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Prediction of Eye Color from Genetic Data Using Bayesian Approach

Authors

  • Ewelina Pośpiech M.S.,

    1. Section of Forensic Genetics, Institute of Forensic Research, Westerplatte 9, 31-033 Kraków, Poland.
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  • Jolanta Draus-Barini M.S.,

    1. Section of Forensic Genetics, Institute of Forensic Research, Westerplatte 9, 31-033 Kraków, Poland.
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  • Tomasz Kupiec Ph.D.,

    1. Section of Forensic Genetics, Institute of Forensic Research, Westerplatte 9, 31-033 Kraków, Poland.
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  • Anna Wojas-Pelc Ph.D., M.D.,

    1. Department of Dermatology, Collegium Medicum of the Jagiellonian University, Skawińska 8, 31-066 Kraków, Poland.
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  • Wojciech Branicki Ph.D.

    1. Section of Forensic Genetics, Institute of Forensic Research, Westerplatte 9, 31-033 Kraków, Poland.
    2. Department of Genetics and Evolution, Institute of Zoology, Jagiellonian University, Ingardena 6, 30-060 Kraków, Poland.
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Errata

This article is corrected by:

  1. Errata: Erratum Volume 58, Issue 2, 565, Article first published online: 12 March 2013

  • Supported by a grant from the Ministry of Science and Higher Education in Poland, No. ON301115136 (science fund for years 2009–2012).

Additional information and reprint requests:
Wojciech Branicki, Ph.D.
Institute of Forensic Research
Section of Forensic Genetics
Westerplatte 9
31-033 Kraków
Poland
E-mail: wbranicki@ies.krakow.pl

Abstract

Abstract:  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.

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