Academic staff at the Department of Mathematical Sciences – University of Copenhagen

Analysis of Forensic DNA Mixtures with Artefacts

Research output: Contribution to journalJournal articleResearchpeer-review

Robert G. Cowell, Therese Graversen, Steffen L. Lauritzen, Julia Mortera

DNA is now routinely used in criminal investigations and court cases, although DNA samples taken at crime scenes 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 of a forensic DNA sample and illustrate its potential use for the analysis of criminal cases. In contrast with most previously used methods, we directly model the peak height information and incorporate important artefacts that are associated with the production of the electropherogram. Our model has a number of unknown parameters, and we show that these can be estimated by the method of maximum likelihood in the presence of multiple unknown individuals contributing to the sample, and their approximate standard errors calculated; the computations exploit a Bayesian network representation of the model. A case example from a UK trial, as reported in the literature, is used to illustrate the efficacy and use of the model, both in finding likelihood ratios to quantify the strength of evidence, and in the deconvolution of mixtures for finding likely profiles of the individuals contributing to the sample. Our model is readily extended to simultaneous analysis of more than one mixture as illustrated in a case example. We show that the combination of evidence from several samples may give an evidential strength which is close to that of a single-source trace and thus modelling of peak height information provides a potentially very efficient mixture analysis.
Original languageEnglish
JournalJournal of the Royal Statistical Society, Series C (Applied Statistics)
Issue number1
Pages (from-to)1-48
StatePublished - 2015
Externally publishedYes

ID: 129775044