traits was examined. The study revealed a significant association between both variable sites and normal variation in hair colour. Only L374F remained significantly associated with hair colour when both SNPs were included in a logistic regression model. No association with other pigmentation traits was detected in this population sample. Our results indicate that the rare allele L374 significantly increases the possibility of having black hair colour (OR = 7.05) and thus may be considered as a future marker for black hair colour prediction.

BackgroundDNA analysis of ancient skeletal remains is invaluable in evolutionary biology for exploring the history of species, including humans. Contemporary human bones and teeth, however, are relevant in forensic DNA analyses that deal with the identification of perpetrators, missing persons, disaster victims or family relationships. They may also provide useful information towards unravelling controversies that surround famous historical individuals. Retrieving information about a deceased person’s externally visible characteristics can be informative in both types of DNA analyses. Recently, we demonstrated that human eye and hair colour can be reliably predicted from DNA using the HIrisPlex system. Here we test the feasibility of the novel HIrisPlex system at establishing eye and hair colour of deceased individuals from skeletal remains of various post-mortem time ranges and storage conditions.MethodsTwenty-one teeth between 1 and approximately 800 years of age and 5 contemporary bones were subjected to DNA extraction using standard organic protocol followed by analysis using the HIrisPlex system.ResultsTwenty-three out of 26 bone DNA extracts yielded the full 24 SNP HIrisPlex profile, therefore successfully allowing model-based eye and hair colour prediction. HIrisPlex analysis of a tooth from the Polish general Władysław Sikorski (1881 to 1943) revealed blue eye colour and blond hair colour, which was positively verified from reliable documentation. The partial profiles collected in the remaining three cases (two contemporary samples and a 14th century sample) were sufficient for eye colour prediction.ConclusionsOverall, we demonstrate that the HIrisPlex system is suitable, sufficiently sensitive and robust to successfully predict eye and hair colour from ancient and contemporary skeletal remains. Our findings, therefore, highlight the HIrisPlex system as a promising tool in future routine forensic casework involving skeletal remains, including ancient DNA studies, for the prediction of eye and hair colour of deceased individuals.

AimTo evaluate the accuracy of eye color prediction based on six IrisPlex single nucleotide polymorphisms (SNP) in a Slovenian population sample.MethodsSix IrisPlex predictor SNPs (HERC2 – rs12913832, OCA2 – rs1800407, SLC45A2 – rs16891982 and TYR – rs1393350, SLC24A4 – rs12896399, and IRF4 – rs12203592) of 105 individuals were analyzed using single base extension approach and SNaPshot chemistry. The IrisPlex multinomial regression prediction model was used to infer eye color probabilities. The accuracy of the IrisPlex was assessed through the calculation of sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and the area under the receiver characteristic operating curves (AUC).ResultsBlue eye color was observed in 44.7%, brown in 29.6%, and intermediate in 25.7% participants. Prediction accuracy expressed by the AUC was 0.966 for blue, 0.913 for brown, and 0.796 for intermediate eye color. Sensitivity was 93.6% for blue, 58.1% for brown, and 0% for intermediate eye color. Specificity was 93.1% for blue, 89.2% for brown, and 100% for intermediate eye color. PPV was 91.7% for blue and 69.2% for brown color. NPV was 94.7% for blue and 83.5% for brown eye color. These values indicate prediction accuracy comparable to that established in other studies.ConclusionBlue and brown eye color can be reliably predicted from DNA samples using only six polymorphisms, while intermediate eye color defies prediction, indicating that more research is needed to genetically predict the whole variation of eye color in humans.

Prediction of visible traits from genetic data in certain forensic cases may provide important information that can speed up the process of investigation. Research that has been conducted on the genetics of pigmentation has revealed polymorphisms that explain a significant proportion of the variation observed in human iris color. Here, on the basis of genetic data for the six most relevant eye color predictors, two alternative Bayesian network model variants were developed and evaluated for their accuracy in prediction of eye color. The first model assumed eye color to be categorized into blue, brown, green, and hazel, while the second variant assumed a simplified classification with two states: light and dark. It was found that particularly high accuracy was obtained for the second model, and this proved that reliable differentiation between light and dark irises is possible based on analysis of six single nucleotide polymorphisms and a Bayesian procedure of evidence interpretation.