Computational methods for the objective review of forensic DNA testing results

by Gilder, Jason R.

Since the advent of criminal investigations, investigators have sought a "gold standard" for the evaluation of forensic evidence. Currently, deoxyribonucleic acid (DNA) technology is the most reliable method of identification. Short Tandem Repeat (STR) DNA genotyping has the potential for impressive match statistics, but the methodology not infallible. The condition of an evidentiary sample and potential issues with the handling and testing of a sample can lead to significant issues with the interpretation of DNA testing results. Forensic DNA interpretation standards are determined by laboratory validation studies that often involve small sample sizes. This dissertation presents novel methodologies to address several open problems in forensic DNA analysis and demonstrates the improvement of the reported statistics over existent methodologies. Establishing a dynamically calculated RFU threshold specific to each analysis run improves the identification of signal from noise in DNA test data. Objectively identifying data consistent with degraded DNA sample input allows for a better understanding of the nature of an evidentiary sample and affects the potential for identifying allelic dropout (missing data). The interpretation of mixtures of two or more individuals has been problematic and new mathematical frameworks are presented to assist in that interpretation. Assessing the weight of a DNA database match (a cold hit) relies on statistics that assume that all individuals in a database are unrelated – this dissertation explores the statistical consequences of related individuals being present in the database. Finally, this dissertation presents a statistical basis for determining if a DNA database search resulting in a very similar but nonetheless non-matching DNA profile indicates that a close relative of the source of the DNA in the database is likely to be the source of an evidentiary sample.