Assessment of the Precision ID Ancestry panel

Al-Asfi, M; McNevin, D; Mehta, B; Power, D; Gahan, ME; Daniel, R

Assessment of the Precision ID Ancestry panel

Al-Asfi, M McNevin, D

Mehta, B Power, D Gahan, ME Daniel, R

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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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Field	Value	Language
dc.contributor.author	Al-Asfi, M	en_US
dc.contributor.author	McNevin, D https://orcid.org/0000-0003-1665-3367	en_US
dc.contributor.author	Mehta, B	en_US
dc.contributor.author	Power, D	en_US
dc.contributor.author	Gahan, ME	en_US
dc.contributor.author	Daniel, R	en_US
dc.date.available	2018-01-17	en_US
dc.date.issued	2018-11-01	en_US
dc.identifier.citation	International Journal of Legal Medicine, 2018, 132 (6), pp. 1581 - 1594	en_US
dc.identifier.issn	0937-9827	en_US
dc.identifier.uri	http://hdl.handle.net/10453/128237
dc.description.abstract	© 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.	en_US
dc.relation.ispartof	International Journal of Legal Medicine	en_US
dc.relation.isbasedon	10.1007/s00414-018-1785-9	en_US
dc.subject.classification	Legal & Forensic Medicine	en_US
dc.subject.mesh	Humans	en_US
dc.subject.mesh	Genetic Markers	en_US
dc.subject.mesh	Reproducibility of Results	en_US
dc.subject.mesh	DNA Fingerprinting	en_US
dc.subject.mesh	Polymerase Chain Reaction	en_US
dc.subject.mesh	Sequence Analysis, DNA	en_US
dc.subject.mesh	Genotype	en_US
dc.subject.mesh	Polymorphism, Single Nucleotide	en_US
dc.subject.mesh	Continental Population Groups	en_US
dc.subject.mesh	Female	en_US
dc.subject.mesh	Male	en_US
dc.subject.mesh	High-Throughput Nucleotide Sequencing	en_US
dc.title	Assessment of the Precision ID Ancestry panel	en_US
dc.type	Journal Article
utslib.citation.volume	6	en_US
utslib.citation.volume	132	en_US
utslib.for	0399 Other Chemical Sciences	en_US
utslib.for	0699 Other Biological Sciences	en_US
utslib.for	1103 Clinical Sciences	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Science
pubs.organisational-group	/University of Technology Sydney/Faculty of Science/School of Mathematical and Physical Sciences
pubs.organisational-group	/University of Technology Sydney/Strength - CFS - Centre for Forensic Science
utslib.copyright.status	closed_access
pubs.issue	6	en_US
pubs.publication-status	Published	en_US
pubs.volume	132	en_US

Abstract:

© 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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http://hdl.handle.net/10453/128237