Genome-Wide Forensic DNA Analysis

• NCJ Number: 251934
• Author(s): Lynn B. Jorde
• Date Published: November 2015
• Length: 63 pages
• Grant Number(s): 2012-DN-BX-K037
• Agencies: NIJ-Sponsored
• Publication Type: Research (Applied/Empirical); Report (Study/Research); Report (Grant Sponsored); Program/Project Description
• Award: Genome-Wide Forensic DNA Analysis

Annotation

Findings and methodology are presented for a research project that adapted and improved techniques for amplifying degraded DNA specifically for SNP genotyping, using micro array technology.

Abstract

The project's main objective was to achieve a cost-effective genotyping of SNPs at high density in DNA samples with concentrations and fragmentation levels common in a forensic settings, and to do so at accuracies above and beyond those necessary for ERSA relationship detection. ERSA is a computational method that uses high-density SNP genotypes to link relatives at 6th-degree relationships with high confidence. This method has proven effective on pristine DNA. The main obstacle to the application of ERSA to forensic DNA is that it requires high-density, high-quality SNP genotype data, which cannot currently be generated from many forensic DNA samples. Such samples are often too fragmented, chemically damaged, inhibited, or limited in quantity. The current project identified and quantified the error patterns in the genotyped high-density SNP data from limited and fragmented DNA samples. Error patterns were compared with those observed from genotyping laboratory-grade, high-quality samples. A multivariable logistic regression model was used to optimize true calls from false genotypes. Overall, the project determined that high-density SNP genotyping micro arrays are promising tools for forensic research, with the potential to deliver just over a million genome-wide SNP genotypes per sample from as little as 200 pg DNA, fragmented to 100bp. Such data could achieve many forensically useful inferences, including precise estimates of a DNA donor's bio-geographical ancestry, the ancestries of his/her parents, and accurate inferences of distant relationships. This is possible through the use of an algorithm developed by the researchers. 13 figures, 4 tables, 40 references, and a listing of project publication and presentations