Today’s guest blog is written by Dr. Jo-Anne Bright, New Zealand Institute of Environmental Science and Research (ESR). Reposted from the ISHI Report with permission.
The field of forensic DNA analysis has long been transformative. Over the past 50 years, though, two step changes in the forensic examination of biological traces have been particularly impactful: the advent of DNA typing in 1989; and the advent of probabilistic genotyping (PG).
Since its introduction, forensic DNA typing methods have evolved rapidly. Technological improvements have massively increased the sensitivity of the technique, resulting in the use of increasingly complex DNA profiles. In addition, the likelihood ratio (LR) has now become the accepted method of assigning the weight of evidence for complex and mixed DNA profiles.
Implementation of the LR requires numerical assignment of a probability of a set of peak heights, given a proposed set of donor genotypes. This involves creating mathematical models that account for the effects of DNA template and degradation, artefact creation, and the variability of these. These models are applied using Markov Chain Monte Carlo methods, leading to the creation of the field now known as probabilistic genotyping.
The success of PG software in interpreting complex autosomal short tandem repeat (STR) profiles, in turn, has given rise to its widespread use in casework. STRmix™ forensic software, for example, has generated usable and legally admissible DNA evidence in more than half a million criminal cases globally since its introduction in 2012.
A more recent innovation for the forensic analysis of DNA evidence is Next Generation Sequencing (NGS), a massively parallel sequencing technology used to determine the order of nucleotides in entire genomes or targeted regions of DNA or RNA. A number of experts in the field contend that NGS (also known as Massively Parallel Sequencing or MPS) technology has the potential to assist forensic laboratories in cases involving human identification, kinship, and ancestral origin by offering ultra-high throughput, scalability, and speed. Reported benefits include faster resolution of criminal cases and reduced costs.
Should those benefits come to fruition, NGS technology not only could revolutionize traditional forensic DNA analysis but also could synergize with emerging techniques like Forensic Investigative Genetic Genealogy (FIGG). FIGG can analyze vast datasets of single nucleotide polymorphism (SNP) loci to harness genealogical research and advanced DNA testing. As a result, it can identify potential familial links to unknown profiles, offering invaluable insights into cold cases and unidentified remains. Some forensic laboratories are already using SNP loci routinely to analyze small, degraded samples, such as bone samples.
The researchers here at STRmix, increasingly have been considering the possibility of integrating our PG and LR assignment technology with NGS technologies. These tools now incorporate a growing NGS analysis capability, including within the investigative functions of DBLR™, our tool for rapidly calculating likelihood ratios in DNA evidence. DBLR™ can accept NGS sequence-based inputs and assign LRs within the Kinship module for single source profiles containing both STR and SNP loci generated using NGS technology.
The STRmix team also recently launched STRmix™ NGS, the first application of STRmix™ technology expressly for the probabilistic genotyping of autosomal NGS profiles. Employing a fully continuous approach for interpretation of NGS-generated STR DNA profiles, STRmix™ NGS allows users to research the potential implementation and validation path of probabilistic genotyping alongside NGS chemistries and sequencing equipment.
While not currently available for casework, this Research and Validation release of STRmix™ NGS has been designed for laboratories familiar with STRmix™ that are investigating future implementation of emergent NGS technology. Pairing NGS with STRmix™ potentially could unlock the value of complex mixtures analyzed using more loci and more sensitive technologies.
The STRmix™ NGS workflow and user experience is grounded on the capillary electrophoresis- based STRmix™ technology, which has been extensively validated and is used for casework interpretation in more than 100 forensic labs worldwide. STRmix™ NGS has many features in common with standard STRmix™ technology, including the ability to model any type of stutter, undertake quality checks of the data, assign LRs with flexible propositions, and generate reports including run diagnostics in a configurable reporting module. Further functionality development and improvement is planned as a result of our very active scientific research program.
Looking to the future, the STRmix team expects NGS technology and its use with PG software to continue to be evaluated and refined by members of the forensic community. While proponents contend this technology can provide a depth of information beyond the capacity of traditional forensic DNA technologies interpreting solely autosomal STRs, we must still grapple with how it will be used in combination with legacy databases.
Globally, these databases contain tens of millions of STR profiles from known individuals. CODIS alone contains more than 20 million offender and arrestee profiles. As a result, autosomal STR loci seem likely to be the workhorse of forensic laboratories, at least for the immediate future.
Despite this legacy data, a logical next step for the STRmix team with the expansion of its PG software solution is the ability to interpret SNP data. By multiplexing thousands of SNP loci, increased discrimination power can be achieved. Given the small sample sizes required, SNPs on NGS platforms will be more sensitive and discriminatory than STRs analyzed on CE technology. This will enable them to produce additional information, such as identity-specific ancestry and phenotypic data.
The anticipated opportunities from use of NGS technology lie in its potential to add significant value to the entire field, promising a third step change for forensic analysis of biological material.
