Bayesian network tool for analyzing the cost-effectiveness of bulk forensic trace DNA profiling

Area of Science:

• Forensic Science
• Molecular Biology
• Bioinformatics

Background:

• DNA profiling has transformed forensic investigations.
• Increased demand and scalable technology present challenges for forensic laboratories managing bulk sample analysis.
• Efficient management of forensic DNA processes is crucial for timely investigations.

Purpose of the Study:

• To develop a Bayesian network for quantitative decision-making in forensic DNA laboratories.
• To analyze the cost-effectiveness of bulk trace DNA profiling.
• To identify strategies for improving the efficiency of forensic DNA profiling workflows.

Main Methods:

• Development of a Bayesian network model for decision-making.
• Analysis of over 21,000 DNA sample results from the National Bureau of Investigation Forensic Laboratory (NBI-FL) in Finland.
• Evaluation of sample type and extracted DNA quantity for cost-effectiveness.

Main Results:

• Filtering samples based on type and DNA quantity significantly reduces processed volumes with minimal risk of missing usable profiles.
• Trace DNA samples, particularly touch samples or those with low DNA quantity, have a low probability of yielding usable profiles.
• Processing low-yield DNA samples is unlikely to be cost-effective in routine forensic workflows.

Conclusions:

• Redesigning forensic sample processing protocols to prioritize certain sample types and quantities can enhance DNA profiling efficiency.
• The developed Bayesian network model offers a template for other forensic laboratories to optimize their workflows.
• Implementing data-driven filtering strategies can lead to substantial cost savings and improved investigative outcomes.