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Forensic validation of a SNP and INDEL panel for individualisation of timber from bigleaf maple (Acer macrophyllum Pursch)

Dormontt, E.E., Jardine, D.I., van Dijk, K-J, Dunker, B.F., Dixon, R.R.M., Hipkins, V.D., Tobe, S.ORCID: 0000-0002-4854-6278, Linacre, A. and Lowe, A.J. (2020) Forensic validation of a SNP and INDEL panel for individualisation of timber from bigleaf maple (Acer macrophyllum Pursch). Forensic Science International: Genetics, 46 . Art. 102252.

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Illegal logging is one of the largest illicit trades in the world, with high profits and generally low risks of detection and prosecution. Timber identification presents problems for law enforcement as traditionally used forensic methods such as wood anatomy and dendrochronology are often unable to confidently match wood evidence to the remains of illegally felled trees. Here we have developed and validated a set of genetic markers for individualisation in bigleaf maple (Acer macrophyllum), a high value timber species often felled illegally in the USA. Using 128 single nucleotide polymorphisms and three insertion/deletion markers developed through massively parallel sequencing, 394 individuals were genotyped on the MassARRAY® iPLEX™ platform (Agena Bio-science™, San Diego, USA) to produce a population reference database for the species. We demonstrate that the resulting DNA assay is reliable, species specific, effective at low DNA concentrations (<1 ng/μL) and suitable for application to timber samples. The PID for the most common profile, calculated using an overall dataset level FST-correction factor, was 1.785 × 10−25 and PID-SIB across all individuals (treated as a single population) was 2.496 × 10-22. The further development of forensic identification assays for timber species has the potential to deliver robust tools for improved detection and prosecution of illegal logging crimes as well as for the verification of legality in reputable supply chains.

Item Type: Journal Article
Murdoch Affiliation: Medical, Molecular and Forensic Sciences
Publisher: Elsevier BV
Copyright: © 2020 Elsevier B.V.
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