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Effect of multiple allelic drop-outs in forensic RMNE calculations

Christophe Van Neste UGent, Dieter Deforce UGent and Filip Van Nieuwerburgh UGent (2015) FORENSIC SCIENCE INTERNATIONAL-GENETICS. 19. p.243-249
abstract
Technological advances such as massively parallel sequencing enable increasing amounts of genetic information to be obtained from increasingly challenging samples. Certainly on low template, degraded and multi-contributor samples, drop-outs will increase in number for many profiles simply by analyzing more loci, making it difficult to probabilistically assess how many drop-outs have occurred and at which loci they might have occurred. Previously we developed a Random Man Not Excluded (RMNE) method that can take into account allelic drop-out while avoiding detailed estimations of the probability that drop-outs have occurred, nor making assumptions about at which loci these drop-outs might have occurred. The number of alleles that have dropped out, does not need to be exactly known. Here we report a generic Python algorithm to calculate the RMNE probabilities for any given number of loci. The number of allowed drop-outs can be set between 0 and twice the number of analyzed loci. The source code has been made available on https://github.com/fvnieuwe/rmne. An online web-based RMNE calculation tool has been made available on http://forensic.ugent.be/rmne. The tool can calculate these RMNE probabilities from a custom list of probabilities of the observed and non-observed alleles from any given number of loci. Using this tool, we explored the effect of allowing allelic drop-outs on the evidential value of random forensic profiles with a varying number of loci. Our results give insight into how the number of allowed drop-outs affects the evidential value of a profile and how drop-out can be managed in the RMNE approach.
Please use this url to cite or link to this publication:
author
organization
year
type
journalArticle (original)
publication status
published
subject
keyword
BELGIAN POPULATION, DNA PROFILES, LOCI, FREQUENCIES
journal title
FORENSIC SCIENCE INTERNATIONAL-GENETICS
Forensic Sci. Int.-Genet.
volume
19
pages
243 - 249
Web of Science type
Article
Web of Science id
000367510300043
JCR category
MEDICINE, LEGAL
JCR impact factor
4.988 (2015)
JCR rank
1/15 (2015)
JCR quartile
1 (2015)
ISSN
1872-4973
DOI
10.1016/j.fsigen.2015.08.001
language
English
UGent publication?
yes
classification
A1
copyright statement
Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)
id
7053508
handle
http://hdl.handle.net/1854/LU-7053508
date created
2016-01-26 11:23:25
date last changed
2017-06-09 08:42:09
@article{7053508,
  abstract     = {Technological advances such as massively parallel sequencing enable increasing amounts of genetic information to be obtained from increasingly challenging samples. Certainly on low template, degraded and multi-contributor samples, drop-outs will increase in number for many profiles simply by analyzing more loci, making it difficult to probabilistically assess how many drop-outs have occurred and at which loci they might have occurred. Previously we developed a Random Man Not Excluded (RMNE) method that can take into account allelic drop-out while avoiding detailed estimations of the probability that drop-outs have occurred, nor making assumptions about at which loci these drop-outs might have occurred. The number of alleles that have dropped out, does not need to be exactly known. Here we report a generic Python algorithm to calculate the RMNE probabilities for any given number of loci. The number of allowed drop-outs can be set between 0 and twice the number of analyzed loci. The source code has been made available on https://github.com/fvnieuwe/rmne. An online web-based RMNE calculation tool has been made available on http://forensic.ugent.be/rmne. The tool can calculate these RMNE probabilities from a custom list of probabilities of the observed and non-observed alleles from any given number of loci. Using this tool, we explored the effect of allowing allelic drop-outs on the evidential value of random forensic profiles with a varying number of loci. Our results give insight into how the number of allowed drop-outs affects the evidential value of a profile and how drop-out can be managed in the RMNE approach.},
  author       = {Van Neste, Christophe and Deforce, Dieter and Van Nieuwerburgh, Filip},
  issn         = {1872-4973},
  journal      = {FORENSIC SCIENCE INTERNATIONAL-GENETICS},
  keyword      = {BELGIAN POPULATION,DNA PROFILES,LOCI,FREQUENCIES},
  language     = {eng},
  pages        = {243--249},
  title        = {Effect of multiple allelic drop-outs in forensic RMNE calculations},
  url          = {http://dx.doi.org/10.1016/j.fsigen.2015.08.001},
  volume       = {19},
  year         = {2015},
}

Chicago
Van Neste, Christophe, Dieter Deforce, and Filip Van Nieuwerburgh. 2015. “Effect of Multiple Allelic Drop-outs in Forensic RMNE Calculations.” Forensic Science International-genetics 19: 243–249.
APA
Van Neste, C., Deforce, D., & Van Nieuwerburgh, F. (2015). Effect of multiple allelic drop-outs in forensic RMNE calculations. FORENSIC SCIENCE INTERNATIONAL-GENETICS, 19, 243–249.
Vancouver
1.
Van Neste C, Deforce D, Van Nieuwerburgh F. Effect of multiple allelic drop-outs in forensic RMNE calculations. FORENSIC SCIENCE INTERNATIONAL-GENETICS. 2015;19:243–9.
MLA
Van Neste, Christophe, Dieter Deforce, and Filip Van Nieuwerburgh. “Effect of Multiple Allelic Drop-outs in Forensic RMNE Calculations.” FORENSIC SCIENCE INTERNATIONAL-GENETICS 19 (2015): 243–249. Print.