Using DNA traces obtained from firearms to distinguish between active use and passive contact with weapons

Abstract

Firearm-related crime presents a significant challenge in forensic investigations, particularly when in court indirect contact is invoked as an alternative explanation for the presence of DNA on a weapon. This study aimed to characterise location-specific DNA distribution patterns from different firearm handling scenarios and to assess whether these patterns can support activity-level evaluation in casework.
Four experimental scenarios were conducted using semi-automatic pistols: controlled and semi-controlled direct transfer via loading and shooting (handling) and indirect transfer via a personal towel or shirt (storage). DNA traces were collected by swabbing 13 locations at each firearm, quantified by qPCR and profiled by STR analysis. A random forest machine learning (ML) classifier was trained on the combined qPCR and STR parameters across all targeted locations to distinguish between direct and indirect transfer. A simulated crime was performed on two firearms with unknown handling history to validate the experimental design under casework like conditions.
Decontamination with the sodium hypochlorite and ethanol protocol applied with a toothbrush was successfully shown to reduce background DNA below the detection limit across all firearm surfaces. In controlled loading and shooting, DNA was concentrated mostly at the grip and magazine surfaces, whereas semi-controlled scenario had distributions dominated by the grip and slide back. Indirect transfer via personal belongings yielded lower total DNA concentrations compared to direct handling.
The random forest (RF) classifier achieved 99,4% ± 1,7% accuracy trained and validated on real data and 98,6% ± 3,4% trained and validated on augmented and real data. The high accuracy can be a consequence of using only two classes (binary classification), data provided to RF was from only controlled studies or too many features caused RF to learn outliers and noise of dataset (data overfitting). It should be interpreted as a proof-of-concept results rather than an indication of performance in real casework conditions. ML model showed that the magazine lip and body, and frame front left were the discriminating locations to determine between direct and indirect transfer. The simulated crime examination produced distribution patterns consistent with controlled experiments, with both models correctly classifying all contributors with their handling scenario.

Popular Abstract

The silent witness: what DNA found on a firearm tells about how it was used
When a firearm is recovered from a crime scene, finding DNA on it is only the beginning. The harder question – and one that starts to matter more in a courtroom – is what the firearm was used for.
DNA is the genetic material found in almost every cell of the human body. Every time we touch an object, we leave invisible traces of it behind. Finding the DNA of a suspect on a weapon does not mean they fired it. They might have touched it briefly or their DNA could have transferred indirectly through for example shared gloves. Currently, forensic scientists have limited tools to distinguish between these explanations.
I addressed that gap by comparing the distribution of DNA on a pistol after two different types of contact: active use (loading and shooting) and passive contact, in which the weapon was wrapped in a personal item such as a towel or shirt and carried in a bag. I collected DNA from 13 specific locations on the weapon, measured their amounts and genetic profiles of volunteers were obtained where possible.
The obtained patterns were informative, though not always straightforward to interpret. Loading and shooting concentrated DNA mostly on the grip and magazine – areas repeatedly contacted during these actions. In contrast, passive contact produced a more even distribution across the weapon, with DNA absent from the magazine. That absence turned out to be one of the most reliable indicators of how the weapon had been handled.
To make sense of the complex patterns across all 13 locations, I first attempted manual interpretation, then tested simpler measures such as the grip-to-magazine ratio. Both performed worse, misclassifying a substantial number of active-use samples as passive contact. In a legal setting, that kind of error could mean failing to identify the person who actually fired the weapon. Machine learning – a tool that helps computers find patterns in complex data that are difficult for humans to interpret directly – proved far more effective, distinguishing the two scenarios with near 100% accuracy.
I also tested the developed ML model on two firearms handled by volunteers under conditions resembling real casework. The DNA patterns matched what the controlled experiments had shown and the algorithm correctly classified both weapons as active use and passive contact, respectively.
These findings suggest that the amount and position of DNA found on a firearm, not just whose DNA it is, can serve as powerful evidence. If developed further, this kind of analysis could give forensic scientists a more precise basis for their expert testimonies in court and a better way to distinguish the person who pulled the trigger from the one who simply found themselves, or at least their DNA, in the wrong place at the wrong time.

• author: Dabrowski, Tomasz
• supervisor: Johannes Hedman; Sofie Bergstrand; Julia Bengtsson
• organization: Applied Microbiology; Biotechnology; Biotechnology (MSc)
• course: KMBM01 20261
• year: 2026
• type: H2 - Master's Degree (Two Years)
• subject: Biology and Life Sciences
• keywords: Activity, DNA distribution, DNA trace, short tandem repeats, STR, STR profiling, PCR, Machine Learning, ML, Applied microbiology
• language: English
• id: 9234778
• date added to LUP: 2026-06-10 15:23:02
• date last changed: 2026-06-10 15:23:02