Targeted S5 RNA sequencing assay for the identification and direct association of common body fluids with DNA donors in mixtures.

The evidentiary value of DNA profiles varies depending upon the context in which the DNA was found. Linking a DNA profile to a particular cellular phenotype in mixtures may aid in assessing its evidentiary relevance and value. We report the development of two dual-function high-resolution messenger RNA (mRNA) sequencing assays that can each identify the presence of 6 body fluids/tissues (blood, semen, saliva, vaginal secretions, menstrual blood, skin) and, via coding region SNPs (cSNPs) present in the body fluid-specific mRNA transcripts, directly associate particular body fluids with their specific DNA donors in mixtures. The original blood, semen, and saliva (BSS) assay contains 23 cSNPs for blood, semen, and saliva, while the expanded 6F (all 6 fluids/tissues) assay encompasses the BSS assay and also contains 23 additional cSNPs for vaginal secretions, menstrual blood, and skin. Software tools were developed to infer the identity of the body fluids present as well as providing the corresponding cSNP genotypes. Concomitant genomic DNA assays (BSS-d and 6F-d), required to genotype the same cSNPs from persons of interest/inferred contributors to the body fluid mixture, were also developed. Body fluid specificity was demonstrated by the ability to identify the body fluid origin of single-source and two-fluid admixtures. The discriminatory power (European Caucasians) for all body fluids is 0.957-0.997, with linkage disequilibrium considered. Reciprocal body fluid admixtures (mixture pairs with the same two donors but reversed body fluid types) were used to demonstrate the ability to identify the body fluid source of origin as well as associate the donor of each of the two fluids.

Predicting the origin of stains from whole miRNome massively parallel sequencing data.

In this study, we have screened the six most relevant forensic body fluids / tissues, namely blood, semen, saliva, vaginal secretion, menstrual blood and skin, for miRNAs using a whole miRNome massively parallel sequencing approach. We applied partial least squares (PLS) and linear discriminant analysis (LDA) to predict body fluids based on the expression of the miRNA markers. We estimated the prediction accuracy for models including different subsets of miRNA markers to identify the minimum number of markers needed for sufficient prediction performance. For one selected model consisting of 9 miRNA markers we calculated their importance for prediction of each of the six different body fluid categories.

HIrisPlex-S system for eye, hair, and skin color prediction from DNA: Massively parallel sequencing solutions for two common forensically used platforms.

Forensic DNA Phenotyping (FDP) provides the ability to predict externally visible characteristics from minute amounts of crime scene DNA, which can help find unknown perpetrators who are typically unidentifiable via conventional forensic DNA profiling. Fundamental human genetics research has led to a better understanding of the specific DNA variants responsible for physical appearance characteristics, particularly eye, hair, and skin color. Recently, we introduced the HIrisPlex-S system for the simultaneous prediction of eye, hair, and skin color based on 41 DNA variants generated from two forensically validated SNaPshot multiplex assays using capillary electrophoresis (CE). Here we introduce 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. Additionally, we present the forensic developmental validation of the two HPS-MPS assays. The Ion Torrent MPS assay, based on Ion AmpliSeq technology, illustrated the successful generation of full HIrisPlex-S genotypic profiles from 100 pg of input control DNA, while the MiSeq MPS assay based on an in-house design yielded complete profiles from 250 pg of input 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 publically accessible HIrisPlex online webtool (https://hirisplex.erasmusmc.nl). In addition, we updated the website to accept VCF input data for those with genome sequence data. We thus provide a user-friendly and semi-automated MPS workflow from DNA sample to individual eye, hair, and skin color prediction probabilities. Furthermore, we present a 2-person mixture separation tool that not only assesses genotype reliability with regards genotyping confidence but also provides the most fitting mixture scenario for both minor and major contributors, including profile separation. We 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.

Predicting the origin of stains from next generation sequencing mRNA data.

We used our previously published NGS mRNA approach for body fluid identification to analyse 183 body fluids/tissues, including mock casework samples. The resulting data set was used to build a probabilistic model that predicts the origin of a stain. Our approach uses partial least squares followed by linear discriminant analysis to classify samples into six commonly occurring forensic body fluids. The model differs from the ones previously suggested in that it incorporates quantitative information (NGS read counts) rather than just presence/absence of markers. The suggested approach also allows for visualisation of important markers and their correlation with the different body fluids. We compared our model to previously published methods to show that the inclusion of read count information improves the prediction. Finally, we applied the model to mixed body fluid samples to test its ability to identify the individual components in a mixture.