Bonaparte | Disaster Victim Identification System

Bonaparte Disaster Victim Identification System

Society is increasingly aware of the possibility of a mass disaster. Recent examples are the WTC attacks, the tsunami, and various airplane crashes. In such an event, the recovery and identification of the remains of the victims is of great importance, both for humanitarian as well as legal reasons. Disaster victim identification (DVI), i.e. the identification of victims of a mass disaster, is greatly facilitated by the advent of modern DNA technology. In forensic laboratories, DNA profiles can be recorded from small samples of body remains which may otherwise be unidentifiable.

The identification task is the match of the unidentified victim with a reported missing person. This is often complicated by the fact that the match has to be made in an indirect way. This is the case when there is no reliable reference material of the missing person. In these cases DNA profiles can be taken from relatives. Since their profiles are statistically related to the profile of the missing person (first degree family members share about half of their DNA) an indirect match can be made.

In cases with one victim, identification is a reasonable straightforward task for forensic researchers. In the case of a few victims, the puzzle to match the victims and the missing persons is often still doable by hand (either by using a spread sheet or with software tools available on the internet). However, large scale DVI is infeasible this way and an automated routine is indispensible for forensic institutes that need to be prepared for DVI.

The purpose of the Bonaparte software

Forensic researchers need a tool to deal with mass fatality incidents in a transparent, consistent and efficient way. Efficiency is important since the number of possible combinations that researches have to calculate grows very quickly: O(n²). Consider a case with 10 victims with their 10 putative pedigrees. This results in just 100 combinations, but 100 victims with their 100 pedigrees yields 10,000 combinations. And then these samples have not even been checked against each other (4,950 combinations) or checked for contamination (100 × the number of elimination profiles).

An automated system also performs each calculation in exactly the same way and eliminates the human-error factor. The Bonaparte system is "transparent" it is not a black box where data is fed into and the results pour out. The models implemented in Bonaparte are well documented and available to end users. Bonaparte uses statistical graphical models; the so-called Bayesian networks.

Bayesian networks are very well suited to model the statistical relations of genetic material of relatives in a pedigree.

They can directly be applied in kinship analysis with any type of pedigree of relatives of the missing persons. An additional advantage of a Bayesian network approach is that it makes the analysis tool more transparent and flexible, allowing to incorporate other factors that play a role such as measurement error probability, missing data, statistics of more advanced genetic markers etc.

In collaboration with the Netherlands Forensic Institute.

The development of Bonaparte was done in collaboration with the Netherlands Forensic Institute. The computational engine of Bonaparte uses automatically generated Bayesian networks and Bayesian inference methods, enabling to correctly do kinship analysis on the basis of DNA profiles combined with pedigree information. It is specifically designed to handle large scale incidents with thousands of victims and missing persons.

In addition, it has a graphical user interface, including a "drag and drop" pedigree editor. Data-interfaces to other laboratory systems (e.g. for the DNA-data input) are also available.

More technical information about the system can be found on our system page.