Making decisions in missing person identification cases with low statistical power.

The present work proposes a general strategy for dealing with missing person identification cases through DNA-database search. Our main example is the identification of abducted children in the last civic-dictatorship of Argentina, known as the "Missing Grandchildren of Argentina". Particularly we focus on those pedigrees where few, or only distant relatives of the missing person are available, resulting in low statistical power. For such complex cases we provide a statistical method for selecting a likelihood ratio (LR) threshold for each pedigree based on error rates. Furthermore, we provide an open-source user friendly software for computing LR thresholds and error rates. The strategy described in the paper could be applied to other large-scale cases of DNA-based identification hampered by low statistical power.

Second GHEP-ISFG exercise for DVI: "DNA-led" victims' identification in a simulated air crash.

The Spanish and Portuguese-Speaking Working Group of the International Society for Forensic Genetics (GHEP-ISFG) has organized a second collaborative exercise on a simulated case of Disaster Victim Identification (DVI), with the participation of eighteen laboratories. The exercise focused on the analysis of a simulated plane crash case of medium-size resulting in 66 victims with varying degrees of fragmentation of the bodies (with commingled remains). As an additional difficulty, this second exercise included 21 related victims belonging to 6 families among the 66 missings to be identified. A total number of 228 post-mortem samples were represented with aSTR and mtDNA profiles, with a proportion of partial aSTR profiles simulating charred remains. To perform the exercise, participants were provided with aSTR and mtDNA data of 51 reference pedigrees -some of which deficient-including 128 donors for identification purposes. The exercise consisted firstly in the comparison of the post-mortem genetic profiles in order to re-associate fragmented remains to the same individual and secondly in the identification of the re-associated remains by comparing aSTR and mtDNA profiles with reference pedigrees using pre-established thresholds to report a positive identification. Regarding the results of the post-mortem samples re-associations, only a small number of discrepancies among participants were detected, all of which were from just a few labs. However, in the identification process by kinship analysis with family references, there were more discrepancies in comparison to the correct results. The identification results of single victims yielded fewer problems than the identification of multiple related victims within the same family groups. Several reasons for the discrepant results were detected: a) the identity/non-identity hypotheses were sometimes wrongly expressed in the likelihood ratio calculations, b) some laboratories failed to use all family references to report the DNA match, c) In families with several related victims, some laboratories firstly identified some victims and then unnecessarily used their genetic information to identify the remaining victims within the family, d) some laboratories did not correctly use "prior odds" values for the Bayesian treatment of the episode for both post-mortem/post-mortem re-associations as well as the ante-mortem/post-mortem comparisons to evaluate the probability of identity. For some of the above reasons, certain laboratories failed to identify some victims. This simulated "DNA-led" identification exercise may help forensic genetic laboratories to gain experience and expertize for DVI or MPI in using genetic data and comparing their own results with the ones in this collaborative exercise.

Evaluating the statistical power of DNA-based identification, exemplified by 'The missing grandchildren of Argentina'.

Methods and implementations of DNA-based identification are well established in several forensic contexts. However, assessing the statistical power of these methods has been largely overlooked, except in the simplest cases. In this paper we outline general methods for such power evaluation, and apply them to a large set of family reunification cases, where the objective is to decide whether a person of interest (POI) is identical to the missing person (MP) in a family, based on the DNA profile of the POI and available family members. As such, this application closely resembles database searching and disaster victim identification (DVI). If parents or children of the MP are available, they will typically provide sufficient statistical evidence to settle the case. However, if one must resort to more distant relatives, it is not a priori obvious that a reliable conclusion is likely to be reached. In these cases power evaluation can be highly valuable, for instance in the recruitment of additional family members. To assess the power in an identification case, we advocate the combined use of two statistics: the Probability of Exclusion, and the Probability of Exceedance. The former is the probability that the genotypes of a random, unrelated person are incompatible with the available family data. If this is close to 1, it is likely that a conclusion will be achieved regarding general relatedness, but not necessarily the specific relationship. To evaluate the ability to recognize a true match, we use simulations to estimate exceedance probabilities, i.e. the probability that the likelihood ratio will exceed a given threshold, assuming that the POI is indeed the MP. All simulations are done conditionally on available family data. Such conditional simulations have a long history in medical linkage analysis, but to our knowledge this is the first systematic forensic genetics application. Also, for forensic markers mutations cannot be ignored and therefore current models and implementations must be extended. All the tools are freely available in Familias (http://www.familias.no) empowered by the R library paramlink. The above approach is applied to a large and important data set: 'The missing grandchildren of Argentina'. We evaluate the power of 196 families from the DNA reference databank (Banco Nacional de Datos Genéticos, http://www.bndg.gob.ar. As a result we show that 58 of the families have poor statistical power and require additional genetic data to enable a positive identification.