This study assesses a likelihood ratio (LR) approach to the forensic interpretation of single cell electropherograms (scEPGs).
This study introduces a likelihood ratio (LR) approach to the forensic interpretation of single cell electropherograms (scEPGs); establishes that averaging LRs across scEPG clusters for a given person of interest (PoI) is the single-cell analog to the LR for bulk mixtures; groups scEPGs using model-based clustering without reference to a person of interest (PoI) and showing high sensitivity and specificity; and obtained LRs for the entire admixture and demonstrate they are sensitive and unaffected by the mixture’s complexity. The consistency between DNA evidence and person(s) of interest (PoI) is summarized by an LR: the probability of the data given the PoI contributed divided by the probability given they did not. Recent technological developments in laboratory systems enable production of single cell data. The authors focused on assessing the consistency between PoIs and a collection of scEPGs from diploid cells. The authors introduce a framework that: I) clusters scEPGs into collections, each originating from one genetic source; II) for each PoI, determines a LR for each cluster of scEPGs; and III) by averaging the likelihood ratios for each PoI across all clusters provides a whole-sample weight of evidence summary. By using Model Based Clustering (MBC) in step I) and an algorithm, named EESCIt for Evidentiary Evaluation of Single Cells, that computes single-cell LRs in step II), the authors show that 99% of the comparisons rendered log LR values > 0 for true contributors, and of these all but one gave log LR > 5, regardless of the number of donors or whether the smallest contributor donated less than 20% of the cells, greatly expanding the collection of cases for which DNA forensics provides informative results. (Published Abstract Provided)
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