EFMex: Using EuroForMix to evaluate DNA mixtures with multiple persons of interest

We present EFMex (EuroForMix-Exhaustive), an R package for calculating likelihood ratios (LRs) in DNA mixture interpretation involving multiple persons of interest (POIs). The software implements an exhaustive method framework, evaluating all possible subsets of POIs as contributors to the mixture. We demonstrate the importance of this framework, particularly when contributors, such as close relatives, exhibit high allele sharing. A series of simulation experiments, designed to reflect complex casework scenarios, were conducted using three- and four-person mixtures in which at least two contributors were in the same family. The exhaustive method proved effective in distinguishing true contributors from closely related non-contributors, even under challenging conditions. A recalculation of exhaustive LR for candidates obtaining LR>1 with the exhaustive method was used to increase discrimination further. An alternative to the exhaustive method, using a generalized likelihood ratio, returned values very close to those of the exhaustive method. A Shiny app was created as a graphical user interface to ease the analysis for practitioners.

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Publication Details

Journal
Forensic Science International Genetics
Published
2025-09-23
DOI
https://doi.org/10.1016/j.fsigen.2025.103365
Primary Topic
Forensic and Genetic Research
Type
article
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article

EFMex: Using EuroForMix to evaluate DNA mixtures with multiple persons of interest

Magnus Dehli Vigeland, Øyvind Bleka, Peter Gill
Forensic Science International Genetics
Forensic and Genetic Research
article

EFMex: Using EuroForMix to evaluate DNA mixtures with multiple persons of interest

Magnus Dehli Vigeland, Øyvind Bleka, Peter Gill
article en

Abstract

We present EFMex (EuroForMix-Exhaustive), an R package for calculating likelihood ratios (LRs) in DNA mixture interpretation involving multiple persons of interest (POIs). The software implements an exhaustive method framework, evaluating all possible subsets of POIs as contributors to the mixture. We demonstrate the importance of this framework, particularly when contributors, such as close relatives, exhibit high allele sharing. A series of simulation experiments, designed to reflect complex casework scenarios, were conducted using three- and four-person mixtures in which at least two contributors were in the same family. The exhaustive method proved effective in distinguishing true contributors from closely related non-contributors, even under challenging conditions. A recalculation of exhaustive LR for candidates obtaining LR>1 with the exhaustive method was used to increase discrimination further. An alternative to the exhaustive method, using a generalized likelihood ratio, returned values very close to those of the exhaustive method. A Shiny app was created as a graphical user interface to ease the analysis for practitioners.

Forensic Science International GeneticsVol. 81
Oslo University Hospital (NO)
Openalex Percentile: Top 72%
Forensic and Genetic Research
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