Spitting in the wind?-The challenges of RNA sequencing for biomarker discovery from saliva.

Forensic trace contextualization, i.e., assessing information beyond who deposited a biological stain, has become an issue of great and steadily growing importance in forensic genetic casework and research. The human transcriptome encodes a wide variety of information and thus has received increasing interest for the identification of biomarkers for different aspects of forensic trace contextualization over the past years. Massively parallel sequencing of reverse-transcribed RNA ("RNA sequencing") has emerged as the gold standard technology to characterize the transcriptome in its entirety and identify RNA markers showing significant expression differences not only between different forensically relevant body fluids but also within a single body fluid between forensically relevant conditions of interest. Here, we analyze the quality and composition of four RNA sequencing datasets (whole transcriptome as well as miRNA sequencing) from two different research projects (the RNAgE project and the TrACES project), aiming at identifying contextualizing forensic biomarker from the forensically relevant body fluid saliva. We describe and characterize challenges of RNA sequencing of saliva samples arising from the presence of oral bacteria, the heterogeneity of sample composition, and the confounding factor of degradation. Based on these observations, we formulate recommendations that might help to improve RNA biomarker discovery from the challenging but forensically relevant body fluid saliva.

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.

Pedigree-based relationship inference from complex DNA mixtures.

We present a general method for analysing DNA mixtures involving relatives that accounts for dropout and drop-in, mutations, silent alleles and population substructure. Whether the aim is to identify the contributors to a mixture who may be related, or to determine the relationship between individuals based on a DNA mixture, both types of problems can be handled by the method and software presented here. We focus on the latter scenario, motivated by non-invasive prenatal paternity testing where the profile of the child is available only in the form of a mixture with the mother's profile. Relationships are represented by pedigrees and can include kinship between more than two individuals. The software is freely available as a graphical user interface in the R package relMix.

Relationship inference based on DNA mixtures.

Today, there exists a number of tools for solving kinship cases. But what happens when information comes from a mixture? DNA mixtures are in general rarely seen in kinship cases, but in a case presented to the Norwegian Institute of Public Health, sample DNA was obtained after a rape case that resulted in an unwanted pregnancy and abortion. The only available DNA from the fetus came in form of a mixture with the mother, and it was of interest to find the father of the fetus. The mother (the victim), however, refused to give her reference data and so commonly used methods for paternity testing were no longer applicable. As this case illustrates, kinship cases involving mixtures and missing reference profiles do occur and make the use of existing methods rather inconvenient. We here present statistical methods that may handle general relationship inference based on DNA mixtures. The basic idea is that likelihood calculations for mixtures can be decomposed into a series of kinship problems. This formulation of the problem facilitates the use of kinship software. We present the freely available R package relMix which extends on the R version of Familias. Complicating factors like mutations, silent alleles, and θ-correction are then easily handled for quite general family relationships, and are included in the statistical methods we develop in this paper. The methods and their implementations are exemplified on the data from the rape case.

Mixtures with relatives and linked markers.

Mixture DNA profiles commonly appear in forensic genetics, and a large number of statistical methods and software are available for such cases. However, most of the literature concerns mixtures where the contributors are assumed unrelated and the genetic markers are unlinked. In this paper, we consider mixtures of linked markers and related contributors. If no relationships are involved, linkage can be ignored. While unlinked markers can be treated independently, linkage introduces dependencies. The use of linked markers presents statistical and computational challenges, but may also lead to a considerable increase in power since the number of markers available is much larger if we do not require the markers to be unlinked. In addition, some cases that cannot be solved with an unlimited number of unlinked autosomal markers can be solved with linked markers. We focus on two special cases of linked markers: pairs of linked autosomal markers and X-chromosomal markers. A framework is presented for calculation of likelihood ratios for mixtures with general relationships and with linkage between any number of markers. Finally, we explore the effect of linkage disequilibrium, also called allelic association, on the likelihood ratio.

Models and implementation for relationship problems with dropout.

Allelic dropout in relationship problems may commonly appear in areas such as disaster victim identification and the identification of missing persons. If dropout is not accounted for, the results may be incorrect interpretation of profiles, loss of valuable information and biased results. In this paper, we explore different models for dropout in kinship cases and present an efficient implementation for one of the models. The implementation allows for dropout to be handled simultaneously with phenomena like silent alleles and mutations that may also cause discordances in relationship data, in addition to subpopulation correction. The implemented dropout model is freely available in the new version of the Familias software. The concepts and methods are illustrated on real and simulated data.

Rotation gene set testing for longitudinal expression data.

Gene set analysis methods are popular tools for identifying differentially expressed gene sets in microarray data. Most existing methods use a permutation test to assess significance for each gene set. The permutation test's assumption of exchangeable samples is often not satisfied for time-series data and complex experimental designs, and in addition it requires a certain number of samples to compute p-values accurately. The method presented here uses a rotation test rather than a permutation test to assess significance. The rotation test can compute accurate p-values also for very small sample sizes. The method can handle complex designs and is particularly suited for longitudinal microarray data where the samples may have complex correlation structures. Dependencies between genes, modeled with the use of gene networks, are incorporated in the estimation of correlations between samples. In addition, the method can test for both gene sets that are differentially expressed and gene sets that show strong time trends. We show on simulated longitudinal data that the ability to identify important gene sets may be improved by taking the correlation structure between samples into account. Applied to real data, the method identifies both gene sets with constant expression and gene sets with strong time trends.

Selecting mRNA markers in blood for age estimation of the donor of a biological stain.

RNA has gained a substantial amount of attention within the forensic field over the last decade. There is evidence that RNAs are differentially expressed with biological age. Since RNA can be co-extracted with DNA from the same piece of evidence, RNA-based analysis appears as a promising molecular alternative for predicting the biological age and hence inferring the chronological age of a person. Using RNA-Seq data we searched for markers in blood potentially associated with age. We used our own RNA-Seq data from dried blood stains as well as publicly available RNA-Seq data from whole blood, and compared two different approaches to select candidate markers. The first approach focused on individual gene analysis with DESeq2 to select the genes most correlated with age, while the second approach employed lasso regression to select a set of genes for optimal prediction of age. We present two lists with 270 candidate markers, one for each approach.

Smoothing gene expression data with network information improves consistency of regulated genes.

Gene set analysis methods have become a widely used tool for including prior biological knowledge in the statistical analysis of gene expression data. Advantages of these methods include increased sensitivity, easier interpretation and more conformity in the results. However, gene set methods do not employ all the available information about gene relations. Genes are arranged in complex networks where the network distances contain detailed information about inter-gene dependencies. We propose a method that uses gene networks to smooth gene expression data with the aim of reducing the number of false positives and identify important subnetworks. Gene dependencies are extracted from the network topology and are used to smooth genewise test statistics. To find the optimal degree of smoothing, we propose using a criterion that considers the correlation between the network and the data. The network smoothing is shown to improve the ability to identify important genes in simulated data. Applied to a real data set, the smoothing accentuates parts of the network with a high density of differentially expressed genes.

In vitro comparison of commensal, probiotic and pathogenic strains of Enterococcus faecalis.

In vivo studies have provided evidence that micro-organisms have important roles in immunological, digestive and respiratory functions, conferring health benefits on the host. Several in vitro methods have been advised for the initial screening of microbes with potential health effects. The objective of the present study was to employ such in vitro methodology to characterise different strains of Enterococcus faecalis. The characteristics of a commercial product marketed as a probiotic, Symbioflor-1 (Symbiopharm), were compared with the characteristics of both pathogenic and commensal strains. Tolerance towards low pH and viability after exposure to human gastric and duodenal juices were assayed. Symbioflor-1 was the most susceptible strain to these treatments when compared with the other E. faecalis strains. Furthermore, Symbioflor-1 exhibited the lowest adhesion capacity to intestinal epithelial cells (IEC) and mucus. Competitive binding studies using heparin indicated that glycosaminoglycans might be involved in the adhesion to IEC, but also that differences in these putative bacteria-host interactions do not cause the relative low adhesion capacity of Symbioflor-1. Maturation of dendritic cells (DC) after exposure to bacteria was assayed as an indication of an immunomodulatory effect. All strains induced a moderate elevation of the DC maturation markers CD83 and CD86; however, no strain-specific differences were detected. Correlations between in vitro and in vivo studies are discussed. Although in vitro assaying is a rational starting point for the selection of microbes with a potential health benefit, it is emphasised that human clinical trials are the definite tool for establishing probiotic status.

Source level interpretation of mixed biological stains using coding region SNPs.

The association of body fluids/cell types and donors in mixed biological traces is an important, but challenging task required to evaluate the value of evidence given forensic propositions concerning the source of the DNA. The linking of a DNA profile with evidence from presumptive tests or RNA analysis is not straightforward. Coding region SNPs (cSNPs) are a novel type of evidential markers that are both cell type specific and individual specific. They thereby provide a direct link between a donor and a body fluid in mixed biological stains. In this proof-of-concept paper we consider the evaluation of cSNP profiles given source level propositions and explore the use of the open-source software EuroForMix to compute likelihood ratios. The discrimination power of the cSNPs for various body fluids is investigated with simulations. We provide case examples where the type of biological material is questioned and where cSNP profiles can be used to assign a donor to a body fluid, and discuss how the results can be reported in court.

Sampling touch DNA from human skin following skin-to-skin contact in mock assault scenarios-A comparison of nine collection methods.

Collection of touch DNA from an offender on the victim's skin can provide relevant evidence for investigations of criminal cases. Therefore, the choice of the optimal sample collection method is crucial. In this study, we investigated the recovery of STR profiles from touch DNA on human skin by comparing nine different collection methods: the dry and wet cotton swabs in three different movements, the double-swab (wet-dry) method, the wet and dry Copan FLOQSwabs™, and the Scene Safe FAST™ minitapes. Mock assault scenarios were conducted with a male offender grasping the forearms of a female victim. Samples were collected from the assaulted area of the victim's skin, and the recovery of the offender's STR profile was evaluated. Our results indicate that the different swabs and swabbing techniques did not have a distinct impact on the STR recovery; however, the lowest STR recovery was achieved with Scene Safe FAST™ minitapes. In addition, we compared the double-swab method to the single-swab method by analyzing the DNA quantity of the wet and dry swabs separately. We found on average 13.7% more offender DNA using the double-swab method, but this did not translate into higher STR recovery. Our findings indicate that several methods perform equally well when collecting touch DNA from human skin, although SceneSafe FAST™ minitapes seem to be the least adequate for this purpose.

mRNA profiling of mock casework samples: Results of a FoRNAP collaborative exercise.

In recent years, forensic mRNA profiling has increasingly been used to identify the origin of human body fluids. By now, several laboratories have implemented mRNA profiling and also use it in criminal casework. In 2018 the FoRNAP (Forensic RNA Profiling) group was established among a number of these laboratories with the aim of sharing experiences, discussing optimization potential, identifying challenges and suggesting solutions with regards to mRNA profiling and casework. To compare mRNA profiling methods and results a collaborative exercise was organized within the FoRNAP group. Seven laboratories from four countries received 16 stains, comprising six pure body fluid / tissue stains and ten mock casework samples. The laboratories were asked to analyze the provided stains with their in-house method (PCR/CE or MPS) and markers of choice. Five laboratories used a DNA/RNA co-extraction strategy. Overall, up to 11 mRNA markers per body fluid were analyzed. We found that mRNA profiling using different extraction and analysis methods as well as different multiplexes can be applied to casework-like samples. In general, high input samples were typed with high accuracy by all laboratories, regardless of the method used. Irrespective of the analysis strategy, samples of low input or mixed stains were more challenging to analyze and interpret since, alike to DNA profiling, a higher number of markers dropped out and/or additional unexpected markers not consistent with the cell type in question were detected. It could be shown that a plethora of different but valid analysis and interpretation strategies exist and are successfully applied in the Forensic Genetics community. Nevertheless, efforts aiming at optimizing and harmonizing interpretation approaches in order to achieve a higher consistency between laboratories might be desirable in the future. The simultaneous extraction of DNA alongside RNA showed to be an effective approach to identify not only the body fluid present but also to identify the donor(s) of the stain. This allows investigators to gain valuable information about the origin of crime scene samples and the course of events in a crime case.

Rotation testing in gene set enrichment analysis for small direct comparison experiments.

Gene Set Enrichment Analysis (GSEA) is a method for analysing gene expression data with a focus on a priori defined gene sets. The permutation test generally used in GSEA for testing the significance of gene set enrichment involves permutation of a phenotype vector and is developed for data from an indirect comparison design, i.e. unpaired data. In some studies the samples representing two phenotypes are paired, e.g. samples taken from a patient before and after treatment, or if samples representing two phenotypes are hybridised to the same two-channel array (direct comparison design). In this paper we will focus on data from direct comparison experiments, but the methods can be applied to paired data in general. For these types of data, a standard permutation test for paired data that randomly re-signs samples can be used. However, if the sample size is very small, which is often the case for a direct comparison design, a permutation test will give very imprecise estimates of the p-values. Here we propose using a rotation test rather than a permutation test for estimation of significance in GSEA of direct comparison data with a limited number of samples. Our proposed rotation test makes GSEA applicable to direct comparison data with few samples, by depending on rotations of the data instead of permutations. The rotation test is a generalisation of the permutation test, and can in addition be used on indirect comparison data and for testing significance of other types of test statistics outside the GSEA framework.

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.

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.

Genotyping and interpretation of STR-DNA: Low-template, mixtures and database matches-Twenty years of research and development.

The introduction of Short Tandem Repeat (STR) DNA was a revolution within a revolution that transformed forensic DNA profiling into a tool that could be used, for the first time, to create National DNA databases. This transformation would not have been possible without the concurrent development of fluorescent automated sequencers, combined with the ability to multiplex several loci together. Use of the polymerase chain reaction (PCR) increased the sensitivity of the method to enable the analysis of a handful of cells. The first multiplexes were simple: 'the quad', introduced by the defunct UK Forensic Science Service (FSS) in 1994, rapidly followed by a more discriminating 'six-plex' (Second Generation Multiplex) in 1995 that was used to create the world's first national DNA database. The success of the database rapidly outgrew the functionality of the original system - by the year 2000 a new multiplex of ten-loci was introduced to reduce the chance of adventitious matches. The technology was adopted world-wide, albeit with different loci. The political requirement to introduce pan-European databases encouraged standardisation - the development of European Standard Set (ESS) of markers comprising twelve-loci is the latest iteration. Although development has been impressive, the methods used to interpret evidence have lagged behind. For example, the theory to interpret complex DNA profiles (low-level mixtures), had been developed fifteen years ago, but only in the past year or so, are the concepts starting to be widely adopted. A plethora of different models (some commercial and others non-commercial) have appeared. This has led to a confusing 'debate' about the 'best' to use. The different models available are described along with their advantages and disadvantages. A section discusses the development of national DNA databases, along with details of an associated controversy to estimate the strength of evidence of matches. Current methodology is limited to searches of complete profiles - another example where the interpretation of matches has not kept pace with development of theory. STRs have also transformed the area of Disaster Victim Identification (DVI) which frequently requires kinship analysis. However, genotyping efficiency is complicated by complex, degraded DNA profiles. Finally, there is now a detailed understanding of the causes of stochastic effects that cause DNA profiles to exhibit the phenomena of drop-out and drop-in, along with artefacts such as stutters. The phenomena discussed include: heterozygote balance; stutter; degradation; the effect of decreasing quantities of DNA; the dilution effect.

Database extraction strategies for low-template evidence.

Often in forensic cases, the profile of at least one of the contributors to a DNA evidence sample is unknown and a database search is needed to discover possible perpetrators. In this article we consider two types of search strategies to extract suspects from a database using methods based on probability arguments. The performance of the proposed match scores is demonstrated by carrying out a study of each match score relative to the level of allele drop-out in the crime sample, simulating low-template DNA. The efficiency was measured by random man simulation and we compared the performance using the SGM Plus kit and the ESX 17 kit for the Norwegian population, demonstrating that the latter has greatly enhanced power to discover perpetrators of crime in large national DNA databases. The code for the database extraction strategies will be prepared for release in the R-package forensim.

Mixtures with relatives: a pedigree perspective.

DNA mixture evidence pertains to cases where several individuals may have contributed to a biological stain. Statistical methods and software for such problems are available and a large number of cases can be handled adequately. However, one class of mixture problems remains untreated in full generality in the literature, namely when the contributors may be related. Disregarding a plausible close relative of the perpetrator as an alternative contributor (identical twin is the most extreme case) may lead to overestimating the evidence against a suspect. Existing methods only accommodate pairwise relationships such as the case where the suspect and the victim are siblings, for example. In this paper we consider relationships in full generality, conveniently represented by pedigrees. In particular, these pedigrees may involve inbreeding, for instance when the parents of an individual of interest are first cousins. Furthermore our framework handles situations where the opposing parties in a court case (prosecution and defence) propose different family relationships. Consequently, our approach combines classical mixture and kinship problems. The basic idea of this paper is to formulate the problem in a way that allows for the exploitation of currently available methods and software designed originally for linkage applications. We have developed a freely available R package, euroMix based on another package, paramlink, and we illustrate the ideas and methods on real and simulated data.

Exact computation of the distribution of likelihood ratios with forensic applications.

If complex DNA profiles, conditioned on multiple individuals are evaluated, it may be difficult to assess the strength of the evidence based on the likelihood ratio. A likelihood ratio does not give information about the relative weights that are provided by separate contributors. Alternatively, the observed likelihood ratio can be evaluated with respect to the distribution of the likelihood ratio under the defense hypothesis. We present an efficient algorithm to compute an exact distribution of likelihood ratios that can be applied to any LR-based model. The distribution may have several applications, but is used here to compute a p-value that corresponds to the observed likelihood ratio. The p-value is the probability that a profile under the defense hypothesis, substituted for a questioned contributor e.g. suspect, would attain a likelihood ratio which is at least the same magnitude as that observed. The p-value can be thought of as a scaled version of the likelihood ratio, giving a quantitative measure of the strength of the evidence relative to the specified hypotheses and the model used for the analysis. The algorithm is demonstrated on examples based on real data. R code for the algorithm is freely available in the R package euroMix.