Despite the improvements in forensic DNA quantification methods that allow for the early detection of low template/challenged DNA samples, complicating stochastic effects are not revealed until the final stage of the DNA analysis workflow. An assay that would provide genotyping information at the earlier stage of quantification would allow examiners to make critical adjustments prior to STR amplification allowing for potentially exclusionary information to be immediately reported. Specifically, qPCR instruments often have dissociation curve and/or high-resolution melt curve (HRM) capabilities; this, coupled with statistical prediction analysis, could provide additional information regarding STR genotypes present. Thus, this study aimed to evaluate Qiagen’s principal component analysis (PCA)-based ScreenClust^® HRM^® software and a linear discriminant analysis (LDA)-based technique for their abilities to accurately predict genotypes and similar groups of genotypes from HRM data. Melt curves from single source samples were generated from STR D5S818 and D18S51 amplicons using a Rotor-Gene^® Q qPCR instrument and EvaGreen^® intercalating dye. When used to predict D5S818 genotypes for unknown samples, LDA analysis outperformed the PCA-based method whether predictions were for individual genotypes (58.92% accuracy) or for geno-groups (81.00% accuracy). However, when a locus with increased heterogeneity was tested (D18S51), PCA-based prediction accuracy rates improved to rates similar to those obtained using LDA (45.10% and 63.46%, respectively). This study provides foundational data documenting the performance of prediction modeling for STR genotyping based on qPCR-HRM data. In order to expand the forensic applicability of this HRM assay, the method could be tested with a more commonly utilized qPCR platform.

Although guidelines exist for identifying mixtures, these measures often occur at the end-point of analysis and are protracted. To facilitate early mixture detection, we integrated a high-resolution melt (HRM) mixture screening assay into the qPCR step of the forensic workflow, producing the integrated Quantifiler^TM Trio-HRM assay. The assay, when coupled with a prediction tool, allowed for 75.0% accurate identification of the contributor status of a sample (single source vs. mixture). To elucidate the limitations of the developed qPCR-HRM assay, developmental validation studies were conducted assessing the reproducibility and samples with varying DNA ratios, contributors, and quality. From this work, it was determined that the integrated Quantifiler^TM Trio-HRM assay is capable of accurately identifying mixtures with up to five contributors and mixtures at ratios up to 1:100. Further, the optimal performance concentration range was found to be between 0.025 and 0.5 ng/µL. With these results, evidentiary-like DNA samples were then analyzed, resulting in 100.0% of the mixture samples being accurately identified; furthermore, every time a sample was predicted as a single source, it was true, giving confidence to any single-source calls. Overall, the integrated Quantifiler^TM Trio-HRM assay has exhibited an enhanced ability to discern mixture samples from single-source samples at the qPCR stage under commonly observed conditions regardless of the contributor’s sex.

In recent years, DNA analysis techniques have drastically increased in sensitivity, allowing for low template DNA samples to be more easily detected and used for identification. Since the problems inherent in low template DNA are exacerbated in DNA mixture samples, it would be advantageous to incorporate an assay earlier in the DNA workflow that could detect a mixture and, potentially, determine the number of contributors. Some real-time PCR instruments have high-resolution melt curve analysis (HRM) capabilities, allowing for an opportunity to integrate an HRM screening assay into a commercial DNA quantification kit. This work describes the integration of a mixture screening HRM assay using STR loci D5S818 and D18S51 into Qiagen’s Investigator Quantiplex^® kit. The integrated Quantiplex^®-HRM assay was tested on two qPCR platforms: The Rotor-Gene^® Q and the QuantStudio™ 6 Flex. Data from this assay was analyzed using linear discriminant and support vector machine analyses for sample classification. When HRM curve data from the Rotor-Gene^® Q was used for classification, the integrated assay exhibited an overall accuracy of 89.39%, correctly classifying 87.5% of single source samples and 100% of mixtures. When HRM curve data from the QuantStudio™ 6 Flex was used for classification, the integrated assay exhibited an overall accuracy of 87.88%, correctly classifying 87.5% of single source samples and 90% of mixtures. The overall accuracy of the integrated Quantiplex^®-HRM assay on both instruments met our goal of ≥ 80% accuracy, demonstrating the viability of the assay to detect mixtures when integrated into a commercial quantification kit.