A likelihood ratio-based approach for improved source attribution in microbiological forensic investigations

Area of Science:

• Microbial Forensics
• Genomic Epidemiology
• Molecular Biology

Background:

• Identifying the origin of pathogenic bacteria is crucial in forensic investigations.
• Current methods lack standardized approaches for quantifying and communicating DNA evidence in microbial forensics.
• Existing likelihood-ratio methods in human DNA matching offer a successful model for interpretation and communication.

Purpose of the Study:

• To develop a concept for calculating probabilistic evidential values for microbial forensic hypotheses.
• To apply this concept to real-world microbial forensic case scenarios.
• To enhance the interpretation and communication of whole genome sequencing data in forensic investigations.

Main Methods:

• Developed a likelihood-ratio method for calculating probabilistic evidential values.
• Applied whole genome sequencing (WGS) data, including low-frequency variants and structural variants (SVs).
• Utilized two case studies: *Francisella tularensis* serial passage and *Listeria monocytogenes* outbreak source tracking.

Main Results:

• Successfully assigned fictive *F. tularensis* samples to correct origin batches using SV data.
• Demonstrated that highly similar bacterial strains can be differentiated using WGS-derived mutational patterns.
• Conclusively assigned *L. monocytogenes* clinical samples to specific sources of origin.

Conclusions:

• Bacterial WGS data, including SNP and SV signatures, can be used to calculate robust evidential values.
• The proposed concept facilitates the interpretation and communication of microbial forensic findings.
• This approach has broad applicability in epidemiological and forensic source tracking of infectious disease outbreaks.