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

详细信息    查看全文

• 作者：G. Cereda ; A. Biedermann ; D. Hall ; F. Taroni
• 关键词：Unbalanced DNA mixture ; Bayesian networks ; Object-orientation ; Deletion/Insertion Polymorphism ; Likelihood ratio
• 刊名：Forensic Science International: Genetics
• 出版年：January, 2014
• 年：2014
• 卷：8
• 期：1
• 页码：159-169
• 全文大小：1080 K

文摘

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鈩?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 鈥榤asks鈥?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 . 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.