Joint DNA-based disaster victim identification

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.

Likelihood Ratios for physical traits in forensic investigations

“…In particular, for closed cases, Vigeland et al showed that identifying each UP individually can give inconsistent solutions and proposed a methodology that jointly considers all UPs [18].…”

“…In particular, for closed cases, Vigeland et al showed that identifying each UP individually can give inconsistent solutions and proposed a methodology that jointly considers all UPs [18]. In all these cases, increasing the statistical power in hypothesis testing is imperative, which could require the incorporation of additional family members or genetic markers [2, 10, 19].…”