Skip to main content
U.S. flag

An official website of the United States government

Official websites use .gov
A .gov website belongs to an official government organization in the United States.

Secure .gov websites use HTTPS
A lock ( ) or https:// means you’ve safely connected to the .gov website. Share sensitive information only on official, secure websites.

The factor of 10 in forensic DNA match probabilities

Published

Author(s)

Simone N. Gittelson, Tamyra R. Moretti, Anthony J. Onorato, Bruce Budowle, Bruce S. Weir, John Buckleton

Abstract

An update was performed of the classic experiments that led to the view that profile probability estimates are usually within a factor of 10 of each other. The data used in this study consist of 15 Identifiler loci collected from forensic publications of a wide range of populations. Following Budowle et al the terms cognate and non-cognate are used. The term cognate refers to the database in which the profile was simulated. The profile probability assignment was usually larger in the cognate database. In about half the cases, the profile probability in the non-cognate database was different from the profile probability in the cognate database by a factor greater than 10. This proportion was between 20% and 40% when the FBI and NIST data were used as the non-cognate databases. A second experiment compared the match probability assignment using a generalized database and recommendation 4.2 from NRC II (the 4.2 assignment) with a proxy for the true answer developed using subpopulation allele frequencies and the product rule. The findings support that the 4.2 assignment has a large conservative bias.
Citation
Forensic Science International: Genetics

Keywords

Subpopulations, allele frequency, database, weight of evidence.

Citation

Gittelson, S. , Moretti, T. , Onorato, A. , Budowle, B. , Weir, B. and Buckleton, J. (2017), The factor of 10 in forensic DNA match probabilities, Forensic Science International: Genetics, [online], https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=920279 (Accessed March 29, 2024)
Created February 16, 2017, Updated January 27, 2020