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

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  • Sarah Riman
  • Hari Iyer
  • Peter M Vallone

Abstract

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 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.

Suggested Citation

  • Sarah Riman & Hari Iyer & Peter M Vallone, 2021. "Examining performance and likelihood ratios for two likelihood ratio systems using the PROVEDIt dataset," PLOS ONE, Public Library of Science, vol. 16(9), pages 1-30, September.
    • Handle: RePEc:plo:pone00:0256714
    DOI: 10.1371/journal.pone.0256714
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    References listed on IDEAS

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    1. R. G. Cowell & T. Graversen & S. L. Lauritzen & J. Mortera, 2015. "Analysis of forensic DNA mixtures with artefacts," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 64(1), pages 1-48, January.
    2. Sho Manabe & Chie Morimoto & Yuya Hamano & Shuntaro Fujimoto & Keiji Tamaki, 2017. "Development and validation of open-source software for DNA mixture interpretation based on a quantitative continuous model," PLOS ONE, Public Library of Science, vol. 12(11), pages 1-18, November.
    3. Harish Swaminathan & Muhammad O Qureshi & Catherine M Grgicak & Ken Duffy & Desmond S Lun, 2018. "Four model variants within a continuous forensic DNA mixture interpretation framework: Effects on evidential inference and reporting," PLOS ONE, Public Library of Science, vol. 13(11), pages 1-23, November.
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