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Developing a Global Genotype Database to Predict Eye, Hair, and Skin Color From DNA

NCJ Number
253099
Date Published
Author(s)
National Institute of Justice
Annotation
This article reports on two National Institute of Justice (NIJ) research grants that focused on improving measurements of eye, hair, and skin pigmentation with new and known single nucleotide polymorphisms (SNPs) and whole genes.
Abstract
A 2013 NIJ grant awarded to anthropologist Heather Norton involved the analysis of genomes from North and South American individuals who had African, Asian, and European ancestry. This work is ongoing and suggests that the genetics of pigmentation in non-Europeans may be more complex than previously thought. A 2014 NIJ grant to biologist Susan Walsh focused on areas “where improvement and fundamental research is required for more accurate prediction of pigmentation from DNA in both categorical and quantitative areas.” Such areas included understanding green and intermediate eye color, age-related changes in hair color, and building systems for predicting categorical and quantitative aspects of skin color. The initial phase of the work involved a genome-wide association study (GWAS) across 1,000 European genomes in order to find the most predictive SNPs for quantitative eye, hair, and skin color. In order to increase the statistical outcome of the results, the researchers added just over 2,000 individuals from the United States. The research team began work with a software company in developing a program that enables users to input genotype (an individual’s entire genetic code) information to produce a specific quantitative color result for eye, hair, and skin color. Researchers also began work with collaborators to improve understanding of the genes involved in human hair structure (curvy, wavy, or straight), along with predictive mutations for these traits. Because of some of the work done under this project, Walsh concluded that “eye, hair, and skin color prediction is now available for forensic practitioners.”
Date Created: July 11, 2019