Nguyen, R. (BMEB) – Development of Computational Methods for Reliable Genetic Identification of Forensic Samples
Advances in sequencing technologies have enabled the recovery of genetic data from minimal, contaminated, and highly degraded samples, overcoming long-standing barriers in forensic analysis. Nevertheless, many evidentiary samples still yield poor-quality DNA that is unconducive to PCR amplification of short tandem repeats (STRs), microarray genotyping, or deep sequencing necessary for accurate, complete genotype calls.
This dissertation addresses these challenges through the development of computational methods for reliable identity analysis of forensic samples. First, I present IBDGem, a fast and robust computational procedure for detecting identity-by-descent (IBD) regions by comparing low-coverage sequence data from an unknown sample against SNP genotype calls from a known individual. Using data from the 1000 Genomes Project and a panel of 8 rootless hairs, I demonstrate that IBDGem can detect relatedness segments at 1x coverage and achieve high-confidence identifications with as little as 0.01x coverage.
The next part of my thesis examines the characteristics of DNA derived from single, rootless hairs and evaluates their potential as a source of forensic genetic information. Analyses of 80 rootless hair samples reveal DNA fragmentation patterns associated with endonuclease-mediated degradation and nucleosome positioning. This chapter also shows that even short segments of rootless hair shafts can yield adequate sequence data to generate statistical support for or against identity.
Finally, I present a comprehensive analysis of IBDGem’s performance across a range of data conditions and program settings. I find that IBDGem is robust to moderate input errors and can identify the major contributor in two-person mixtures. The method also reliably distinguishes self-comparisons from close-relative comparisons, and remains effective even when limited to 94 target SNPs in the ForenSeq assay. Overall, these findings establish IBDGem as a practical tool for analyzing trace DNA evidence when conventional methods are unsuccessful.
Event Host: Remy Nguyen, Ph.D. Candidate, Biomolecular Engineering & Bioinformatics
Advisor: Ed Green
Zoom- https://ucsc.zoom.us/j/91522009894?pwd=JWPSUcIi7IaZ4YOeLDQJohyRApos4T.1
Passcode- 854645
