Eric Schwoebel

Robust detection of individual forensic profiles in DNA mixtures

For a forensic identification method to be admissible in international courts, the probability of false match must be quantified. For comparison of individuals against complex mixtures using a panel of single nucleotide polymorphisms (SNPs), the probability of a random man not excluded, P(RMNE) is one admissible standard. While the P(RMNE) of SNP alleles has been previously studied, it remains to be rigorously defined and calculated for experimentally genotyped mixtures. In this report, exact P(RMNE) values were calculated for a range of complex mixtures, verified with Monte Carlo simulations, and compared alongside experimentally determined detection probabilities.

Sherlock's Toolkit: A forensic DNA analysis system

DNA sequence analysis has multiple forensic applications. The justice system currently uses sizes of STRs as well as mitochondrial DNA (mtDNA) for DNA evidence. With recent advancements in DNA sequencing technologies, inclusion of additional polymorphic loci, including SNPs, enable new useful analyses while maintaining backwards compatibility with STR sizing. Sherlocks Toolkit, developed by MIT Lincoln Laboratory, is an open source, scalable system for the integration and automation of STR and SNP-based analysis for high-throughput sequence data. The toolkit includes modules for a range of kinship, biogeographic ancestry, replicate, and mixture analyses.

KinLinks: Software Toolkit for kinship analysis and pedigree generation from HTS datasets

The ability to predict familial relationships from source DNA in multiple samples has a number of forensic and medical applications. Kinship testing of suspect DNA profiles against relatives in a law enforcement database can provide valuable investigative leads, determination of familial relationships can inform immigration decisions, and remains identification can provide closure to families of missing individuals. The proliferation of High-Throughput Sequencing technologies allows for enhanced capabilities to accurately predict familial relationships to the third degree and beyond. KinLinks, developed by MIT Lincoln Laboratory, is a software tool that predicts pairwise relationships and reconstructs kinship pedigrees for multiple input samples using single-nucleotide polymorphism (SNP) profiles. The software has been trained and evaluated on a set of 175 subjects (30,450 pairwise relationships), consisting of three multi-generational families and 52 geographically diverse subjects. Though a panel of 5396 SNPs was selected for kinship prediction, KinLinks is highly modular, allowing for the substitution of expanded SNP panels and additional training models as sequencing capabilities continue to progress. KinLinks builds on the SNP-calling capabilities of Sherlocks Toolkit, and is fully integrated with the Sherlocks Toolkit pipeline.

Human CODIS STR loci profiling from HTS data

Human DNA identification is currently performed by amplifying a small, defined set of short tandem repeat (STR) loci (e.g. CODIS) and analyzing the size of the alleles present at those loci by capillary electrophoresis. High-throughput DNA sequencing (HTS) could enable the simultaneous analysis of many additional STR and single nucleotide polymorphism (SNP) loci, improving accuracy and discrimination. However, it is necessary to demonstrate that HTS can generate accurate data on the CODIS loci to enable backwards compatibility with the FBI NDIS database. Sequencing can also detect novel polymorphisms within alleles that migrate with identical sizes by capillary electrophoresis, improving allele discrimination, and enhancing human identification analysis. All CODIS alleles from an individual can be amplified in a single, multiplex PCR reaction, and combined with additional barcoded samples prior to sequencing. A computational tool for allele identification from multiplexed sequence data has been developed. With longer-read-length platforms, 99.6% allele calling accuracy can be achieved. In the course of STR sequencing protocol development, 12 novel allele sequences have been identified for multiple loci. Sequencing STR loci combined with SNPs will enable new forensic applications.

Estimating Individual Contributions to Complex DNA SNP Mixtures

Mixture analysis and deconvolution methods can identify both known and unknown individuals contributing to DNA mixtures. These methods may not identify all DNA contributors with the remaining fraction of the mixture being contributed by one or more unknown individuals. The proportion of DNA contributed by individuals to a forensic sample can be estimated using their quantified mixture alleles. For short tandem repeats (STRs), methods to estimate individual contribution concentrations compare capillary electrophoresis peak heights and or peak areas within a mixture. For single nucleotide polymorphisms (SNPs), the major:minor allele ratios or counts, unique to each contributor, can be compared to estimate contributor proportion within the mixture. This article introduces three approaches (mean, median, and slope methods) for estimating individual DNA contributions to forensic mixtures for SNP panels and high throughput sequencing (HTS)/massively parallel sequencing (MPS) technology.

TranslucentID: Detecting Individuals with High Confidence in Saturated DNA SNP Mixtures

High throughput sequencing (HTS) of complex DNA mixtures with single nucleotide polymorphisms (SNPs) panels can identify multiple individuals in forensic DNA mixture samples. SNP mixture analysis relies upon the exclusion of non-contributing individuals with the subset of SNP loci with no detected minor alleles in the mixture. Few, if any, individuals are anticipated to be detectable in saturated mixtures by this mixture analysis approach because of the increased probability of matching random individuals. Being able to identify a subset of the contributors in saturated HTS SNP mixtures is valuable for forensic investigations. A desaturated mixture can be created by treating a set of SNPs with the lowest minor allele ratios as having no minor alleles. Leveraging differences in DNA contributor concentrations in saturated mixtures, we introduce TranslucentID for the identification of a subset of individuals with high confidence who contributed DNA to saturated mixtures by desaturating the mixtures.

The Plateau Method for Forensic DNA SNP Mixture Deconvolution

Identification of individuals in complex DNA mixtures remains a challenge for forensic analysts. Recent advances in high throughput sequencing (HTS) are enabling analysis of DNA mixtures with expanded panels of Short Tandem Repeats (STRs) and/or Single Nucleotide Polymorphisms (SNPs). We present the plateau method for direct SNP DNA mixture deconvolution into sub-profiles based on differences in contributors’ DNA concentrations in the mixtures in the absence of matching reference profiles. The Plateau method can detect profiles of individuals whose contribution is as low as 1/200 in a DNA mixture (patent pending)1.