Effects and considerations of multiplexing ForenSeq Kintelligence libraries with a negative control.

The negative template control or negative amplification control has been an essential component of the forensic DNA analysis workflow that helps monitor contamination. As such, the inclusion of a negative control in forensic DNA analysis has been a requirement for all laboratories audited under the FBI's Quality Assurance Standards. As massively parallel sequencing (MPS) becomes more conventional in forensic laboratories, considerations for the inclusion of a negative control in every sequencing run can be evaluated. Although the inclusion of a negative control in library preparation and the first sequencing run has a practical function, there is less utility for its inclusion in all subsequent sequencing runs for that library preparation. Although this is universal to all MPS assays, it is most relevant for an assay that has a low sample multiplexing capacity, such as the ForenSeq Kintelligence Kit (Qiagen/Verogen, Inc.). The ForenSeq Kintelligence Kit is an investigative genetic genealogy (IGG) sequencing-based assay that targets 10,230 forensically relevant single-nucleotide polymorphisms. The manufacturer recommends multiplexing 3 libraries per sequencing run, which includes controls. The purpose of this study was to investigate the effect of the inclusion of a negative control in every Kintelligence sequencing run. We observed that the library generated from a negative amplification control will take 7%-14% of the run output. The loss of sequencing space taken by a negative control decreased the available output for DNA-containing samples, leading in some cases to allele or locus dropout and accompanying higher numbers of sixth to seventh order unknown associations in GEDmatch PRO.

Compound and Conditioned Likelihood Ratio Behavior within a Probabilistic Genotyping Context.

In cases where multiple questioned individuals are separately supported as contributors to a mixed DNA profile, guidance documents recommend performing a comparison to see if there is support for their joint contribution. Anecdotal observations suggest the summed log of the individual likelihood ratios (LR), termed the simple LR product, should be roughly equivalent to or less than the log(LR) for the joint likelihood ratio, termed the compound LR. To assist casework analysts in evaluating statistical weights applied to a case at hand, this study assessed how consistently compound LRs conform to an additive behavior when compared to the simple LR product counterparts. Two-, three-, and four-person DNA mixture data, of various mixture proportions and DNA inputs, were interpreted by STRmix® version 2.8 Probabilistic Genotyping Software. Relative magnitudes of LR increases were found to be dependent on both template level and mixture composition. The distribution of log(LR) differences between all compound/simple LR comparisons was ~-2.7 to ~28.3. This level of information gain was similar to that for compound LR comparisons, with and without interpretation conditioning (~-3.2 to ~27.7). In both scenarios, the probability density peaked at approximately 0.5, indicating the information gain from constrained genotype combinations has a comparable impact on the outcome of LR calculations whether the restriction is applied before or after interpretation.

Improving the Utilization of STRmix™ Variance Parameters as Semi-Quantitative Profile Modeling Metrics.

Distributions of the variance parameter values developed during the validation process. Comparisons of these prior distributions to the run-specific average are one measure used by analysts to assess the reliability of a STRmix deconvolution. This study examined the behavior of three different STRmix variance parameters under standard amplification and interpretation conditions, as well as under a variety of challenging conditions, with the goal of making comparisons to the prior distributions more practical and meaningful. Using information found in STRmix v2.8 Interpretation Reports, we plotted the log10 of each variance parameter against the log10 of the template amount of the highest-level contributor (Tc) for a large set of mixture data amplified under standard conditions. We observed nonlinear trends in these plots, which we regressed to fourth-order polynomials, and used the regression data to establish typical ranges for the variance parameters over the Tc range. We then compared the typical variance parameter ranges to log10(variance parameter) v log10(Tc) plots for mixtures amplified and interpreted under a variety of challenging conditions. We observed several distinct patterns to variance parameter shifts in the challenged data interpretations in comparison to the unchallenged data interpretations, as well as distinct shifts in the unchallenged variance parameters away from their prior gamma distribution modes over specific ranges of Tc. These findings suggest that employing empirically determined working ranges for variance parameters may be an improved means of detecting whether aberrations in the interpretation were meaningful enough to trigger greater scrutiny of the electropherogram and genotype interpretation.

Modeling allelic analyte signals for aSTRs in NGS DNA profiles.

We describe an adaption of Bright et al.'s work modeling peak height variability in CE-DNA profiles to the modeling of allelic aSTR (autosomal short tandem repeats) read counts from NGS-DNA profiles, specifically for profiles generated from the ForenSeq™ DNA Signature Prep Kit, DNA Primer Mix B. Bright et al.'s model consists of three key components within the estimation of total allelic product-template, locus-specific amplification efficiencies, and degradation. In this work, we investigated the two mass parameters-template and locus-specific amplification efficiencies-and used MLE (maximum likelihood estimation) and MCMC (Markov chain Monte Carlo) methods to obtain point estimates to calculate the total allelic product. The expected read counts for alleles were then calculated after proportioning some of the expected stutter product from the total allelic product. Due to preferential amplicon selection introduced by the sample purification beads, degradation is difficult to model from the aSTR outputs alone. Improved modeling of the locus-specific amplification efficiencies may mask the effects of degradation. Whilst this model could be improved by introducing locus specific variances in addition to locus specific priors, our results demonstrate the suitability of adapting Bright et al.'s allele peak height model for NGS-DNA profiles. This model could be incorporated into continuous probabilistic interpretation approaches for mixed DNA profiles.

Mitochondrial Sequencing of Missing Persons DNA Casework by Implementing Thermo Fisher's Precision ID mtDNA Whole Genome Assay.

The advent of massively parallel sequencing (MPS) in the past decade has opened the doors to mitochondrial whole-genome sequencing. Mitochondrial (mt) DNA is used in forensics due to its high copy number per cell and maternal mode of inheritance. Consequently, we have implemented the Thermo Fisher Precision ID mtDNA Whole Genome panel coupled with the Ion Chef™ and Ion S5™ for MPS analysis in the California Department of Justice, Missing Persons DNA Program. Thirty-one mostly challenging samples (degraded, inhibited, low template, or mixed) were evaluated for this study. The majority of these samples generated single source full or partial genome sequences with MPS, providing information in cases where previously there was none. The quantitative and sensitive nature of MPS analysis was beneficial, but also led to detection of low-level contaminants. In addition, we found Precision ID to be more susceptible to inhibition than our legacy Sanger assay. Overall, the success rate (full single source hypervariable regions I and II (HVI/HVII) for Sanger and control region for MPS result) for these challenging samples increased from 32.3% with Sanger sequencing to 74.2% with the Precision ID assay. Considering the increase in success rate, the simple workflow and the higher discriminating potential of whole genome data, the Precision ID platform is a significant improvement for the CA Department of Justice Missing Persons DNA Program.