Probabilistic genotyping software: An overview.

DNA mixture Forensic DNA Interpretation Mixture software Probabilistic genotyping Validation

Journal

Forensic science international. Genetics
ISSN: 1878-0326
Titre abrégé: Forensic Sci Int Genet
Pays: Netherlands
ID NLM: 101317016

Informations de publication

Date de publication:
01 2019
Historique:
received: 23 10 2018
revised: 07 11 2018
accepted: 07 11 2018
pubmed: 21 11 2018
medline: 27 1 2019
entrez: 21 11 2018
Statut: ppublish

Résumé

The interpretation of mixed profiles from DNA evidentiary material is one of the more challenging duties of the forensic scientist. Traditionally, analysts have used a "binary" approach to interpretation where inferred genotypes are either included or excluded from the mixture using a stochastic threshold and other biological parameters such as heterozygote balance, mixture ratio, and stutter ratios. As the sensitivity of STR multiplexes and capillary electrophoresis instrumentation improved over the past 25 years, coupled with the change in the type of evidence being submitted for analysis (from high quality and quantity (often single-source) stains to low quality and quantity (often mixed) "touch" samples), the complexity of DNA profile interpretation has equally increased. This review provides a historical perspective on the movement from binary methods of interpretation to probabilistic methods of interpretation. We describe the two approaches to probabilistic genotyping (semi-continuous and fully continuous) and address issues such as validation and court acceptance. Areas of future needs for probabilistic software are discussed.

Identifiants

pubmed: 30458407
pii: S1872-4973(18)30552-0
doi: 10.1016/j.fsigen.2018.11.009
pii:
doi:

Substances chimiques

DNA 9007-49-2

Types de publication

Journal Article Research Support, U.S. Gov't, Non-P.H.S. Review

Langues

eng

Sous-ensembles de citation

IM

Pagination

219-224

Informations de copyright

Copyright © 2018 Elsevier B.V. All rights reserved.

Auteurs

Michael D Coble (MD)

Center for Human Identification, Department of Microbiology, Immunology, and Genetics, University of North Texas Health Science Center, 3500 Camp Bowie Blvd., Fort Worth, TX 76107, USA. Electronic address: michael.coble@unthsc.edu.

Jo-Anne Bright (JA)

Institute of Environmental Science and Research Limited, Private Bag 92021, Auckland, 1142 New Zealand.

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Classifications MeSH