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Paternity testing and other inference about relationships from DNA mixtures

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FOSW02 - Bayesian networks and argumentation in evidence analysis

DNA is now routinely used in criminal  and civil investigations.  DNA samples are of varying quality and therefore present challenging problems for their interpretation. We present a statistical model for the quantitative peak information obtained from an electropherogram (EPG) of a forensic DNA sample and illustrate its potential use for the analysis of civil and criminal cases. In contrast to most previously used methods, we directly model the peak height information and incorporate important artefacts associated with the production of the EPG . The model has a number of unknown parameters, that  can be estimated in the presence of multiple unknown contributors; the computations exploit a Bayesian network   representation of the model.

We illustrate real casework examples from a criminal case and a disputed paternity case, where in both cases part of the evidence was from a DNA mixture.   We present methods for inference about the relationships between contributors to a DNA mixture of unknown genotype and other individuals of known genotype: a basic example would be testing whether a contributor to a mixture is the father of a child of known genotype (or indeed the similar question with the roles of parent and child reversed). Following commonly accepted practice, the evidence for such a relationship is presented as the likelihood ratio for the specified relationship versus the alternative that there is no such relationship, so the father is taken to be a random member of the population. Our methods are based on the statistical model for DNA mixtures, in which a Bayesian network is used as a computational device for efficiently computing likelihoods; the present work builds on that approach, but makes more explicit use of the BN in the modelling.
Based on joint work with Peter Green.

This talk is part of the Isaac Newton Institute Seminar Series series.

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