Towards Broadening Forensic DNA Phenotyping Beyond Pigmentation: Improving the Prediction of Head Hair Shape From DNA

• NCJ Number: 253997
• Journal: Forensic Science International-Genetics Volume: 37 Dated: November 2018 Pages: 241-251
• Author(s): Ewelina Pospiech; Yan Chen; Magdalena Kukla-Bartoszek; Krystal Breslin; Anastasia Aliferi; Jeppe D. Andersen; David Ballard; Lakshmi Chaitanya; Ana Freire-Ardas; Kristiaan J. van der Gaagf; Lorena Guiron-Santamaria; Theresa E. gysi; Gabriela Huber; Ana Moquera-Miguel; Charanya Muralidharan; Malgorzata Skowron; Angel Carracedo; Manfred Kayser
• Date Published: November 2018
• Length: 11 pages
• Grant Number(s): 2014-DN-BX-K031
• Agencies: NIJ-Sponsored
• Publication Type: Research (Applied/Empirical); Report (Study/Research); Report (Grant Sponsored); Program/Project Description

Annotation

This study assessed the capacity of DNA-based prediction of head hair shape prediction (wavy, curly, or frizzy) by investigating an extended set of candidate SNP predictors and by using an independent set of samples for model validation.

Abstract

Human head hair shape, commonly classified as straight, wavy, curly or frizzy, is an attractive target for Forensic DNA Phenotyping and other applications of human appearance prediction from DNA, such as in paleogenetics. The genetic knowledge underlying head hair shape variation was recently improved by the outcome of a series of genome-wide association and replication studies in a total of 26,964 subjects, highlighting 12 loci, of which 8 were novel, and introducing a prediction model for Europeans based on 14 SNPs. Prediction model building involved 9,674 subjects (6,068 from Europe, 2,899 from Asia and 707 of admixed European and Asian ancestries) used previously, by considering a novel list of 90 candidate SNPs. For model validation, genotype, and phenotype, data were newly collected in 2,415 independent subjects (2,138 Europeans and 277 non-Europeans) by applying two targeted massively parallel sequencing platforms, Ion Torrent PGM and MiSeq, or the MassARRAY platform. A binomial model was developed to predict straight vs. non-straight hair based on 32 SNPs from 26 genetic loci identified as significantly contributing to the model. This model achieved prediction accuracies, expressed as AUC, of 0.664 in Europeans and 0.789 in non-Europeans. The statistically significant difference was explained mostly by the effect of one EDAR SNP in non-Europeans. Considering sex and age, in addition to the SNPs, slightly and insignificantly increased the prediction accuracies (AUC of 0.680 and 0.800, respectively). Based on the sample size and candidate DNA markers investigated, this study provides the most robust, validated, and accurate statistical prediction models and SNP predictor marker sets currently available for predicting head hair shape from DNA, providing the next step towards broadening Forensic DNA Phenotyping beyond pigmentation traits. (publisher abstract modified)