A multiple predictive tool approach for phenotypic and biogeographical ancestry inferences. Issue 2 (27th May 2022)

• Record Type: Journal Article
• Authors: Frégeau, Chantal J.
• Is Part Of: Journal. Volume 55:Issue 2(2022)
• Page Start: 71
• Page End: 99
• Publication Date: 2022-05-27
• Subjects: Forensic DNA phenotyping -- biogeographical ancestry prediction -- ForenSeq™ -- massively parallel sequencing -- forensic predictive web tools; Phénotypage judiciaire basé sur l'ADN -- prédiction d'ascendance biogéographique -- ForenSeq™ -- séquençage parallèle de masse -- outils judiciaires de prédiction en ligne; Chemistry, Forensic -- Periodicals; Criminal investigation -- Periodicals; Periodicals; 363.2506071
• DOI: 10.1080/00085030.2021.2016206
• Languages: English
• ISSNs: 0008-5030

Abstract

Abstract: Phenotypic and biogeographical ancestry predictions for 128 Canadians with various self-reported ancestries, generated using the ForenSeq™ DNA Signature Prep kit/Primer Mix B and VEROGEN's Universal Analysis Software (UAS) v1.3, were compared to predictions derived from the ERASMUS v2.0, Snipper Application v2.5, Forensic Reference/Resource on Genetics knowledge base [FROG-kb] ©2019 and STRUCTURE v2.3.4, web tools. Performance metrics were calculated for all tools tested. The UAS v1.3 eye color predictions were determined to be accurate (91.8% for brown, 82.4% for blue) with no need to complement results with any other predictive tools. For hair color predictions, the UAS v1.3 was accurate for black (93.5%), for red (80.0%) but limited for brown (50.0%) and for blond (31.3%) using the Highest Probability Approach. The ERASMUS web tool could complement the UAS results using a revised Prediction Guide Approach. Non-admixed individuals with ancestries from Africa, East Asia or Europe were predicted with 79.7% accuracy using the UAS v1.3. Admixed individuals and those with ancestries from India, the Middle East and South America were better classified using Snipper, FROG-kb and STRUCTURE. Complementing UAS v1.3 predictions with those obtained from open access web tools thus represents a way to maximize information derivable from unknown samples.