Highlights
- •Characteristics of EuroForMix and LRmix Studio were compared for LR calculations.
- •Characteristics of EuroForMix and LoCIM-tool were compared for deconvolution.
- •A total of 59 complex two–three person mixtures were evaluated.
- •EuroForMix most efficient for major/minor mixtures where the minor was evidential.
- •EuroForMix most efficient to determine genotype of major contributor.
Abstract
The investigation of the performance of models to interpret complex DNA profiles is
best undertaken using real DNA profiles. Here we used a data set to reflect the variety
typically encountered in real casework. The “crime-stains” were constructed from known
individuals and comprised a total of 59 diverse samples: pristine DNA/DNA extracted
from blood, 2–3 person mixtures, degradation/no-degradation, differences in allele
sharing, dropout/no dropout, etc. Two siblings were also included in the test-set
in order to challenge the systems. Two kinds of analyses were performed, namely tests
on whether a person of interest is a contributor based on weight-of-evidence (likelihood
ratio) calculations, and deconvolution test to estimate the profile of unknown constituent
parts. The weight-of-evidence analyses compared LRmix Studio with EuroForMix including exploration of the effect of applying an ad hoc stutter-filter. For the deconvolution analysis we compared EuroForMix with LoCIM-tool. When we classified persons of interests into being true contributors or non-contributors,
we found that EuroForMix, overall, returned a higher true positive rate for the same false positive levels
compared to LRmix. In particular, in cases with an unknown major component, EuroForMix was more discriminating for mixtures where the person of interest was a minor contributor.
Comparing deconvolution of major contributors we found that EuroForMix overall performed better than LoCIM-tool.
Keywords
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Article info
Publication history
Published online: August 03, 2016
Accepted:
July 27,
2016
Received in revised form:
July 6,
2016
Received:
April 15,
2016
Identification
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© 2016 Elsevier Ireland Ltd. All rights reserved.
