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Assessment of the Information Content of Likelihood Ratios Derived from Complex Mixtures

NCJ Number
249967
Date Published
May 2016
Length
9 pages
Author(s)
C. D. Marsden, N. Rudin, K. Inman, K. E. Lohmueller
Agencies
NIJ-Sponsored
Annotation
In order to characterize the ability to distinguish a true contributor (TC) of DNA from a known non-contributor (KNC) in complex DNA samples, this study simulated sets of six 15-locus Caucasian genotype profiles and used them to create mixtures that contained two to five contributors; and likelihood ratios were computed for various situations, including varying numbers of contributors and unknowns in the evidence profile and comparisons of the evidence profile to TCs and KNCs.
Abstract
This work was intended to illustrate the best-case scenario, in which all alleles from the TC were detected in the simulated evidence samples; therefore, the possibility of drop-out was not modeled in this study. Likelihood ratios (LRs) were computed for various situations, including varying numbers of contributors and unknowns in the evidence profile, as well as comparisons of the evidence profile to TCs and KNCs. The complex mixture simulations show that even when all alleles are detected (i.e., no drop-out), TCs can generate LRs less than one across a 15-locus profile; however, this outcome was rare, 7 of 140,000 replicates (0.005 percent), and associated only with mixtures comprising five contributors in which the numerator hypothesis includes one or more unknown contributors. For KNCs, LRs were found to be greater than one in a small number of replicates (75 of 140,000 replicates, or 0.05 percent). These replicates were limited to four- and five-person mixtures with one or more unknowns in the numerator. Only 5 of these 75 replicates (0.004 percent) yielded a LR greater than 1,000. Thus, overall, these results imply that the weight of evidence that can be derived from complex mixtures containing up to five contributors, under a scenario in which no drop-out is required to explain any of the contributors, is remarkably high. This is a useful benchmark result on top of which to layer the effects of additional factors, such as drop-out, peak height, and other variables. The computer program DNAMIX was used to compute LRs comparing the evidence profile to TCs and KNCs. This resulted in 140,000 LRs for each of the two scenarios. (Publisher abstract modified)
Date Created: April 23, 2017