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Prediction of Eye Color from Genetic Data Using Bayesian Approach *
Author(s) -
Pośpiech Ewelina,
DrausBarini Jolanta,
Kupiec Tomasz,
WojasPelc Anna,
Branicki Wojciech
Publication year - 2012
Publication title -
journal of forensic sciences
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.715
H-Index - 96
eISSN - 1556-4029
pISSN - 0022-1198
DOI - 10.1111/j.1556-4029.2012.02077.x
Subject(s) - eye color , bayesian probability , artificial intelligence , bayesian network , computer science , human eye , pattern recognition (psychology) , biology , genetics , gene
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.