Examining performance and likelihood ratios for two likelihood ratio systems using the PROVEDIt dataset.


Journal

PloS one
ISSN: 1932-6203
Titre abrégé: PLoS One
Pays: United States
ID NLM: 101285081

Informations de publication

Date de publication:
2021
Historique:
received: 16 06 2021
accepted: 07 08 2021
entrez: 17 9 2021
pubmed: 18 9 2021
medline: 19 11 2021
Statut: epublish

Résumé

A likelihood ratio (LR) system is defined as the entire pipeline of the measurement and interpretation processes where probabilistic genotyping software (PGS) is a piece of the whole LR system. To gain understanding on how two LR systems perform, a total of 154 two-person, 147 three-person, and 127 four-person mixture profiles of varying DNA quality, DNA quantity, and mixture ratios were obtained from the filtered (.CSV) files of the GlobalFiler 29 cycles 15s PROVEDIt dataset and deconvolved in two independently developed fully continuous programs, STRmix v2.6 and EuroForMix v2.1.0. Various parameters were set in each software and LR computations obtained from the two software were based on same/fixed EPG features, same pair of propositions, number of contributors, theta, and population allele frequencies. The ability of each LR system to discriminate between contributor (H1-true) and non-contributor (H2-true) scenarios was evaluated qualitatively and quantitatively. Differences in the numeric LR values and their corresponding verbal classifications between the two LR systems were compared. The magnitude of the differences in the assigned LRs and the potential explanations for the observed differences greater than or equal to 3 on the log10 scale were described. Cases of LR < 1 for H1-true tests and LR > 1 for H2-true tests were also discussed. Our intent is to demonstrate the value of using a publicly available ground truth known mixture dataset to assess discrimination performance of any LR system and show the steps used to understand similarities and differences between different LR systems. We share our observations with the forensic community and describe how examining more than one PGS with similar discrimination power can be beneficial, help analysts compare interpretation especially with low-template profiles or minor contributor cases, and be a potential additional diagnostic check even if software in use does contain certain diagnostic statistics as part of the output.

Identifiants

pubmed: 34534241
doi: 10.1371/journal.pone.0256714
pii: PONE-D-21-18672
pmc: PMC8448353
doi:

Substances chimiques

DNA 9007-49-2

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

e0256714

Déclaration de conflit d'intérêts

The authors declare no conflict of interest.

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Auteurs

Sarah Riman (S)

Applied Genetics Group, National Institute of Standards and Technology, Gaithersburg, Maryland, United States of America.

Hari Iyer (H)

Statistical Design, Analysis, Modeling Group, National Institute of Standards and Technology, Gaithersburg, Maryland, United States of America.

Peter M Vallone (PM)

Applied Genetics Group, National Institute of Standards and Technology, Gaithersburg, Maryland, United States of America.

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