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Legal medicine (Tokyo, Japan) · 2026

Forensic DNA phenotyping: Prediction of eye and hair colour and allelic frequency estimation in the Italian population for the development of a reference dataset.

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Fazio Giulia, Gianfreda Denise, Amurri Sara, Corradini Beatrice, Ferri Gianmarco, Ferrari Francesca, Borciani Ilaria, Bini Carla, Cecchi Rossana, Pelotti Susi, Santunione Anna Laura

Аннотация

In recent years, Forensic DNA Phenotyping (FDP) has emerged as an innovative "DNA intelligence approach" aimed at predicting externally visible individual characteristics from biological traces collected at crime scenes or from biological samples of unidentified corpses or skeletal remains. This study aims to analyse the allele frequency distribution of genetic markers involved in the prediction of eye and hair colour in an Italian population sample, to enhance the existing European dataset. A large cohort of Italian individuals with at least three generations of ancestry was recruited, ensuring representation of phenotypic variability. The 24 SNP markers included in the HIrisPlex panel were analysed using SNaPshot sequencing technique, and the genotypic data were uploaded to the FDP web tool validated for forensic use by the international VISAGE Consortium's. This tool provides prediction probabilities for three eye colour categories (blue, brown, and intermediate) and four hair colour categories (black, blonde, brown, and red). The results confirmed a high predictive accuracy for the Italian population as well; however, the complex genetic structure of intermediate phenotypic traits highlights the need for novel prediction models that include genetic markers associated with complex phenotypes. The strong north-south European gradient in eye colour, along with the hypothesis that variation in eye colour is associated with skin pigmentation and UV environmental adaptation, underscores the importance for further research. These should focus on novel genetic markers to refine the European dataset, in which the Italian population may be included to enhance the accuracy of predictive models.

Предметные рубрики MeSH

ИП Урсолов Владлен Сергеевич

ИНН 500118482352 · ОГРНИП 314500117700049

Реквизиты и контакты