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Production of high-fidelity electropherograms results in improved and consistent DNA interpretation: Standardizing the forensic validation process

NCJ Number
Forensic Science International: Genetics Volume: 31 Dated: 2017 Pages: 160-170
Kelsey C. Peters; et al
Date Published
11 pages

This project sought to develop a validation scheme to aid in mitigating the effects of evidence samples in which the loss of genetic information on contributors is associated with sampling and detection effects.



Researchers established a scheme designed to simultaneously improve signal resolution and detection rates without costly large-scale experimental validation studies by applying a combined simulation and experimental approach. Specifically, the project parameterized an in-silico DNA pipeline with experimental data acquired from the laboratory and used this to evaluate multifarious scenarios in a cost-effective manner. Metrics, such as signal1copy-to-noise resolution, false positive, and false negative signal detection rates were used to select tenable laboratory parameters that resulted in high-fidelity signal in the single-copy regime. The project demonstrated that the metrics acquired from simulation were consistent with experimental data obtained from two capillary electrophoresis platforms and various injection parameters. Once good resolution is obtained, analytical thresholds can be determined using detection error tradeoff analysis, if necessary. Decreasing the limit of detection of the forensic process to one copy of DNA is a powerful mechanism by which to increase the information content on minor components of a mixture, which is particularly important for probabilistic system inference. If the forensic pipeline is engineered such that high-fidelity electropherogram signal is obtained, then the likelihood ratio (LR) of a true contributor increases and the probability that the LR of a randomly chosen person is greater than one decreases. This is, potentially, the first step toward standardization of the analytical pipeline across operational laboratories. (publisher abstract modified)

Date Published: January 1, 2017