City Research Online

Abstract

This paper presents a coherent probabilistic framework for taking account of allelic dropout, stutter bands and silent alleles when interpreting STR DNA profiles from a mixture sample using peak size information arising from a PCR analysis. This information can be exploited for evaluating the evidential strength for a hypothesis that DNA from a particular person is present in the mixture. It extends an earlier Bayesian network approach that ignored such artifacts. We illustrate the use of the extended network on a published casework example.

Publication Type:	Article
Additional Information:	NOTICE: this is the author’s version of a work that was accepted for publication in Forensic Science International: Genetics. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Forensic Science International: Genetics, Volume 5, Issue 3, June 2011, Pages 202–209, http://dx.doi.org/10.1016/j.fsigen.2010.03.008
Publisher Keywords:	Allelic dropout; Artifacts; Bayesian networks; DNA mixtures; Peak area; Probabilistic expert systems; silent alleles; STR marker; stutter bands
Subjects:	Q Science > QA Mathematics Q Science > QH Natural history > QH426 Genetics
Departments:	Bayes Business School > Actuarial Science & Insurance
Related URLs:	Author
SWORD Depositor:	Symplectic Administrator

Export

Downloads