Stay Informed:
Baskin Engineering COVID-19 Information and Resources
Campus Roadmap to Recovery
Zoom Links: Zoom Help | Teaching with Zoom | Zoom Quick Guide

Advancement: Case Closed: Developing Reliable Computational Approaches for Identity Analysis from Low-Coverage Sequencing Data

Speaker Name: 
Remy Nguyen
Speaker Title: 
PhD Student
Speaker Organization: 
Biomolecular Engineering & Bioinformatics PhD
Start Time: 
Wednesday, June 16, 2021 - 3:00pm
End Time: 
Wednesday, June 16, 2021 - 4:00pm
Location: 
Zoom - https://ucsc.zoom.us/j/5837681187?pwd=SlJzU3dNM3lKMlc3LzFSbG43OWErdz09 - Passcode: 562924

Abstract: Recent developments in next-generation sequencing technologies have enabled the recovery of low-coverage genetic data from minute, contaminated, and/or heavily degraded biological samples, removing roadblocks to many forensic investigations. Nevertheless, conventional approaches for identity and kinship analysis, often relying on PCR-based determination of a panel of hypervariable short tandem repeat (STR) markers, fail when full genotype calls at the sites used for comparison are impossible to obtain using low-coverage shotgun data. Furthermore, DNA profiling methods that employ single-nucleotide polymorphic (SNP) sites and mitochondrial DNA suffer from the lack of available tools that can quickly and reliably incorporate whole genome low-coverage sequencing information to produce meaningful probability statistics.

To address these challenges, I propose the development of fast and reliable computational approaches for genetic identification using low-coverage sequencing data. First, I aim to implement a pipeline that calculates likelihood ratios for different identity-by-descent models of relatedness using low-coverage sequencing data from an unknown sample and variant information from a reference population. Then, I will apply this tool on DNA data extracted from a panel of hair samples, as well as perform boundary analyses to ensure its applicability in actual forensic scenarios. Finally, I propose to develop methods for the complete reconstruction of mitochondrial haplotypes from forensic sample mixtures. This work will assist investigators in pursuing cold cases with limited genetic evidence and enable kinship studies in poor-quality forensic specimens.

Event Type: 
Advancement/Defense
Advisor: 
Ed Green
Graduate Program: 
Biomolecular Engineering & Bioinformatics PhD