This is the Final Summary Overview of a project whose overall purpose was to develop better sets of SNP markers for forensics, using the research team’s genomic analysis expertise and unique population resources to document the validity of these panels for their specific purposes.
The two kinds of studies pursued are 1) those involving panels of single SNPs and 2) those involving microhaplotypes of multiple SNPs. Through the current study and previous studies by the research team funded by the National Institute of Justice (NIJ), the team has collected data on large numbers of SNPs on an extensive number of populations that involve thousands of individuals. This forensic research has been oriented toward both identifying SNPs and haplotypes that are especially useful for several types of SNP panels, i.e., individual identification SNPs (IISNPs), ancestry informative SNPs (AISNPs), and microhaplotypes informative for identifying the aforementioned SNPs plus mixtures. The research team also has a small component of highly informative very small (<75 bp) microhaplotypes that are useful on degraded DNA and on exogenous cell-free DNA in blood. This report indicates that the achievements of the research team are best measured by the papers published during this project period and the growing list of citations of papers published under previous NIJ awards. These publications are listed in this report. New reference populations plus SNP and microhaplotype frequencies were entered into ALFRED and FROG-kb during early 2019. A database of several thousand SNPs on all 2,500 individuals represented by cell-line DNA has been assembled; over 1,000 of those SNPs have been added by the current project.
Downloads
Similar Publications
- Evaluation of Cannabis Product Mislabeling: The Development of a Unified Cannabinoid LC-MS/MS Method to Analyze E-liquids and Edible Products
- Electroanalytical Paper-based Sensors for Infield Detection of Chlorate-based Explosives and Quantification of Oxyanions
- Quantitative Matching of Forensic Evidence Fragments Using Fracture Surface Topography and Statistical Learning