The Probabilistic Genotyping Software STRmix: Utility and Evidence for its Validity.

Forensic DNA interpretation is transitioning from manual interpretation based usually on binary decision-making toward computer-based systems that model the probability of the profile given different explanations for it, termed probabilistic genotyping (PG). Decision-making by laboratories to implement probability-based interpretation should be based on scientific principles for validity and information that supports its utility, such as criteria to support admissibility. The principles behind STRmix™ are outlined in this study and include standard mathematics and modeling of peak heights and variability in those heights. All PG methods generate a likelihood ratio (LR) and require the formulation of propositions. Principles underpinning formulations of propositions include the identification of reasonably assumed contributors. Substantial data have been produced that support precision, error rate, and reliability of PG, and in particular, STRmix™. A current issue is access to the code and quality processes used while coding. There are substantial data that describe the performance, strengths, and limitations of STRmix™, one of the available PG software.

Biological Sexing of a 4000-Year-Old Egyptian Mummy Head to Assess the Potential of Nuclear DNA Recovery from the Most Damaged and Limited Forensic Specimens.

High throughput sequencing (HTS) has been used for a number of years in the field of paleogenomics to facilitate the recovery of small DNA fragments from ancient specimens. Recently, these techniques have also been applied in forensics, where they have been used for the recovery of mitochondrial DNA sequences from samples where traditional PCR-based assays fail because of the very short length of endogenous DNA molecules. Here, we describe the biological sexing of a ~4000-year-old Egyptian mummy using shotgun sequencing and two established methods of biological sex determination (RX and RY), by way of mitochondrial genome analysis as a means of sequence data authentication. This particular case of historical interest increases the potential utility of HTS techniques for forensic purposes by demonstrating that data from the more discriminatory nuclear genome can be recovered from the most damaged specimens, even in cases where mitochondrial DNA cannot be recovered with current PCR-based forensic technologies. Although additional work remains to be done before nuclear DNA recovered via these methods can be used routinely in operational casework for individual identification purposes, these results indicate substantial promise for the retrieval of probative individually identifying DNA data from the most limited and degraded forensic specimens.

Internal validation of STRmix™ for the interpretation of single source and mixed DNA profiles.

The interpretation of DNA evidence can entail analysis of challenging STR typing results. Genotypes inferred from low quality or quantity specimens, or mixed DNA samples originating from multiple contributors, can result in weak or inconclusive match probabilities when a binary interpretation method and necessary thresholds (such as a stochastic threshold) are employed. Probabilistic genotyping approaches, such as fully continuous methods that incorporate empirically determined biological parameter models, enable usage of more of the profile information and reduce subjectivity in interpretation. As a result, software-based probabilistic analyses tend to produce more consistent and more informative results regarding potential contributors to DNA evidence. Studies to assess and internally validate the probabilistic genotyping software STRmix™ for casework usage at the Federal Bureau of Investigation Laboratory were conducted using lab-specific parameters and more than 300 single-source and mixed contributor profiles. Simulated forensic specimens, including constructed mixtures that included DNA from two to five donors across a broad range of template amounts and contributor proportions, were used to examine the sensitivity and specificity of the system via more than 60,000 tests comparing hundreds of known contributors and non-contributors to the specimens. Conditioned analyses, concurrent interpretation of amplification replicates, and application of an incorrect contributor number were also performed to further investigate software performance and probe the limitations of the system. In addition, the results from manual and probabilistic interpretation of both prepared and evidentiary mixtures were compared. The findings support that STRmix™ is sufficiently robust for implementation in forensic laboratories, offering numerous advantages over historical methods of DNA profile analysis and greater statistical power for the estimation of evidentiary weight, and can be used reliably in human identification testing. With few exceptions, likelihood ratio results reflected intuitively correct estimates of the weight of the genotype possibilities and known contributor genotypes. This comprehensive evaluation provides a model in accordance with SWGDAM recommendations for internal validation of a probabilistic genotyping system for DNA evidence interpretation.

The factor of 10 in forensic DNA match probabilities.

An update was performed of the classic experiments that led to the view that profile probability assignments are usually within a factor of 10 of each other. The data used in this study consist of 15 Identifiler loci collected from a wide range of forensic populations. Following Budowle et al. [1], the terms cognate and non-cognate are used. The cognate database is the database from which the profiles are simulated. The profile probability assignment was usually larger in the cognate database. In 44%-65% of the cases, the profile probability for 15 loci in the non-cognate database was within a factor of 10 of the profile probability in the cognate database. This proportion was between 60% and 80% when the FBI and NIST data were used as the non-cognate databases. A second experiment compared the match probability assignment using a generalised database and recommendation 4.2 from NRC II (the 4.2 assignment) with a proxy for the matching proportion developed using subpopulation allele frequencies and the product rule. The findings support that the 4.2 assignment has a large conservative bias. These results are in agreement with previous research results.

Population data on the expanded CODIS core STR loci for eleven populations of significance for forensic DNA analyses in the United States.

Allele distributions for twenty-three autosomal short tandem repeat (STR) loci - D1S1656, D2S441, D2S1338, D3S1358, D5S818, D7S820, D8S1179, D10S1248, D12S391, D13S317, D16S539, D18S51, D19S433, D21S11, D22S1045, CSF1PO, FGA, Penta D, Penta E, SE33, TH01, TPOX and vWA - were determined in Caucasians, Southwestern Hispanics, Southeastern Hispanics, African Americans, Bahamians, Jamaicans, Trinidadians, Chamorros, Filipinos, Apaches, and Navajos. The data are included in the FBI PopStats software for calculating statistical estimates of DNA typing results and cover the expanded CODIS Core STR Loci required of U.S. laboratories that participate in the National DNA Index System (NDIS).

Evaluation of mRNA marker specificity for the identification of five human body fluids by capillary electrophoresis.

The identification of forensically relevant human body fluids through messenger RNA (mRNA) profiling is of interest to the forensic community. Previous studies have proposed several tissue-specific mRNA markers to achieve this goal. Seven markers for the following genes were selected for evaluation in this study: histatin 3 (HTN3) and statherin (STATH) for saliva, mucin 4 (MUC4) for vaginal secretions, matrix metalloproteinase 7 (MMP7) for menstrual blood, delta-aminolevulinate synthase 2 (ALAS2) for peripheral blood, and protamine 2 (PRM2) and transglutaminase 4 (TGM4) for semen. The expression of these markers was examined in each body fluid. All mRNA markers were present in their target body fluids. Peripheral blood and saliva showed little cross-reactivity with the selected markers. However, a high level of cross-reactivity was observed between the vaginal secretion marker MUC4 and saliva stains. Semen showed a high level of cross-reactivity with the selected markers. Co-expression of the predicted body fluid markers was detected in menstrual blood and vaginal secretion stains. The expression pattern of these mRNA markers varied through the menstrual cycle time points tested. Differences in gene expression levels and marker cross-reactivity were observed in the donors tested. Despite the presence of cross-reactivity and co-expression, each of the body fluids examined have distinct gene expression profiles, allowing for body fluid identification based on mRNA profiling.

Mixture interpretation: defining the relevant features for guidelines for the assessment of mixed DNA profiles in forensic casework.

Currently in the United States there is little direction for what constitutes sufficient guidelines for DNA mixture interpretation. While a standardized approach is not possible or desirable, more definition is necessary to ensure reliable interpretation of results is carried out. In addition, qualified DNA examiners should be able to review reports and understand the assumptions made by the analyst who performed the interpretation. Interpretation of DNA mixture profiles requires consideration of a number of aspects of a mixed profile, many of which need to be established by on-site, internal validation studies conducted by a laboratory's technical staff, prior to performing casework analysis. The relevant features include: criteria for identification of mixed specimens, establishing detection and interpretation threshold values, defining allele peaks, defining nonallele peaks, identifying artifacts, consideration of tri-allelic patterns, estimating the minimum number of contributors, resolving components of a mixture, determining when a portion of the mixed profile can be treated as a single source profile, consideration of potential additive effects of allele sharing, impact of stutter peaks on interpretation in the presence of a minor contributor, comparison with reference specimens, and some issues related to the application of mixture calculation statistics. Equally important is using sensible judgment based on sound and documented principles of DNA analyses. Assumptions should be documented so that reliable descriptive information is conveyed adequately concerning that mixture and what were the bases for the interpretations that were carried out. Examples are provided to guide the community. Interpretation guidelines also should incorporate strategies to minimize potential bias that could occur by making inferences based on a reference sample. The intent of this paper is to promote more thought, provide assistance on many aspects for consideration, and to support that more formalized mixture interpretation guidelines are developed.