Literature DB >> 24528587

Exact computation of the distribution of likelihood ratios with forensic applications.

Guro Dørum1, Øyvind Bleka2, Peter Gill3, Hinda Haned4, Lars Snipen5, Solve Sæbø5, Thore Egeland6.   

Abstract

If complex DNA profiles, conditioned on multiple individuals are evaluated, it may be difficult to assess the strength of the evidence based on the likelihood ratio. A likelihood ratio does not give information about the relative weights that are provided by separate contributors. Alternatively, the observed likelihood ratio can be evaluated with respect to the distribution of the likelihood ratio under the defense hypothesis. We present an efficient algorithm to compute an exact distribution of likelihood ratios that can be applied to any LR-based model. The distribution may have several applications, but is used here to compute a p-value that corresponds to the observed likelihood ratio. The p-value is the probability that a profile under the defense hypothesis, substituted for a questioned contributor e.g. suspect, would attain a likelihood ratio which is at least the same magnitude as that observed. The p-value can be thought of as a scaled version of the likelihood ratio, giving a quantitative measure of the strength of the evidence relative to the specified hypotheses and the model used for the analysis. The algorithm is demonstrated on examples based on real data. R code for the algorithm is freely available in the R package euroMix.
Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

Keywords:  Complex mixtures; Distribution of likelihood ratios; Likelihood ratios; p-Values

Mesh:

Year:  2013        PMID: 24528587     DOI: 10.1016/j.fsigen.2013.11.008

Source DB:  PubMed          Journal:  Forensic Sci Int Genet        ISSN: 1872-4973            Impact factor:   4.882


  5 in total

Review 1.  Separation/extraction, detection, and interpretation of DNA mixtures in forensic science (review).

Authors:  Ruiyang Tao; Shouyu Wang; Jiashuo Zhang; Jingyi Zhang; Zihao Yang; Xiang Sheng; Yiping Hou; Suhua Zhang; Chengtao Li
Journal:  Int J Legal Med       Date:  2018-05-25       Impact factor: 2.686

2.  Four model variants within a continuous forensic DNA mixture interpretation framework: Effects on evidential inference and reporting.

Authors:  Harish Swaminathan; Muhammad O Qureshi; Catherine M Grgicak; Ken Duffy; Desmond S Lun
Journal:  PLoS One       Date:  2018-11-20       Impact factor: 3.240

3.  Noninvasive Prenatal Paternity Testing (NIPAT) through Maternal Plasma DNA Sequencing: A Pilot Study.

Authors:  Haojun Jiang; Yifan Xie; Xuchao Li; Huijuan Ge; Yongqiang Deng; Haofang Mu; Xiaoli Feng; Lu Yin; Zhou Du; Fang Chen; Nongyue He
Journal:  PLoS One       Date:  2016-09-15       Impact factor: 3.240

4.  Efficient construction of match strength distributions for uncertain multi-locus genotypes.

Authors:  Mark W Perlin
Journal:  Heliyon       Date:  2018-10-08

5.  Lab Retriever: a software tool for calculating likelihood ratios incorporating a probability of drop-out for forensic DNA profiles.

Authors:  Keith Inman; Norah Rudin; Ken Cheng; Chris Robinson; Adam Kirschner; Luke Inman-Semerau; Kirk E Lohmueller
Journal:  BMC Bioinformatics       Date:  2015-09-18       Impact factor: 3.169

  5 in total

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