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Probabilistic Expert Systems for Handling Artifacts in Complex DNA Mixtures

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If you have a question about this talk, please contact Richard Nickl .

This paper presents a coherent probabilistic framework for taking account of allelic dropout, stutter bands and silent alleles when interpreting short tandem repeat DNA profiles from a mixture sample using peak size information. This information can be exploited to make inferences regarding the genetic profiles of unknown contributors to the mixture, or for evaluating the evidential strength for a hypothesis that DNA from a particular person is present in the mixture.It extends an earlier Bayesian network approach that ignored such artifacts. We illustrate the use of the extended network on a published criminal casework example. This is joint work with R. G. Cowell and S. L. Lauritzen

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