Joint Bayesian analysis of forensic mixtures.

Evaluation of series of PCR experiments referring to the same evidence is not infrequent in a forensic casework. This situation is met when 'series of results in mixture' (EPGs produced by reiterating PCR experiments over the same DNA mixture extract) have to be interpreted or when 'potentially related traces' (mixtures that can have contributors in common) require a combined interpretation. In these cases, there can be uncertainty on the genotype assignment, since: (a) more than one genotype combination fall under the same peak profile; (b) PCR preferential amplification alters pre-PCR allelic proportions; (c) other, more unpredictable technical problems (dropouts/dropins, etc.) take place. The uncertainty in the genotype assignment is in most cases addressed by empirical methods (selection of just one particular profile; extraction of consensual or composite profiles) that disregard part of the evidence. Genotype assignment should conversely take advantage from a joint Bayesian analysis (JBA) of all STRs peak areas generated at each experiment. This is the typical case of Bayesian analysis in which adoption of object-oriented Bayesian networks (OOBNs) could be highly helpful. Starting from experimentally designed mixtures, we created typical examples of 'series of results in mixture' of 'potentially related traces'. JBA was some administered to the whole peak area evidence, by specifically tailored OOBNs models, which enabled genotype assignment reflecting all the available evidence. Examples of a residual ambiguity in the genotype assignment came to light at assumed genotypes with partially overlapping alleles (for example: AB+AC→ABC). In the 'series of results in mixture', this uncertainty was in part refractory to the joint evaluation. Ambiguity was conversely dissipated at the 'potentially related' trace example, where the ABC allelic scheme at the first trace was interpreted together with other unambiguous combinations (ABCD; AB) at the related trace. We emphasize the need to carry out extensive, blind sensitivity tests specifically addressing the residual ambiguity that arises from overlapping results mixed at various quantitative ratios.