Statistical analysis of replicate measurements of DNA mixtures
J.F. Koks (TU Delft - Electrical Engineering, Mathematics and Computer Science)
J. Söhl – Mentor (TU Delft - Electrical Engineering, Mathematics and Computer Science)
R.J.F. Ypma – Mentor (Nederlands Forensisch Instituut (NFI))
D. Kurowicka – Graduation committee member (TU Delft - Electrical Engineering, Mathematics and Computer Science)
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Abstract
At the Netherlands Forensic Institute, additional replicate measurements of the same DNA trace, referred to as rework, can be performed to obtain more information from a DNA mixture profile. Rework may increase the evidential value, expressed by the likelihood ratio (LR), but it also costs laboratory time, resources, and DNA sample material. This thesis investigates whether the LR after rework can be predicted from the original DNA mixture profile.
Two main contributions were made. First, a simulation framework was developed to construct predictive distributions for the rework LR. Starting from the deconvolution of the original profile, plausible contributor genotypes are sampled, additional replicate profiles are simulated, and the LR of the combined profile is calculated. Second, a Bayesian MCMC implementation was developed for the EuroForMix/DNAStatistX peak-height model, making it possible to propagate uncertainty in the nuisance parameters when computing LR values.
The framework was evaluated on cleaned two-person NFI research data, focusing on minor contributors. The frequentist plug-in simulation was not sufficiently calibrated: nominal 95% prediction intervals covered only 69.0% of the observed minor true-donor rework LRs. Including Bayesian parameter uncertainty improved the empirical coverage to 81.6% and reduced the mean interval score from 50.5 to 21.6. However, the predicted distributions remained insufficiently calibrated for casework use.
Overall, this thesis shows that predicting rework LRs is possible in principle and that parameter uncertainty is important for such predictions. The current framework should be viewed as a mathematical proof of concept rather than an operational tool. Further work is needed on artefact modelling, computational scaling, full MCMC validation, extension to more complex mixtures, and validation on casework-like data.