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qPCR Genotype Determination and Mixture Detection Using High Resolution Melting Curve Analysis of STR Loci

NCJ Number
Date Published
November 2018
25 pages
This report presents the findings and methodology of a project with the objective of designing an assay for DNA mixture detection that could be multiplexed with the quantitation step of the forensic DNA workflow.

The assay developed uses post-qPCR melt-curve analysis to detect the presence of double-stranded amplicon products from the targeted STRs (D5S818 and D18S51). The primary goal was to directly integrate this melt-curve assay into existing commercially available qPCR human DNA quantitation kits so as to accurately assign the sample to either a single-source geno-group or a geno-group that is typical of a mixed sample. The researchers examined two qPCR platforms, the Rotor-Gene Q and the more commonly used ABI 7500, along with several analytical approaches for the resulting melt-curve data classification: 1) use of a commercially available principal component analysis (PCA)-based melt-curve analysis software; 2) development and use of linear discriminate analysis (LDQA) code written for R statistical software; and 3) development and use of a novel support vector machine (SVM) software tool. After assessment of the tested qPCR platforms, selection of the best statistical approach, and integration of the melt-curve assay into a commercially available quantitation kit, the newly developed multiplex would need to be assessed for quantitation precision, geno-grouping concordance, and reproducibility. Overall, this work produced a qPCR-based melt-curve assay for the re-screening identification of mixtures, and the assay has been demonstrated to be viable when integrated into a commercial quantification assay. Implementation of this assay into a forensic DNA laboratory will provide analysts with more information about their evidentiary samples without the need for any additional steps in the workflow; however, there are several considerations that must be addressed prior to crime lab implementation. These considerations are discussed in this report. 13 tables, 6 figures, and 18 references

Date Published: November 1, 2018