An assessment of the information content of likelihood ratios derived from complex mixtures

Decreased accuracy of forensic DNA mixture analysis for groups with lower genetic diversity

Forensic investigation of DNA samples from multiple contributors has become commonplace. These complex analyses use statistical frameworks accounting for multiple levels of uncertainty in allelic contributions from different individuals, particularly for samples containing few molecules of DNA. These methods have been thoroughly tested along some axes of variation, but less attention has been paid to accuracy across human genetic variation. Here, we quantify the accuracy of DNA mixture analysis over 244 human groups. We find higher false inclusion rates for mixtures with more contributors, and for groups with lower genetic diversity. Even for two-contributor mixtures where one contributor is known and the reference group is correctly specified, false inclusion rates are 1e-5 or higher for 56 out of 244 groups. This means that, depending on multiple testing, some false inclusions may be expected. These false positives could be lessened with more selective and conservative use of DNA mixture analysis.

Estimating individual mtDNA haplotypes in mixed DNA samples by combining MinION and MiSeq

We tried to estimate individual mtDNA haplotypes in mixed DNA samples by combining MinION and MiSeq. The BAM files produced by MiSeq were viewed using Integrative Genomics Viewer (IGV) to verify mixed bases. By sorting the reads according to base type for each mixed base, partial haplotypes were determined. Then, the BAM files produced by MinKNOW were viewed using IGV. To determine haplotypes with IGV, only mixed bases determined by MiSeq were used as target bases. By sorting the reads according to base type for each target base, each contributor's haplotype was estimated. In mixed samples from two contributors, even a haplotype with a minor contribution of 5% could be distinguished from the haplotype of the major contributor. In mixed samples of three contributors (mixture ratios of 1:1:1 and 4:2:1), each haplotype could also be distinguished. Sequences of C-stretches were determined very inaccurately in the MinION analysis. Although the analysis method was simple, each haplotype was correctly detected in all mixed samples with two or three contributors in various mixture ratios by combining MinION and MiSeq. This should be useful for identifying contributors to mixed samples.

Estimation of the number of contributors to mixed samples of DNA by mitochondrial DNA analyses using massively parallel sequencing

We evaluated whether the number of contributors to mixed DNA samples can be estimated by analyzing the D-loop of mitochondrial DNA using massively parallel sequencing. The A- (positions 16,209-16,400) and B- (positions 30-284) amplicons in hypervariable regions 1 and 2, respectively, were sequenced using MiSeq with 2 × 251 cycles. Sequence extraction and trimming were performed using CLC Genomics Workbench 11 and the number of observed haplotypes was counted for each amplicon type using Microsoft Excel. The haplotype ratios were calculated by dividing the number of counted reads of the corresponding haplotype by the total number of sequence reads. Haplotypes that were over the threshold (5%) were defined as positive haplotypes. The number of larger positive haplotypes in either of the two amplicon types was defined as the number of contributors. Samples were collected from seven individuals. Seventeen mixed samples were prepared by mixing DNA from two to five contributors at various ratios. The number of contributors was correctly estimated from almost all of the mixed samples containing equal amounts of DNA from two to five people. In mixed samples of two or three people, the minor components were detected down to a ratio of 20:1 or 8:2:1. However, heteroplasmy, base deletions, and sharing of the same haplotypes caused incorrect estimations of the number of contributors. Although this method still has room for improvement, it may be useful for estimating the number of contributors in a mixed sample, as it does not rely on forensic mathematics.

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.

Separation/extraction, detection, and interpretation of DNA mixtures in forensic science (review)

Interpreting mixed DNA samples containing material from multiple contributors has long been considered a major challenge in forensic casework, especially when encountering low-template DNA (LT-DNA) or high-order mixtures that may involve missing alleles (dropout) and unrelated alleles (drop-in), among others. In the last decades, extraordinary progress has been made in the analysis of mixed DNA samples, which has led to increasing attention to this research field. The advent of new methods for the separation and extraction of DNA from mixtures, novel or jointly applied genetic markers for detection and reliable interpretation approaches for estimating the weight of evidence, as well as the powerful massively parallel sequencing (MPS) technology, has greatly extended the range of mixed samples that can be correctly analyzed. Here, we summarized the investigative approaches and progress in the field of forensic DNA mixture analysis, hoping to provide some assistance to forensic practitioners and to promote further development involving this issue.