Abstract
We address computational and statistical aspects of DNA-based identification of victims in the aftermath of disasters. Current methods and software for such identification typically consider each victim individually, leading to suboptimal power of identification and potential inconsistencies in the statistical summary of the evidence. We resolve these problems by performing joint identification of all victims, using the complete genetic data set. Individual identification probabilities, conditional on all available information, are derived from the joint solution in the form of posterior pairing probabilities. A closed formula is obtained for the a priori number of possible joint solutions to a given DVI problem. This number increases quickly with the number of victims and missing persons, posing computational challenges for brute force approaches. We address this complexity with a preparatory sequential step aiming to reduce the search space. The examples show that realistic cases are handled efficiently. User-friendly implementations of all methods are provided in the R package dvir, freely available on all platforms.
Highlights
We address computational and statistical aspects of DNA-based identification of victims in the aftermath of disasters
A simple example of how this may cause missed identifications is given in Fig. 2, where we show that the missing person ( M1 ) cannot be identified unless the data are considered jointly
The purpose of the example is to illustrate that the joint approach may succeed in cases where the sequential methods fail
Summary
Current methods and software for such identification typically consider each victim individually, leading to suboptimal power of identification and potential inconsistencies in the statistical summary of the evidence We resolve these problems by performing joint identification of all victims, using the complete genetic data set. A closed formula is obtained for the a priori number of possible joint solutions to a given DVI problem This number increases quickly with the number of victims and missing persons, posing computational challenges for brute force approaches. The one-to-one approach amounts to comparing each PM profile to each AM reference, looking for evidence of a close relationship This method is widely used, at least for an initial screening, since easy cases, like direct matches and parent-child, often can be reliably resolved in this w ay[2,9]. If priors are specified the LRs can be converted to posterior probabilities[10]
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