NCJ Number
254413
Date Published
November 2019
Length
13 pages
Annotation
This research project introduces massively parallel sequencing (MPS) solutions for the HIrisPlex-S (HPS) system on two MPS platforms commonly used in forensics, Ion Torrent and MiSeq, that cover all 41 DNA variants in a single assay, respectively; and the forensic developmental validation of the two HPS-MPS assays is reported.
Abstract
The Ion Torrent MPS assay, based on Ion AmpliSeq technology, illustrated the successful generation of full HIrisPlex-S genotypic profiles from control DNA, while the MiSeq MPS assay based on an in-house design yielded complete profiles DNA. Assessing simulated forensic casework samples such as saliva, hair (bulb), blood, semen, and low quantity touch DNA, as well as artificially damaged DNA samples, concordance testing, and samples from numerous species, all illustrated the ability of both versions of the HIrisPlex-S MPS assay to produce results that motivate forensic applications. By also providing an integrated bioinformatics analysis pipeline, MPS data can now be analyzed and a file generated for upload to the publicly accessible HIrisPlex online webtool (https://hirisplex.erasmusmc.nl). In addition, researchers updated the website to accept VCF input data for those with genome sequence data. This provides a user-friendly and semi-automated MPS workflow from DNA sample to individual eye, hair, and skin color prediction probabilities. Furthermore, the project developed a two-person mixture separation tool that not only assesses genotype reliability regarding genotyping confidence, but also provides the most fitting mixture scenario for both minor and major contributors, including profile separation. The researchers envision this MPS implementation of the HIrisPlex-S system for eye, hair, and skin color prediction from DNA as a starting point for further expanding MPS-based forensic DNA phenotyping. This may include the future addition of SNPs predictive for more externally visible characteristics, as well as SNPs for bio-geographic ancestry inference, provided the statistical framework for DNA prediction of these traits is in place. (publisher abstract modified)
Date Published: November 1, 2019
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