A top-down approach to DNA mixtures. (May 2020)

• Record Type: Journal Article
• Authors: Slooten, Klaas
• Is Part Of: Forensic science international. Volume 46(2020)
• Publication Date: 2020-05
• Subjects: Likelihood ratios -- DNA mixtures -- Semi-continuous model -- Deconvolution -- Weight of evidence; Forensic genetics -- Periodicals; Génétique légale -- Périodiques; Forensic genetics; Electronic journals; Periodicals; 614.1
• DOI: 10.1016/j.fsigen.2020.102250
• Languages: English
• ISSNs: 1872-4973

Abstract

Highlights: We give a method to target the most prominent contributors to DNA mixture profiles, and calculate likelihood ratios for being one of those. We use peak height information in the trace qualitatively, without specifying a peak height model. We argue why and illustrate that the results obtained with this method are a useful approximation of those obtained with continuous models. The complexity of the calculations with our method depends on the targeted number of contributors instead of on the trace as a whole. We present results on mixtures with known contributors and on data from a research project. Abstract: Presently, there exist many different models and algorithms for determining, in the form of a likelihood ratio, whether there is evidence that a person of interest contributed to a mixed trace profile. These methods have in common that they model the whole trace, hence all its contributors, which leads to the computation time being mostly determined by the number of contributors that is assumed. At some point, these calculations are no longer feasible. We present another approach, in which we target the contributors of the mixture in the order of their contribution. With this approach the calculation time now depends on how many contributors are queried. This means that any trace can be subjected to calculations of likelihood ratios in favor of being a relatively prominent contributor, and we can choose not to query it for all its contributors, e.g., if that is computationally not feasible, or not relevant for the case. We do so without using a quantitative peak height model, i.e., we do not define a peak height distribution. Instead, we work with subprofiles derived from the full trace profile, carrying out likelihood ratio calculations on these with a discrete method. This lack of modeling makes our method widely applicable. The results with our top-down method are slightly conservative with respect to the one of a continuous model, and more so as we query less and less prominent contributors. We present results on mixtures with known contributors and on research data, analyzing traces with plausibly 6 or more contributors. If a top- k of most prominent contributors is targeted, it is not necessary to know how many other contributors there are for LR calculations, and the more prominent the queried contributor is relatively to all others, the less the evidential value depends on the specifics of a chosen peak height model. For these contributors the qualitative statement that more input DNA leads to larger peaks suffices. The evidential value for a comparison with minor contributors on the other hand, potentially depends much more on the chosen model. We also conclude that a trace's complexity, as meaning its (in)ability to yield large LR's that are not too model-dependent, is not measured by its number of contributors; rather, it is the equality of contribution that makes it harder to obtain strong evidence.