Accuracy of Eye and Hair Color Prediction in Mexican Mestizos from Monterrey City Based on ForenSeqTM DNA Signature Prep

Author:

Aguilar-Velázquez José Alonso12,Llamas-de-Dios Blanca Jeannete3,Córdova-Mercado Miranda Fabiola4,Coronado-Ávila Carolina Elena4,Salas-Salas Orlando4,López-Quintero Andrés5ORCID,Ramos-González Benito4,Rangel-Villalobos Héctor6ORCID

Affiliation:

1. Instituto de Investigación en Ciencias Biomédicas, Centro Universitario de Ciencias de la Salud, Universidad de Guadalajara (CUCS-UdeG), Guadalajara 44340, Jalisco, Mexico

2. Departamento de Morfología, Centro Universitario de Ciencias de la Salud, Universidad de Guadalajara (CUCS-UdeG), Guadalajara 44340, Jalisco, Mexico

3. Licenciatura en Ciencias Forenses, Centro Universitario de Ciencias de la Salud, Universidad de Guadalajara (CUCS-UdeG), Guadalajara 44340, Jalisco, Mexico

4. Instituto de Criminalística y Servicios Periciales, Fiscalía General de Justicia del Estado de Nuevo León (FGJNL), Monterrey 64720, Nuevo León, Mexico

5. Instituto de Nutrigenética y Nutrigenómica Traslacional, Centro Universitario de Ciencias de la Salud, Universidad de Guadalajara (CUCS-UdeG), Guadalajara 44340, Jalisco, Mexico

6. Instituto de Investigación en Genética Molecular, Centro Universitario de la Ciénega, Universidad de Guadalajara (CUCI-UdeG), Ocotlán 47820, Jalisco, Mexico

Abstract

Forensic genomic systems allow simultaneously analyzing identity informative (iiSNPs), ancestry informative (aiSNPs), and phenotype informative (piSNPs) genetic markers. Among these kits, the ForenSeq DNA Signature prep (Verogen) analyzes identity STRs and SNPs as well as 24 piSNPs from the HIrisPlex system to predict the hair and eye color. We report herein these 24 piSNPs in 88 samples from Monterrey City (Northeast, Mexico) based on the ForenSeq DNA Signature prep. Phenotypes were predicted by genotype results with both Universal Analysis Software (UAS) and the web tool of the Erasmus Medical Center (EMC). We observed predominantly brown eyes (96.5%) and black hair (75%) phenotypes, whereas blue eyes, and blond and red hair were not observed. Both UAS and EMC showed high performance in eye color prediction (p ≥ 96.6%), but a lower accuracy was observed for hair color prediction. Overall, UAS hair color predictions showed better performance and robustness than those obtained with the EMC web tool (when hair shade is excluded). Although we employed a threshold (p > 70%), we suggest using the EMC enhanced approach to avoid the exclusion of a high number of samples. Finally, although our results are helpful to employ these genomic tools to predict eye color, caution is suggested for hair color prediction in Latin American (admixed) populations such as those studied herein, principally when no black color is predicted.

Funder

CONACYT

Publisher

MDPI AG

Subject

Genetics (clinical),Genetics

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