Literature DB >> 20888313

Forensic identification of an individual in complex DNA mixtures.

Lev Voskoboinik1, Ariel Darvasi.   

Abstract

The identification of a suspect in a complex DNA mixture typed with standard short tandem repeat (STR) kits has proved difficult. In the current study we present the theoretical framework of a method aimed to resolve this problem in forensic cases. The method suggests genotyping a specially designed panel of 1000-3000 single nucleotide polymorphisms (SNPs), each with a relatively low (<0.1) minor allele frequency (MAF). The rationale of this method is that any individual will carry a specific set of dozens of rare alleles and the complex DNA mixture will carry this particular set only if the one individual is represented in the DNA mixture. The efficiency of the method is evaluated by estimating the probability that a random man will not be excluded (RMNE) from the mixture. When this probability, P(RMNE), is low, one can conclude that the suspect's DNA is present in the DNA mixture. Essentially, a P(RMNE)<10(-9) is considered as proof, whereas a P(RMNE)<10(-6) is considered strong evidence. For completeness, we also analyzed the method using the likelihood ratio (LR) approach. We have analyzed the method for a variety of conditions and found that generally the method will provide highly significant results even for complex mixtures combining up to 10 individuals. The method performs well even when close relatives (one or two brothers) are present in the complex DNA mixture and when contributors or suspects come from different populations. We have also found that the method can accurately identify the number of contributors to the mixture, something that in some instances has significant forensic value on its own.
Copyright © 2010 Elsevier Ireland Ltd. All rights reserved.

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Year:  2010        PMID: 20888313     DOI: 10.1016/j.fsigen.2010.09.002

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


  9 in total

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2.  Enhanced mixture interpretation with macrohaplotypes based on long-read DNA sequencing.

Authors:  Jianye Ge; Jonathan King; Sammed Mandape; Bruce Budowle
Journal:  Int J Legal Med       Date:  2021-08-11       Impact factor: 2.686

3.  Haplotype Counting for Sensitive Chimerism Testing: Potential for Early Leukemia Relapse Detection.

Authors:  Marija Debeljak; Evelina Mocci; Max C Morrison; Aparna Pallavajjalla; Katie Beierl; Marie Amiel; Michaël Noë; Laura D Wood; Ming-Tseh Lin; Christopher D Gocke; Alison P Klein; Ephraim J Fuchs; Richard J Jones; James R Eshleman
Journal:  J Mol Diagn       Date:  2017-05       Impact factor: 5.568

4.  Differential DNA extraction of challenging simulated sexual-assault samples: a Swiss collaborative study.

Authors:  Séverine Vuichard; Urs Borer; Michel Bottinelli; Christian Cossu; Naseem Malik; Verena Meier; Christian Gehrig; Andrea Sulzer; Marie-Laure Morerod; Vincent Castella
Journal:  Investig Genet       Date:  2011-05-04

Review 5.  The National DNA Data Bank of Canada: a Quebecer perspective.

Authors:  Emmanuel Milot; Marie M J Lecomte; Hugo Germain; Frank Crispino
Journal:  Front Genet       Date:  2013-11-20       Impact factor: 4.599

6.  Graph Algorithms for Mixture Interpretation.

Authors:  Benjamin Crysup; August E Woerner; Jonathan L King; Bruce Budowle
Journal:  Genes (Basel)       Date:  2021-01-27       Impact factor: 4.096

7.  DIP-STR: highly sensitive markers for the analysis of unbalanced genomic mixtures.

Authors:  Vincent Castella; Joëlle Gervaix; Diana Hall
Journal:  Hum Mutat       Date:  2013-03-05       Impact factor: 4.878

8.  Current developments in forensic interpretation of mixed DNA samples (Review).

Authors:  Na Hu; Bin Cong; Shujin Li; Chunling Ma; Lihong Fu; Xiaojing Zhang
Journal:  Biomed Rep       Date:  2014-01-28

9.  Evaluation of parameters in mixed male DNA profiles for the Identifiler® multiplex system.

Authors:  Na Hu; Bin Cong; Tao Gao; Rong Hu; Yi Chen; Hui Tang; Luyan Xue; Shujin Li; Chunling Ma
Journal:  Int J Mol Med       Date:  2014-05-12       Impact factor: 4.101

  9 in total

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