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                            Funding & Awards
 Towards Commercialization: Preliminary developmental validation of a high resolution melt curve mixture prediction assay and SVM tool
Award Information
Awardee
              VIRGINIA COMMONWEALTH UNIVERSITY
Award #
              2019-DU-BX-0003
Funding Category
              Competitive Discretionary
Location
                    RICHMOND, VA
Congressional District
              4
Status
              Past Project Period End Date
Funding First Awarded
              2019
Total funding (to date)
              $399,699
Original Solicitation
              Research and Evaluation for the Testing and Interpretation of Physical Evidence in Publicly Funded Forensic Laboratories, FY 2019
Description of original award (Fiscal Year 2019, $399,699)
Deconvolution of mixed genetic profiles can be challenging for even the most experienced forensic DNA examiners, particularly when only a few cells are present (such as with touch DNA samples). These low-level DNA samples often have high failure rates - showing data below established thresholds or showing complex mixtures, both of which present extreme interpretational challenges. Unfortunately, in the current workflow of forensic laboratories, both allele designation and mixture detection occur at the final step of the analytical process (data review). The late revelation of these issues proves problematic, as initial testing of touch DNA evidence samples often include consumption of the DNA. If screening tools could be developed to provide information about the number of contributors in the DNA sample earlier on in the DNA workflow, examiners could easily adjust amplification conditions or combine multiple swabs from the same item in an effort to improve the chances of generating a clear profile from the first round of testing, reducing the retest rate associated with the processing of touch DNA samples. A previous 2015 NIJ award paved the way for significant progress towards this goal.
From this, a melt curve assay for mixture identification that was multiplexed within the quantitation step of the forensic DNA workflow was developed. Initial testing of single source and 2-person 1 :1 mixed samples using the Rotor-Gene Q qPCR platform revealed that this quantitation-HRM integrated assay was able to accurately distinguish between single-source and mixture samples 94 percent or 100 percent of the time when selected key features or when entire melt curve data sets were used respectively. While this work has successfully produced a viable qPCR-based melt curve assay for prescreening identification of mixtures, there are several considerations that must be addressed prior to crime lab testing and implementation. In order to minimize risks under this new approach, additional key developmental validation studies must be completed, including an evaluation of the integrated assay on the ABI 7500 platform, a comprehensive assessment of reproducibility, and an assessment of assay performance using mixtures that contain more than 2 persons and across a spectrum of mixture ratios. Most importantly, the assay needs a comprehensive assessment by public lab forensic examiners using DNA from non-probative compromised forensic evidence samples. Further, a web-based tool is needed to facilitate broader access of the melt curve standards database and to provide an easy-to-use online tool for quick assessment of melt curve data.
Note: This project contains a research and/or development component, as defined in applicable law, and complies with Part 200 Uniform Requirements - 2 CFR 200.210(a)(14).
CA/NCF



  
Grant-Funded Publications
        
                      Towards Commercialization: Preliminary Developmental Validation of a High Resolution Melt Curve Mixture Prediction Assay and SVM Tool
                  A quantifiler(TM) trio-based HRM screening assay for the accurate prediction of single source versus mixed biological samples
                  A quantifilerTM trio-based HRM screening assay for the accurate prediction of single source versus mixed biological samples

  
Date Created: September 13, 2019
    
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