Assessment of the Precision ID Ancestry panel

Publication Type:
Journal Article
Citation:
International Journal of Legal Medicine, 2018, 132 (6), pp. 1581 - 1594
Issue Date:
2018-11-01
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© 2018, Springer-Verlag GmbH Germany, part of Springer Nature. Abstract The ability to provide accurate DNA-based forensic intelligence requires analysis of multiple DNA markers to predict the biogeographical ancestry (BGA) and externally visible characteristics (EVCs) of the donor of biological evidence. Massively parallel sequencing (MPS) enables the analysis of hundreds of DNA markers in multiple samples simultaneously, increasing the value of the intelligence provided to forensic investigators while reducing the depletion of evidential material resulting from multiple analyses. The Precision ID Ancestry Panel (formerly the HID Ion AmpliSeq™ Ancestry Panel) (Thermo Fisher Scientific) (TFS)) consists of 165 autosomal SNPs selected to infer BGA. Forensic validation criteria were applied to 95 samples using this panel to assess sensitivity (1 ng-15 pg), reproducibility (inter- and intra-run variability) and effects of compromised and forensic casework type samples (artificially degraded and inhibited, mixed source and aged blood and bone samples). BGA prediction accuracy was assessed using samples from individuals who self-declared their ancestry as being from single populations of origin (n = 36) or from multiple populations of origin (n = 14). Sequencing was conducted on Ion 318™ chips (TFS) on the Ion PGM™ System (TFS). HID SNP Genotyper v4.3.1 software (TFS) was used to perform BGA predictions based on admixture proportions (continental level) and likelihood estimates (sub-population level). BGA prediction was accurate at DNA template amounts of 125pg and 30pg using 21 and 25 PCR cycles respectively. HID SNP Genotyper continental level BGA assignments were concordant with BGAs for self-declared East Asian, African, European and South Asian individuals. Compromised, mixed source and admixed samples, in addition to sub-population level prediction, requires more extensive analysis.
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