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

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...

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Autores principales:	Sarah Riman, Hari Iyer, Peter M Vallone
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/c55011d1ed804c8c993e34206a76699e
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spelling	oai:doaj.org-article:c55011d1ed804c8c993e34206a76699e2021-12-02T20:14:32ZExamining performance and likelihood ratios for two likelihood ratio systems using the PROVEDIt dataset.1932-620310.1371/journal.pone.0256714https://doaj.org/article/c55011d1ed804c8c993e34206a76699e2021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0256714https://doaj.org/toc/1932-6203A 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.Sarah RimanHari IyerPeter M VallonePublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 9, p e0256714 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Sarah Riman Hari Iyer Peter M Vallone Examining performance and likelihood ratios for two likelihood ratio systems using the PROVEDIt dataset.
description	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.
format	article
author	Sarah Riman Hari Iyer Peter M Vallone
author_facet	Sarah Riman Hari Iyer Peter M Vallone
author_sort	Sarah Riman
title	Examining performance and likelihood ratios for two likelihood ratio systems using the PROVEDIt dataset.
title_short	Examining performance and likelihood ratios for two likelihood ratio systems using the PROVEDIt dataset.
title_full	Examining performance and likelihood ratios for two likelihood ratio systems using the PROVEDIt dataset.
title_fullStr	Examining performance and likelihood ratios for two likelihood ratio systems using the PROVEDIt dataset.
title_full_unstemmed	Examining performance and likelihood ratios for two likelihood ratio systems using the PROVEDIt dataset.
title_sort	examining performance and likelihood ratios for two likelihood ratio systems using the provedit dataset.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/c55011d1ed804c8c993e34206a76699e
work_keys_str_mv	AT sarahriman examiningperformanceandlikelihoodratiosfortwolikelihoodratiosystemsusingtheproveditdataset AT hariiyer examiningperformanceandlikelihoodratiosfortwolikelihoodratiosystemsusingtheproveditdataset AT petermvallone examiningperformanceandlikelihoodratiosfortwolikelihoodratiosystemsusingtheproveditdataset
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Examining performance and likelihood ratios for two likelihood ratio systems using the PROVEDIt dataset.

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