A multivariate statistical approach to for the evaluation of the biogeographical ancestry information from traditional STRs

• Published in: Forensic science international. Genetics supplement series 2019-12, Vol.7 (1), p.253-255
• Main Authors: Alladio, Eugenio, Della Rocca, Chiara, Cruciani, Fulvio, Vincenti, Marco, Garofano, Paolo, Berti, Andrea, Barni, Filippo
• Format: Article
• Language: English
• Subjects: Autosomal STRs; Biogeographic ancestry (BGA); Forensim; Partial Least Squares – Discriminant Analysis (PLS-DA); Prediction
• ISSN: 1875-1768; 1875-175X
• DOI: 10.1016/j.fsigss.2019.09.097

The capability to achieve biogeographic ancestry (BGA) information from DNA profiles have been largely explored in forensic genetics because of its potential usefulness in providing investigative clues. For law enforcement and security purposes, when genetic data have been obtained from unknown evidence, but no reference samples are available and no hints come out from DNA databases, it would be extremely useful at least to infer the ethno-geographic origin of the stain donor by just examining traditional STRs DNA profiles. Current protocols for ethnic origin estimation using STRs profiles are usually based on Principal Component Analysis approaches and Bayesian methods. The present study provides an alternative approach that involves the use of target multivariate data analysis strategies for estimation of the BGA information from unknown biological traces. A powerful multivariate technique such as Partial Least Squares-Discriminant Analysis (PLS-DA) has been applied on NIST U.S. population datasets containing, for instance, the allele frequencies of African-American, Asian, Caucasian and Hispanic individuals. PLS-DA approach provided robust classifications, yielding high sensitivity and specificity models capable of discriminating the populations on ethnic basis. Finally, a real casework has been examined by extending the developed model to smaller and more geographically-restricted populations involving, for instance, Albanian, Italian and Montenegrian individuals.