Predictive DNA analysis for biogeographical ancestry

Cheung, EYY; Gahan, ME; McNevin, D

Predictive DNA analysis for biogeographical ancestry

Cheung, EYY Gahan, ME McNevin, D

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Publication Type:: Journal Article
Citation:: Australian Journal of Forensic Sciences, 2018, 50 (6), pp. 651 - 658
Issue Date:: 2018-11-02

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Field	Value	Language
dc.contributor.author	Cheung, EYY	en_US
dc.contributor.author	Gahan, ME	en_US
dc.contributor.author	McNevin, D https://orcid.org/0000-0003-1665-3367	en_US
dc.date.issued	2018-11-02	en_US
dc.identifier.citation	Australian Journal of Forensic Sciences, 2018, 50 (6), pp. 651 - 658	en_US
dc.identifier.issn	0045-0618	en_US
dc.identifier.uri	http://hdl.handle.net/10453/130860
dc.description.abstract	© 2018, © 2018 Australian Academy of Forensic Sciences. Establishment of national DNA databases in Australia and overseas has increased the number of criminal convictions, yet a high volume of serious crime cases remain with no suspect profile nor any DNA database matches. In these circumstances prediction of biogeographical ancestry (BGA) and externally visible characteristics can assist by providing forensic intelligence in conjunction with, or in place of, eyewitness testimonies. To predict the BGA of an individual requires: genetic markers selected for their ability to differentiate between BGAs; representative BGA reference populations; and a prediction algorithm (‘classifier’) that predicts the BGA of an unknown individual based on genetic markers in the reference populations. The human genome contains autosomal ancestry informative markers that are easily harvested from publicly accessible collections of genotypes with associated ancestry information. A number of classification methods are available including Bayesian approaches and distance-based algorithms. BGA is likely to be continuous rather than discrete and some methods are inappropriate for the prediction of admixed BGA. As predictive services become available to the public and private sectors, there is a risk of results being misinterpreted if an inappropriate tool is applied. Understanding the underlying marker sets, reference populations and classification algorithms is required to prevent ill-informed predictions.	en_US
dc.relation.ispartof	Australian Journal of Forensic Sciences	en_US
dc.relation.isbasedon	10.1080/00450618.2017.1422021	en_US
dc.subject.classification	Legal & Forensic Medicine	en_US
dc.title	Predictive DNA analysis for biogeographical ancestry	en_US
dc.type	Journal Article
utslib.citation.volume	6	en_US
utslib.citation.volume	50	en_US
utslib.for	0699 Other Biological Sciences	en_US
utslib.for	0604 Genetics	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	50	en_US

Abstract:

© 2018, © 2018 Australian Academy of Forensic Sciences. Establishment of national DNA databases in Australia and overseas has increased the number of criminal convictions, yet a high volume of serious crime cases remain with no suspect profile nor any DNA database matches. In these circumstances prediction of biogeographical ancestry (BGA) and externally visible characteristics can assist by providing forensic intelligence in conjunction with, or in place of, eyewitness testimonies. To predict the BGA of an individual requires: genetic markers selected for their ability to differentiate between BGAs; representative BGA reference populations; and a prediction algorithm (‘classifier’) that predicts the BGA of an unknown individual based on genetic markers in the reference populations. The human genome contains autosomal ancestry informative markers that are easily harvested from publicly accessible collections of genotypes with associated ancestry information. A number of classification methods are available including Bayesian approaches and distance-based algorithms. BGA is likely to be continuous rather than discrete and some methods are inappropriate for the prediction of admixed BGA. As predictive services become available to the public and private sectors, there is a risk of results being misinterpreted if an inappropriate tool is applied. Understanding the underlying marker sets, reference populations and classification algorithms is required to prevent ill-informed predictions.

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http://hdl.handle.net/10453/130860