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.

STRmix™ collaborative exercise on DNA mixture interpretation.

An intra and inter-laboratory study using the probabilistic genotyping (PG) software STRmix™ is reported. Two complex mixtures from the PROVEDIt set, analysed on an Applied Biosystems™ 3500 Series Genetic Analyzer, were selected. 174 participants responded. For Sample 1 (low template, in the order of 200 rfu for major contributors) five participants described the comparison as inconclusive with respect to the POI or excluded him. Where LRs were assigned, the point estimates ranging from 2 × 104 to 8 × 106. For Sample 2 (in the order of 2000 rfu for major contributors), LRs ranged from 2 × 1028 to 2 × 1029. Where LRs were calculated, the differences between participants can be attributed to (from largest to smallest impact): This study demonstrates a high level of repeatability and reproducibility among the participants. For those results that differed from the mode, the differences in LR were almost always minor or conservative.

Mitochondrial DNA analysis of the domestic dog: control region variation within and among breeds.

The mitochondrial DNA (mtDNA) control regions of 125 domestic dogs (Canis familiaris) encompassing 43 breeds, as well as one coyote and two wolves were sequenced and subsequently examined for sequence variation in an effort to construct a reference dog mtDNA data set for forensic analysis. Forty informative variable sites were identified that described 45 haplotypes, 29 of which were observed only once. Substantial variation was found both within and among breeds in the mtDNA derived from tissue, indicating that analysis of the mtDNA derived from dog hairs could be a valuable, discriminating piece of evidence in forensic investigations. The dog data set single nucleotide polymorphisms (SNPs) ranged from having one to six changes on a phylogenetic tree. On average, there were 1.9 character changes for each variable position on the tree. The most variable sites (with four or more changes each, listed from the most changes to the fewest) observed were 15,639 (L=6), 16,672 (L=5), 15,955 (L=4), 15,627 (L=3), 16,431 (L=3), and 16,439 (L=3). These sites were consistent with other reports on variable positions in the dog mtDNA genome. A total of 26 SNPs were chosen to best identify all major clusters in the domestic dog data set. The descriptive analyses revealed that this data set is similar to other published canine data sets and further demonstrates that this domestic dog data set is a useful resource for forensic applications. This reference data set has been compiled and validated against the published dog genetic literature with an aim to aid forensic investigations that seek to incorporate mtDNA sequences and SNPs from trace evidence such as dog hair.

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.

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.

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).