Prediction of Eye Color from Genetic Data Using Bayesian Approach

• NCJ Number: 241212
• Journal: Journal of Forensic Sciences Volume: 57 Issue: 4 Dated: July 2012 Pages: 880-886
• Author(s): Ewelina Pospiech, M.S.; Jolanta Draus-Barini, M.S.; Tomasz Kupiec, Ph.D.; Anna Wojas-Pelc, Ph.D., M.D.; Wojciech Branicki, Ph.D.
• Date Published: July 2012
• Length: 7 pages
• Publication Format: Article
• Publication Type: Report (Technical Assistance)
• Language: English
• Country: United States of America

Annotation

This article examined two alternative Bayesian network model variants developed and evaluated for their accuracy in prediction of eye color.

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. Abstract published by arrangement with John Wiley & Sons.