Object-oriented Bayesian networks for evaluating DIP–STR profiling results from unbalanced DNA mixtures

• Published in: Forensic science international : genetics Vol. 8; no. 1; pp. 159 - 169
• Main Authors: Cereda, G, Biedermann, A, Hall, D, Taroni, F
• Format: Journal Article
• Language: English
• Published: Netherlands 01.01.2014
• Subjects: Bayes Theorem; DNA - genetics; Humans; Microsatellite Repeats - genetics; Pathology; Polymerase Chain Reaction; Likelihood ratio; Object-orientation; Deletion/Insertion Polymorphism; Unbalanced DNA mixture; Bayesian networks
• ISSN: 1872-4973; 1878-0326
• DOI: 10.1016/j.fsigen.2013.09.001

Abstract The genetic characterization of unbalanced mixed stains remains an important area where improvement is imperative. In fact, with current methods for DNA analysis (Polymerase Chain Reaction with the SGM Plus™ multiplex kit), it is generally not possible to obtain a conventional autosomal DNA profile of the minor contributor if the ratio between the two contributors in a mixture is smaller than 1:10. This is a consequence of the fact that the major contributor's profile ‘masks’ that of the minor contributor. Besides known remedies to this problem, such as Y-STR analysis, a new compound genetic marker that consists of a Deletion/Insertion Polymorphism (DIP), linked to a Short Tandem Repeat (STR) polymorphism, has recently been developed and proposed elsewhere in literature [1] . The present paper reports on the derivation of an approach for the probabilistic evaluation of DIP–STR profiling results obtained from unbalanced DNA mixtures. The procedure is based on object-oriented Bayesian networks (OOBNs) and uses the likelihood ratio as an expression of the probative value. OOBNs are retained in this paper because they allow one to provide a clear description of the genotypic configuration observed for the mixed stain as well as for the various potential contributors (e.g., victim and suspect). These models also allow one to depict the assumed relevance relationships and perform the necessary probabilistic computations.