- •In a collaborative exercise we tested dried body fluid stains with an Illumina 33plex mRNA panel for body fluid/tissue identification and a 35plex cSNP panel for assignment of body fluids/tissues to donors.
- •We predicted the origin of the stains using a PLS-DA model, where most of the single source samples were correctly predicted.
- •We were able to associate donors with body fluids in mixtures of different body fluids as well as in stains where both donors have contributed the same body fluid.
- •cSNP genotype calling can be difficult in RNA due to pseudo-homozygous phenomena.
In a previous EUROFORGEN/EDNAP collaborative exercise, we tested two assays for targeted mRNA massively parallel sequencing for the identification of body fluids/tissues, optimized for the Illumina MiSeq/FGx and the Ion Torrent PGM/S5 platforms, respectively. The task of the second EUROFORGEN/EDNAP collaborative exercise was to analyze dried body fluid stains with two different multiplexes, the former Illumina 33plex mRNA panel for body fluid/tissue identification and a 35plex cSNP panel for assignment of body fluids/tissues to donors that was introduced in a proof-of-concept study recently. The coding region SNPs (cSNPs) are located within the body fluid specific mRNA transcripts and represent a direct link between the body fluid and the donor.
We predicted the origin of the stains using a partial least squares discriminant analysis (PLS-DA) model, where most of the single source samples were correctly predicted. The mixed body fluid stains showed poorer results, however, at least one component was predicted correctly in most stains. The cSNP data demonstrated that coding region SNPs can give valuable information on linking body fluids/tissues with donors in mixed body fluid stains. However, due to the unfavorable performance of some cSNPs, the interpretation remains challenging. As a consequence, additional markers are needed to increase the discrimination power in each body fluid/tissue category.
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Published online: November 26, 2019
Accepted: November 21, 2019
Received in revised form: November 19, 2019
Received: September 19, 2019
© 2019 Elsevier B.V. All rights reserved.