Inter-sample contamination detection using mixture deconvolution comparison
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Date
2019-02-26
Authors
Taylor, Duncan A
Rowe, Emily
Kruijver, Maarten
Abarno, Damien
Bright, Jo-Anne
Buckleton, John S
Journal Title
Journal ISSN
Volume Title
Publisher
Elsevier
Rights
© 2019 Elsevier B.V.
Rights Holder
Elsevier B.V.
Abstract
A recent publication has provided the ability to compare two mixed DNA profiles and consider their probability of occurrence if they do, compared to if they do not, have a common contributor. This ability has applications to both quality assurance (to test for sample to sample contamination) and for intelligence gathering purposes (did the same unknown offender donate DNA to multiple samples). We use a mixture to mixture comparison tool to investigate the prevalence of sample to sample contamination that could occur from two laboratory mechanisms, one during DNA extraction and one during electrophoresis. By carrying out pairwise comparisons of all samples (deconvoluted using probabilistic genotyping software STRmix™) within extraction or run batches we identify any potential common DNA donors and investigate these with respect to their risk of contamination from the two proposed mechanisms. While not identifying any contamination, we inadvertently find a potential intelligence link between samples, showing the use of a mixture to mixture comparison tool for investigative purposes.
Description
© 2019 Elsevier B.V. This manuscript version is made available under the CC-BY-NC-ND 4.0 license:
http://creativecommons.org/licenses/by-nc-nd/4.0/
This author accepted manuscript is made available following 12 month embargo from date of publication (February 2019) in accordance with the publisher’s archiving policy
Keywords
Contamination, Extraction batch, Deconvolution, Mixture comparison
Citation
Taylor, D., Rowe, E., Kruijver, M., Abarno, D., Bright, J.-A., & Buckleton, J. (2019). Inter-sample contamination detection using mixture deconvolution comparison. Forensic Science International: Genetics, 40, 160–167. https://doi.org/10.1016/j.fsigen.2019.02.021