title: Error rates in forensic DNA analysis: definition, numbers, impact and communication.
abstract: Forensic DNA casework is currently regarded as one of the most important types of forensic evidence, and important decisions in intelligence and justice are based on it. However, errors occasionally occur and may have very serious consequences. In other domains, error rates have been defined and published. The forensic domain is lagging behind concerning this transparency for various reasons. In this paper we provide definitions and observed frequencies for different types of errors at the Human Biological Traces Department of the Netherlands Forensic Institute (NFI) over the years 2008-2012. Furthermore, we assess their actual and potential impact and describe how the NFI deals with the communication of these numbers to the legal justice system. We conclude that the observed relative frequency of quality failures is comparable to studies from clinical laboratories and genetic testing centres. Furthermore, this frequency is constant over the five-year study period. The most common causes of failures related to the laboratory process were contamination and human error. Most human errors could be corrected, whereas gross contamination in crime samples often resulted in irreversible consequences. Hence this type of contamination is identified as the most significant source of error. Of the known contamination incidents, most were detected by the NFI quality control system before the report was issued to the authorities, and thus did not lead to flawed decisions like false convictions. However in a very limited number of cases crucial errors were detected after the report was issued, sometimes with severe consequences. Many of these errors were made in the post-analytical phase. The error rates reported in this paper are useful for quality improvement and benchmarking, and contribute to an open research culture that promotes public trust. However, they are irrelevant in the context of a particular case. Here case-specific probabilities of undetected errors are needed. These should be reported, separately from the match probability, when requested by the court or when there are internal or external indications for error. It should also be made clear that there are various other issues to consider, like DNA transfer. Forensic statistical models, in particular Bayesian networks, may be useful to take the various uncertainties into account and demonstrate their effects on the evidential value of the forensic DNA results.
authors.affiliation: Department of Human Biological Traces (HBS), Netherlands Forensic Institute, P.O. Box 24044, 2490 AA The Hague, The Netherlands; Department of Science, Interdisciplinary Research, Statistics and Knowledge Management (WISK), Netherlands Forensic Institute, P.O. Box 24044, 2490 AA The Hague, The Netherlands; Institute for Biodiversity and Ecosystem Dynamics, University of Amsterdam, Science Park 904, 1098 XH Amsterdam, The Netherlands. Electronic address: a.kloosterman@nfi.minvenj.nl.
authors.forename: Ate
authors.initials: A
authors.lastname: Kloosterman
authors.affiliation: Department of Science, Interdisciplinary Research, Statistics and Knowledge Management (WISK), Netherlands Forensic Institute, P.O. Box 24044, 2490 AA The Hague, The Netherlands; Korteweg-de Vries Institute for Mathematics, University of Amsterdam, Science Park 904, 1098 XH Amsterdam, The Netherlands.
authors.forename: Marjan
authors.initials: M
authors.lastname: Sjerps
authors.affiliation: Department of Human Biological Traces (HBS), Netherlands Forensic Institute, P.O. Box 24044, 2490 AA The Hague, The Netherlands.
authors.forename: Astrid
authors.initials: A
authors.lastname: Quak
mesh_terms: D004247:DNA / Q000235:genetics*; D053803:Forensic Genetics; D006801:Humans; D011786:Quality Control; D017422:Sequence Analysis, DNA / Q000592:standards*
chemical_list: D004247:DNA
keywords: Contamination; Forensic DNA analysis; Lab error rate; Misidentification; Quality control