MaSTR™: an effective probabilistic genotyping tool for interpretation of STR mixtures associated with differentially degraded DNA

A comparison of likelihood ratios calculated from surface DNA mixtures using MPS and CE Technologies

This study evaluates the performance of analysing surface DNA samples using massively parallel sequencing (MPS) compared to traditional capillary electrophoresis (CE). A total of 30 samples were collected from various surfaces in an office environment and were analysed with CE and MPS. These were compared against 60 reference samples (office inhabitants). To identify contributors, likelihood ratios (LRs) were calculated for MPS and CE data using the probabilistic genotyping software MPSproto and EuroForMix respectively. Although a higher number of sequences/peaks were observed per DNA profile in MPS compared to CE, LR values were found to be lower for MPS data formats. This might be the result of the increased complexity of MPS data, along with a possible elevation of unknown alleles and/or artefacts. The study highlights avenues for improving MPS data quality and analysis to facilitate more robust interpretation of challenging casework-like samples.

Advancements in differentiation between sperm cells and epithelial cells for efficient forensic DNA analysis in sexual assault cases

Most of the sexual assault casework samples are of mixed sources. Forensic DNA laboratories are always in the requirement of a precise technique for the efficient separation of sperm and non-sperm DNA from mixed samples. Since the introduction of the differential extraction technique in 1985, it has seen significant advancements in the form of either chemicals used or modification of incubation times. Several automated and semi-automated techniques have also adopted the fundamentals of conventional differential extraction techniques. However, lengthy incubation, several manual steps, and carryover over non-sperm material in sperm fraction are some of the major limitations of this technique. Advanced cell separation techniques have shown huge promise in separating sperm cells from a mixture based on their size, shape, composition, and membrane structure and antigens present on sperm membranes. Such advanced techniques such as DEParray, ADE, FACS, LCM, HOT and their respective pros and cons have been discussed in this article. As current-day forensic techniques should be as per the line of Olympic slogan i.e., faster, higher, stronger, the advanced cell separation techniques show a huge potential to be implemented in the casework samples.

Evidentiary evaluation of single cells renders highly informative forensic comparisons across multifarious admixtures

The consistency between DNA evidence and person(s) of interest (PoI) is summarized by a likelihood ratio (LR): the probability of the data given the PoI contributed divided by the probability given they did not. It is often the case that there are several PoI who may have individually or jointly contributed to the stain. If there is more than one PoI, or the number of contributors (NoC) cannot easily be determined, then several sets of hypotheses are needed, requiring significant resources to complete the interpretation. Recent technological developments in laboratory systems offer a way forward, by enabling production of single cell data. Though single-cell data may be procured by next generation sequencing or capillary electrophoresis workflows, in this work we focus our attention on assessing the consistency between PoIs and a collection of single cell electropherograms (scEPGs) from diploid cells - i.e., leukocytes and epithelial cells. Specifically, we introduce a framework that: I) clusters scEPGs into collections, each originating from one genetic source; II) for each PoI, determines a LR for each cluster of scEPGs; and III) by averaging the likelihood ratios for each PoI across all clusters provides a whole-sample weight of evidence summary. By using Model Based Clustering (MBC) in step I) and an algorithm, named EESCIt for Evidentiary Evaluation of Single Cells, that computes single-cell LRs in step II), we show that 99% of the comparisons rendered log LR values > 0 for true contributors, and of these all but one gave log LR > 5, regardless of the number of donors or whether the smallest contributor donated less than 20% of the cells, greatly expanding the collection of cases for which DNA forensics provides informative results.

Recent advances in forensic biology and forensic DNA typing: INTERPOL review 2019-2022

This review paper covers the forensic-relevant literature in biological sciences from 2019 to 2022 as a part of the 20th INTERPOL International Forensic Science Managers Symposium. Topics reviewed include rapid DNA testing, using law enforcement DNA databases plus investigative genetic genealogy DNA databases along with privacy/ethical issues, forensic biology and body fluid identification, DNA extraction and typing methods, mixture interpretation involving probabilistic genotyping software (PGS), DNA transfer and activity-level evaluations, next-generation sequencing (NGS), DNA phenotyping, lineage markers (Y-chromosome, mitochondrial DNA, X-chromosome), new markers and approaches (microhaplotypes, proteomics, and microbial DNA), kinship analysis and human identification with disaster victim identification (DVI), and non-human DNA testing including wildlife forensics. Available books and review articles are summarized as well as 70 guidance documents to assist in quality control that were published in the past three years by various groups within the United States and around the world.

Internal Validation of MaSTR™ Probabilistic Genotyping Software for the Interpretation of 2–5 Person Mixed DNA Profiles

Mixed human deoxyribonucleic acid (DNA) samples present one of the most challenging pieces of evidence that a forensic analyst can encounter. When multiple contributors, stochastic amplification, and allele drop-out further complicate the mixture profile, interpretation by hand becomes unreliable and statistical analysis problematic. Probabilistic genotyping software has provided a tool to address complex mixture interpretation and provide likelihood ratios for defined sets of propositions. The MaSTR™ software is a fully continuous probabilistic system that considers a wide range of STR profile data to provide likelihood ratios on DNA mixtures. Mixtures with two to five contributors and a range of component ratios and allele peak heights were created to test the validity of MaSTR™ with data similar to real casework. Over 280 different mixed DNA profiles were used to perform more than 2600 analyses using different sets of propositions and numbers of contributors. The results of the analyses demonstrated that MaSTR™ provided accurate and precise statistical data on DNA mixtures with up to five contributors, including minor contributors with stochastic amplification effects. Tests for both Type I and Type II errors were performed. The findings in this study support that MaSTR™ is a robust tool that meets the current standards for probabilistic genotyping.