Bayesian Forensic DNA Mixture Deconvolution Using a Novel String Similarity Measure

TL;DR

This paper introduces a Bayesian method utilizing a novel string similarity measure to improve forensic DNA mixture deconvolution with massively parallel sequencing data, enhancing the accuracy of identifying specific individuals in complex samples.

Contribution

The work presents a new Bayesian framework and a string edit distance metric tailored for MPS-based DNA mixture analysis, advancing forensic interpretation techniques.

Findings

Bayes factors effectively distinguish samples with or without the POI.

The method shows strong performance in hypothesis testing.

Enhanced discrimination in complex DNA mixtures.

Abstract

Mixture interpretation is a central challenge in forensic science, where evidence often contains contributions from multiple sources. In the context of DNA analysis, biological samples recovered from crime scenes may include genetic material from several individuals, necessitating robust statistical tools to assess whether a specific person of interest (POI) is among the contributors. Methods based on capillary electrophoresis (CE) are currently in use worldwide, but offer limited resolution in complex mixtures. Advancements in massively parallel sequencing (MPS) technologies provide a richer, more detailed representation of DNA mixtures, but require new analytical strategies to fully leverage this information. In this work, we present a Bayesian framework for evaluating whether a POIs DNA is present in an MPS-based forensic sample. The model accommodates known contributors, such as the victim, and uses a novel string edit distance to quantify similarity between observed alleles and sequencing artifacts. The resulting Bayes factors enable effective discrimination between samples that do and do not contain the POIs DNA, demonstrating strong performance in both hypothesis testing and classification settings.