In discussing the estimation of population structure parameter, this report indicates that a DNA match probability is the probability that an untyped person has a DNA profile, given that a typed person has the profile. This depends on the genetic structure of the population to which these two people belong. The problems with structured populations arises when the people of interest belong, or are assumed to belong, to the same subpopulation, but data are available from only the whole population. The number and nature of subpopulations is generally unknown. The discussion of the estimation of population structure parameter is followed by a section on Y-STR match probabilities, since there is growing interest in the use of Y-STR profiles for forensic purposes. How relevant issues have been addressed in this project are described. This is followed by a description of a continuous model for mixtures. This discussion notes that over the past 3 years, the researchers in the current project have made contributions to the literature on providing numerical characterization of the evidentiary strength of DNA evidence. This work assumes the applicability of likelihood ratios, and it has been designed to avoid problems with the "binary model," in which decision rules on allelic presence in a profile are based on detection or analysis thresholds. This work is described in three stages. The concluding section of this report describes outreach activities from this project. It is noted that the work on population structure and Y-STR matching had a significant role in the "SWGDAM Interpretation Guidelines for Y-Chromosome STR Typing by Forensic DNA Laboratories." 4 figures, 1 table, and 25 references
Downloads
Similar Publications
- CtrSVDD: A Benchmark Dataset and Baseline Analysis for Controlled Singing Voice Deepfake Detection
- Improved Nucleic Acid Recovery From Trace and Degraded Samples Using Affinity Purification
- Success Story: NIJ and The Virginia Department of Forensic Science Advancing Drug Analysis in Forensic Toxicology for Enhanced Judicial Outcomes