DNA Mixture Interpretation: Effect of the Hypothesis on the Likelihood Ratio

Document Type

Article

Publication Date

9-2019

MeSH Terms

Forensic Sciences, DNA, Alleles

Subject: LCSH

Forensic Sciences, DNA

Disciplines

Forensic Science and Technology

Abstract

Although nuclear forensic DNA tests are standard practice in most forensic science laboratories, complex DNA mixture analysis remains a challenge. Although new to many laboratories, the concept of probabilistic genotyping has been presented for over a decade as a tool to aid in mixture analysis. Probabilistic genotyping can be defined as a mathematical approach using the likelihood ratio (LR) to estimate if an individual is likely to be included or excluded in a DNA mixture based on statistical inference. Mathematical modelling of biological data has been shown to be less biased than using analyst discretion in determining an inclusion of a DNA donor to a complex mixture. Still, there are caveats to using probabilistic genotyping software that become evident when applied to forensic casework. The effect of allele sharing and the uncertainty of the number of contributors to the likelihood ratio hypothesis are discussed.

Comments

©2019 This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

This article appeared in the September 2019 issue of International Research Journal of Computer Science (IRJCS).

The full-text of this article is available at www.irjcs.com.

DOI

10.26562/IRJCS.2019.SPCS10081

Creative Commons License

Creative Commons Attribution 4.0 International License
This work is licensed under a Creative Commons Attribution 4.0 International License.

Publisher Citation

Heather (2019). DNA Mixture Interpretation: Effect of the Hypothesis on the Likelihood Ratio. IRJCS:: International Research Journal of Computer Science, Volume VI, 672-675. doi://10.26562/IRJCS.2019.SPCS10081

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