Throughout the Probabilistic Genotyping of Evidentiary DNA Typing Results virtual workshop series, we have reviewed aspects of DNA mixture interpretation and have seen that probabilistic genotyping (PG) software can serve as a tool to assist the DNA Examiner in identifying possible genotype sets within a mixture and then calculating a likelihood ratio (LR). Approaches to modeling were shared by the developers of different PG software programs, followed by examples of internal validation studies and results. Uncertainty and limitations of PG were addressed. Proposition setting, statistical aspects of PG and ways to properly (and improperly) communicate the LR in the laboratory report and testimony were explained. Relevant case decisions on the admissibility of DNA evidence based on probabilistic genotyping were reviewed, and guidance was provided on admissibility-related topics. The hierarchy of propositions and the potential for discussing activity-level propositions were presented, along with a review of information on DNA transfer and persistence.
In this module, lecturers in the series return to address audience questions and delve deeper into special areas of interest related to probabilistic genotyping of forensic DNA evidence.
Lecture topics include: further guidance to prepare the DNA Examiner to address specific admissibility challenges; what should and should not be considered “error” in PG; understanding and discussing the significance of LRs close to one (1); and implications for statistical weight when a suspect is identified through a search of a DNA database such as the National DNA Index System (NDIS). Given the information and experiences shared in this series, a considered perspective and advisement on the role of the DNA Examiner in U.S. courts will be shared by the U.S. Department of Justice Senior Advisor on Forensic Science.
John Buckleton – Institute of Environmental Science and Research, Auckland, New Zealand
Jo-Anne Bright – Institute of Environmental Science and Research, Auckland, New Zealand
Ted Hunt – U.S. Department of Justice, Washington, D.C.
Klaas Slooten – Netherlands Forensic Institute & Vrije University, Amsterdam, The Netherlands
Detailed Learning Objectives:
• Relay information to support the admissibility of probabilistic genotyping evidence
• Articulate the meaning of error in the context of probabilistic genotyping
• Discuss uncertainty and limitations of data with limited support
• Address questions about LRs when a suspect is identified through a database search
• Understand the kinds of information related to statistical weight, as well as activity level propositions, that a DNA expert can provide during testimony in U.S. courts