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Model-based prediction of human hair color using DNA variants

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Model-based prediction of human hair color using DNA variants

Content Provider	PubMed Central
Author	Branicki, Wojciech Liu, Fan Duijn, Kate Van Draus-barini, Jolanta Pośpiech, Ewelina Walsh, Susan Kupiec, Tomasz Anna, Wojas-pelc Kayser, Manfred
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.
Related Links	http://dx.doi.org/10.1007/s00439-010-0939-8
Ending Page	454
Page Count	12
Starting Page	443
File Format	PDF
ISSN	03406717
e-ISSN	14321203
Journal	Human Genetics
Issue Number	4
Volume Number	129
Language	English
Publisher	Springer-Verlag
Publisher Date	2011-04-01
Access Restriction	Open
Rights Holder	Springer-Verlag
Subject Keyword	Genetics(clinical) Genetics Research in Higher Education
Content Type	Text
Resource Type	Article
Subject	Genetics Genetics (clinical)

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