Forensic genealogy-A comparison of methods to infer distant relationships based on dense SNP data

The concept forensic genealogy was discussed already in 2005 but has recently emerged in relation to the use of public genealogy databases to find relatives of the donor of a crime stain. In this study we explored the results and evaluation of searches conducted in such databases. In particular, we focused on the statistical classification that entails from the search and study the variation observed for different relationship classes. The forensic guidelines advocate the use of the likelihood ratio (LR) as a mean to measure the weight of evidence, which requires exact formulation of competing hypotheses. We contrast the LR approach with alternative approaches relying on identical by state (IBS) measures to estimate the total length of shared genomic segments as well as identical by descent (IBD) coefficients for a pair of individuals. We used freely accessible data from the 1000 Genome project to perform extensive simulations, generating data for a number of distinct relationships. Specifically we studied some overarching relationship classes and the performance of the above-mentioned evaluative approaches to classify a known pair of relatives into each class. The results indicate that the traditional LR approach as a single source of classification is as good as, and in some cases even better than, the alternative approaches. In particular the true classification rate is higher for some distant relationship. However, the LR approach is both computer-intensive and sensitive to population frequencies as well as genetic maps (positions of the markers). We further showed that when combining different classification approaches, a lower false classification rate was achieved while still maintaining a high true classification rate.