  Many forensic genetics problems can be handled using structured systems of
discrete variables, for which Bayesian networks offer an appealing practical
modeling framework, and allow inferences to be computed by probability
propagation methods. However, when standard assumptions are violated--for
example, when allele frequencies are unknown, there is identity by descent or
the population is heterogeneous--dependence is generated among founding genes,
that makes exact calculation of conditional probabilities by propagation
methods less straightforward. Here we illustrate different methodologies for
assessing sensitivity to assumptions about founders in forensic genetics
problems. These include constrained steepest descent, linear fractional
programming and representing dependence by structure. We illustrate these
methods on several forensic genetics examples involving criminal
identification, simple and complex disputed paternity and DNA mixtures.
