ProbRank: An efficient DNA database search method for complex mixtures per a quantitative likelihood ratio model.

Searching a DNA Database with a DNA profile from an evidentiary trace can provide investigative leads in a forensic case. Various searching approaches exist such as conventional methods based on matching alleles or more advanced methods computing likelihood ratios (LR) while considering drop-in and drop-out. Here we examine the potential of using a quantitative LR model (EuroForMix model incorporated in ProbRank method) that takes peak heights into account in comparison to a qualitative LR model (LRmix model implemented in SmartRank method). Both methods present DNA database candidates in order of decreasing LR. Especially regarding minor contributors in complex mixtures, the method using the quantitative model outperforms the method using the qualitative model in terms of sensitivity and specificity as more true donors and less adventitious matches are retrieved. ProbRank is to be implemented in DNAStatistX and is sufficiently fast for daily use.

Targeted DNA methylation analysis and prediction of smoking habits in blood based on massively parallel sequencing.

Tobacco smoking is a frequent habit sustained by > 1.3 billion people in 2020 and the leading preventable factor for health risk and premature mortality worldwide. In the forensic context, predicting smoking habits from biological samples may allow broadening DNA phenotyping. In this study, we aimed to implement previously published smoking habit classification models based on blood DNA methylation at 13 CpGs. First, we developed a matching lab tool based on bisulfite conversion and multiplex PCR followed by amplification-free library preparation and targeted paired-end massively parallel sequencing (MPS). Analysis of six technical duplicates revealed high reproducibility of methylation measurements (Pearson correlation of 0.983). Artificially methylated standards uncovered marker-specific amplification bias, which we corrected via bi-exponential models. We then applied our MPS tool to 232 blood samples from Europeans of a wide age range, of which 90 were current, 71 former and 71 never smokers. On average, we obtained 189,000 reads/sample and 15,000 reads/CpG, without marker drop-out. Methylation distributions per smoking category roughly corresponded to previous microarray analysis, showcasing large inter-individual variation but with technology-driven bias. Methylation at 11 out of 13 smoking-CpGs correlated with daily cigarettes in current smokers, while solely one was weakly correlated with time since cessation in former smokers. Interestingly, eight smoking-CpGs correlated with age, and one displayed weak but significant sex-associated methylation differences. Using bias-uncorrected MPS data, smoking habits were relatively accurately predicted using both two- (current/non-current) and three- (never/former/current) category model, but bias correction resulted in worse prediction performance for both models. Finally, to account for technology-driven variation, we built new, joint models with inter-technology corrections, which resulted in improved prediction results for both models, with or without PCR bias correction (e.g. MPS cross-validation F1-score > 0.8; 2-categories). Overall, our novel assay takes us one step closer towards the forensic application of viable smoking habit prediction from blood traces. However, future research is needed towards forensically validating the assay, especially in terms of sensitivity. We also need to further shed light on the employed biomarkers, particularly on the mechanistics, tissue specificity and putative confounders of smoking epigenetic signatures.

Comparison of genotyping and weight of evidence results when applying different genotyping strategies on samples from a DNA transfer experiment.

In this study, we assessed to what extent data on the subject of TPPR (transfer, persistence, prevalence, recovery) that are obtained through an older STR typing kit can be used in an activity-level evaluation for a case profiled with a more modern STR kit. Newer kits generally hold more loci and may show higher sensitivity especially when reduced reaction volumes are used, and this could increase the evidential value at the source level. On the other hand, the increased genotyping information may invoke a higher number of contributors in the weight of evidence calculations, which could affect the evidential values as well. An activity scenario well explored in earlier studies [1,2] was redone using volunteers with known DNA profiles. DNA extracts were analyzed with three different approaches, namely using the optimal DNA input for (1) an older and (2) a newer STR typing system, and (3) using a standard, volume-based input combined with replicate PCR analysis with only the newer STR kit. The genotyping results were analyzed for various aspects such as percentage detected alleles and relative peak height contribution for background and the contributors known to be involved in the activity. Next, source-level LRs were calculated and the same trends were observed with regard to inclusionary and exclusionary LRs for persons who had or had not been in direct contact with the sampled areas. We subsequently assessed the impact on the outcome of the activity-level evaluation in an exemplary case by applying the assigned probabilities to a Bayesian network. We infer that data from different STR kits can be combined in the activity-level evaluations.

Experiences in successive forensic analysis of GSR and DNA traces collected on a single tape lift stub.

Gunshot residue (GSR) and deoxyribonucleic acid (DNA) analyses are used in the reconstruction of shooting incidents involving firearms. In both fields, adhesive tapes are commonly applied to recover traces. Using a simultaneous sampling approach for retrieving both types of traces from objects related to a shooting can be powerful. We present the results for DNA testing and GSR analysis using "DNA stubs" to recover both types of traces in one sampling. The analyses are successive: stubs are first examined with scanning electron microscopy for the presence of GSR and next subjected to DNA extraction and short tandem repeat (STR) typing. The results show no negative effect on the DNA and GSR results. The risk of DNA contamination during GSR analysis is addressed, showing that it is negligible. Our experiences in casework show that the approach can be successfully implemented.

Advancing FDSTools by integrating STRNaming 1.1.

The introduction of massively parallel sequencing in forensic analysis has been facilitated with typing kits, analysis software and allele naming tools such as the ForenSeq DNA Signature Prep (DSP) kit, FDSTools and STRNaming respectively. Here we describe how FDSTools 2.0 with integrated and refined STRNaming nomenclature was validated for implementation under ISO 17025 accreditation for the ForenSeq DSP kit. Newly-added options result in efficient automatic allele calling for the majority of markers while specific settings are applied for 'novel' sequence variants to avoid the calling of remaining variable noise observed in samples sequenced with the ForenSeq DSP kit that seem to arise in the PCR. Genome-wide built-in reference data allows for greatly simplified configuration of allele naming for human targets.

Evaluation of the VISAGE basic tool for appearance and ancestry inference using ForenSeq® chemistry on the MiSeq FGx® system.

The possibility of providing investigative leads when conventional DNA identification methods fail to solve a case can be of extreme relevance to law enforcement. Therefore, the forensic genetics community has focused research towards the broadened use of DNA, particularly for prediction of appearance traits, bio-geographical ancestry and age. The VISible Attributes through GEnomics (VISAGE) Consortium expanded the use of DNA phenotyping by developing new molecular and statistical tools for appearance, age and ancestry prediction. The VISAGE basic tool for appearance (EVC) and ancestry (BGA) prediction was initially developed using Ampliseq chemistry, but here is being evaluated using ForenSeq chemistry. The VISAGE basic tool offers a total of 41 EVC and 115 BGA SNPs and thus provides more predictions, i.e., skin color, than achieved with the ForenSeq DNA Signature Prep kit that is based on 24 EVC and 56 BGA SNPs. Five VISAGE laboratories participated in collaborative experiments to provide foreground for developmental validation of the assay. Assessment of assay performance and quality metrics, reproducibility, sensitivity, inhibitor tolerance and species specificity are described. Furthermore, the assay was tested using challenging samples such as mock casework samples and artificially degraded DNA. Two different analysis strategies were applied for this study and output on genotype calls and read depth was compared. Overall, inter-laboratory, inter-method and concordance with publicly available data were analysed and compared. Finally, the results showed a reliable and robust tool, which can be easily applied for laboratories already using a MiSeq FGx with ForenSeq reagents.

On the Identification of Body Fluids and Tissues: A Crucial Link in the Investigation and Solution of Crime.

Body fluid and body tissue identification are important in forensic science as they can provide key evidence in a criminal investigation and may assist the court in reaching conclusions. Establishing a link between identifying the fluid or tissue and the DNA profile adds further weight to this evidence. Many forensic laboratories retain techniques for the identification of biological fluids that have been widely used for some time. More recently, many different biomarkers and technologies have been proposed for identification of body fluids and tissues of forensic relevance some of which are now used in forensic casework. Here, we summarize the role of body fluid/ tissue identification in the evaluation of forensic evidence, describe how such evidence is detected at the crime scene and in the laboratory, elaborate different technologies available to do this, and reflect real life experiences. We explain how, by including this information, crucial links can be made to aid in the investigation and solution of crime.

RMplex: An efficient method for analyzing 30 Y-STRs with high mutation rates.

Y-chromosomal short tandem repeats (Y-STRs) with high mutation rates are recognized as valuable genetic markers for differentiating paternally related men, who typically cannot be separated with standard Y-STRs, and were shown to provide paternal lineage differentiation on a higher resolution level than standard Y-STRs. Both features make Y-STRs with high mutation rates relevant in criminal casework, particularly in sexual assault cases involving highly unbalanced male-female DNA mixtures that often fail autosomal forensic STR profiling for the male donor. Previously, the number of known Y-STRs with mutation rates higher than 10-2 per locus per generation termed rapidly mutating Y-STRs (RM Y-STRs) was limited to 13, which has recently been overcome by the discovery and characterization of 12 additional RM Y-STRs. Here, we present the development and validation of RMplex, an efficient genotyping system for analyzing 30 Y-STRs with high mutation rates, including all currently known RM Y-STRs, using multiplex PCR with capillary electrophoresis (CE) or massively parallel sequencing (MPS), overall targeting a total of 44 male-specific loci. If previously unavailable, repeat number assignations were provided based on newly generated MPS data. Validation tests based on the CE method demonstrated that the results were both repeatable and reproducible, full profiles were achieved with minimal input DNA of 250 pg for RMplex 1 and 100 pg for RMplex 2, and in the presence of inhibitors, or with a surplus of female DNA, the assays performed reasonably well. Application of RMplex to differentiate between paternally related men was exemplified in 32 males belonging to five different paternal pedigrees. Given further successful forensic validation testing, we envision the future application of RMplex in criminal cases where it is suspected, or cannot be excluded, that the crime scene trace originated from a male relatives of the suspect who is highlighted with standard Y-STR matching. Other applications of RMplex are in criminal cases without known suspects to differentiate between male relatives highlighted in familial searching based on standard Y-STR matching.

Application of a probabilistic genotyping software to MPS mixture STR data is supported by similar trends in LRs compared with CE data.

The interpretation of short tandem repeat (STR) profiles can be challenging when, for example, alleles are masked due to allele sharing among contributors and/or when they are subject to drop-out, for instance from sample degradation. Mixture interpretation can be improved by increasing the number of STRs and/or loci with a higher discriminatory power. Both capillary electrophoresis (CE, 6-dye) and massively parallel sequencing (MPS) provide a platform for analysing relatively large numbers of autosomal STRs. In addition, MPS enables distinguishing between sequence variants, resulting in enlarged discriminatory power. Also, MPS allows for small amplicon sizes for all loci as spacing is not an issue, which is beneficial with degraded DNA. Altogether, MPS has the potential to increase the weights of evidence for true contributors to (complex) DNA profiles. In this study, likelihood ratio (LR) calculations were performed using STR profiles obtained with two different MPS systems and analysed using different settings: 1) MPS PowerSeq™ Auto System profiles analysed using FDSTools equipped with optimized settings such as noise correction, 2) ForenSeq™ DNA Signature Prep Kit profiles analysed using the default settings in the Universal Analysis Software (UAS), and 3) ForenSeq™ DNA Signature Prep Kit profiles analysed using FDSTools empirically adapted to cope with one-directional reads and provisional, basic settings. The LR calculations used genotyping data for two- to four-person mixtures varying for mixture proportion, level of drop-out and allele sharing and were generated with the continuous model EuroForMix. The LR results for the over 2000 sets of propositions were affected by the variation for the number of markers and analysis settings used in the three approaches. Nevertheless, trends for true and non-contributors, effects of replicates, assigned number of contributors, and model validation results were comparable for the three MPS approaches and alike the trends known for CE data. Based on this analogy, we regard the probabilistic interpretation of MPS STR data fit for forensic DNA casework. In addition, guidelines were derived on when to apply LR calculations to MPS autosomal STR data and report the corresponding results.

STRNaming: Generating simple, informative names for sequenced STR alleles in a standardised and automated manner.

The introduction of Massively Parallel Sequencing in the forensic domain has exposed the need for comprehensive nomenclature of sequenced Short Tandem Repeat (STR) alleles. In general, three strategies are at hand: 1) the full sequence mapped to the human genome reference sequence, which ensures exact data exchange; 2) shortened, human-readable formats for forensic reporting and data presentation and 3) very short codes that enable compact figures and tables but do not convey any sequence information. Here, we describe an algorithm of the second type: STRNaming, which generates human-readable names for sequenced STR alleles. STRNaming is guided by a reference sequence at each locus and then functions independently to automatically assign a unique, sequence-descriptive name that also includes the capillary electrophoresis allele number. STRNaming settings were established based on preferences that were surveyed internationally in the forensic community. These settings ensure that a small change in the sequence corresponds to a small change in the allele name, which is helpful for recognising for instance stutter products. Sequence variants outside of the repeat units are indicated as simple variant calls. Since the STR name is sequence-descriptive, the sequence can be traced back from the allele name. Because STRNaming is fully guided by an assignable reference sequence, no central coordination or configuration is required and the method will work for any STR locus, be it autosomal, Y-, X-chromosomal in current or future use. The algorithm is publicly available online and offline.

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.

Reducing the Number of Mismatches between Hairs and Buccal References When Analysing mtDNA Heteroplasmic Variation by Massively Parallel Sequencing.

In forensics, mitochondrial DNA (mtDNA) analysis is foremost applied to rootless hairs often lacking detectable nuclear DNA. Sanger sequencing is the routine mtDNA method in most forensic laboratories, even though interpretation of mixed samples and heteroplasmic sites can be challenging. Individuals may hold cells with low-level heteroplasmy variants below the detection threshold and other cells where this minor variant is the major one. This difference may be interpreted as a mismatch between reference and evidentiary trace samples, such as buccal specimens and rootless hairs. Such mismatches may be solved by Massively Parallel Sequencing (MPS), allowing more sensitive quantitative analysis for mixed positions than Sanger. The mtDNA control region was analysed in buccal reference samples from 26 individuals and 475 corresponding hairs by MPS and compared to Sanger sequencing data generated on the same samples. With MPS, mixed contributions down to 3% were regarded, leading to a substantial increase in the frequency of heteroplasmy. Our results demonstrate that previously reported mismatches between buccal reference and hair shaft samples by Sanger are detected as low-level heteroplasmy by MPS. A detailed overview of buccal and hair heteroplasmy is provided and implications for MPS-based mtDNA analysis in the context of forensic cases are discussed.

Multi-laboratory validation of DNAxs including the statistical library DNAStatistX.

This study describes a multi-laboratory validation of DNAxs, a DNA eXpert System for the data management and probabilistic interpretation of DNA profiles [1], and its statistical library DNAStatistX to which, besides the organising laboratory, four laboratories participated. The software was modified to read multiple data formats and the study was performed prior to the release of the software to the forensic community. The first exercise explored all main functionalities of DNAxs with feedback on user-friendliness, installation and general performance. Next, every laboratory performed likelihood ratio (LR) calculations using their own dataset and a dataset provided by the organising laboratory. The organising laboratory performed LR calculations using all datasets. The datasets were generated with different STR typing kits or analysis systems and consisted of samples varying in DNA amounts, mixture ratios, number of contributors and drop-out level. Hypothesis sets had the correct, under- and over-assigned number of contributors and true and false donors as person of interest. When comparing the results between laboratories, the LRs were foremost within one unit on log10 scale. The few LR results that deviated more had differences for the parameters estimated by the optimizer within DNAStatistX. Some of these were indicated by failed iteration results, others by a failed model validation, since unrealistic hypotheses were included. When these results that do not meet the quality criteria were excluded, as is in accordance with interpretation guidelines, none of the analyses in the different laboratories yielded a different statement in the casework report. Nonetheless, changes in software parameters were sought that minimized differences in outcomes, which made the DNAStatistX module more robust. Overall, the software was found intuitive, user-friendly and valid for use in multiple laboratories.

DNA commission of the International society for forensic genetics: Assessing the value of forensic biological evidence - Guidelines highlighting the importance of propositions. Part II: Evaluation of biological traces considering activity level propositions.

The value of the evidence depends critically on propositions. In the second of two papers intended to provide advice to the community on difficult aspects of evaluation and the formulation of propositions, we focus primarily on activity level propositions. This helps the court address the question of "How did an individual's cell material get there?". In order to do this, we expand the framework outlined in the first companion paper. First, it is important not to conflate results and propositions. Statements given activity level propositions aim to help address issues of indirect vs direct transfer, and the time of the activity, but it is important to avoid use of the word 'transfer' in propositions. This is because propositions are assessed by the Court, but DNA transfer is a factor that scientists need to take into account for the interpretation of their results. Suitable activity level propositions are ideally set before knowledge of the results and address issues like: X stabbed Y vs. an unknown person stabbed Y but X met Y the day before. The scientist assigns the probability of the evidence, if each of the alternate propositions is true, to derive a likelihood ratio. To do this, the scientist asks: a) "what are the expectations if each of the propositions is true?" b) "What data are available to assist in the evaluation of the results given the propositions?" When presenting evidence, scientists work within the hierarchy of propositions framework. The value of evidence calculated for a DNA profile cannot be carried over to higher levels in the hierarchy - the calculations given sub-source, source and activity level propositions are all separate. A number of examples are provided to illustrate the principles espoused, and the criteria that such assessments should meet. Ideally in order to assign probabilities, the analyst should have/collect data that are relevant to the case in question. These data must be relevant to the case at hand and we encourage further research and collection of data to form knowledge bases. Bayesian Networks are extremely useful to help us think about a problem, because they force us to consider all relevant possibilities in a logical way. An example is provided.

Inter-laboratory study on standardized MPS libraries: evaluation of performance, concordance, and sensitivity using mixtures and degraded DNA.

We present results from an inter-laboratory massively parallel sequencing (MPS) study in the framework of the SeqForSTRs project to evaluate forensically relevant parameters, such as performance, concordance, and sensitivity, using a standardized sequencing library including reference material, mixtures, and ancient DNA samples. The standardized library was prepared using the ForenSeq DNA Signature Prep Kit (primer mix A). The library was shared between eight European laboratories located in Austria, France, Germany, The Netherlands, and Sweden to perform MPS on their particular MiSeq FGx sequencers. Despite variation in performance between sequencing runs, all laboratories obtained quality metrics that fell within the manufacturer's recommended ranges. Furthermore, differences in locus coverage did not inevitably adversely affect heterozygous balance. Inter-laboratory concordance showed 100% concordant genotypes for the included autosomal and Y-STRs, and still, X-STR concordance exceeded 83%. The exclusive reasons for X-STR discordances were drop-outs at DXS10103. Sensitivity experiments demonstrated that correct allele calling varied between sequencing instruments in particular for lower DNA amounts (≤ 125 pg). The analysis of compromised DNA samples showed the drop-out of one sample (FA10013B01A) while for the remaining three degraded DNA samples MPS was able to successfully type ≥ 87% of all aSTRs, ≥ 78% of all Y-STRs, ≥ 68% of all X-STRs, and ≥ 92% of all iSNPs demonstrating that MPS is a promising tool for human identity testing, which in return, has to undergo rigorous in-house validation before it can be implemented into forensic routine casework.

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.

An assessment of the performance of the probabilistic genotyping software EuroForMix: Trends in likelihood ratios and analysis of Type I & II errors.

Continuous probabilistic genotyping software enables the interpretation of highly complex DNA profiles that are prone to stochastic effects and/or consist of multiple contributions. The process of introducing probabilistic genotyping into an accredited forensic laboratory requires testing, validation, documentation and training. Documents that include guidelines and/or requirements have been published in order to guide forensic laboratories through this extensive process and there has been encouragements to share the results obtained from internal laboratory studies. To this end, we present the results obtained from the quantitative probabilistic genotyping system EuroForMix applied to mixed DNA profiles with known contributions mixed in known proportions, levels of allele sharing and levels of allelic drop-out. The mixtures were profiled using the PowerPlex® Fusion 6C (PPF6C) kit. Using these mixtures, 427 Hp-true tests and 408 Hd-true tests were performed. In the Hd-true tests, non-contributors were selected deliberately to a have large overlap with the alleles within the mixture and worst-case scenarios were examined in which a simulated relative of one of the true donors was considered as the person of interest under the prosecution hypothesis. The effects of selecting different EuroForMix modelling options, the use of PCR replicates, allelic drop-out, and varying the assigned number of contributors were examined. Instances of Type I and Type II errors are discussed. In addition 330 likelihood ratio results from EuroForMix are compared to the semi-continuous model LRmix Studio. Results demonstrate the performance and trends of EuroForMix when applied to PPF6C profiles.

DNAxs/DNAStatistX: Development and validation of a software suite for the data management and probabilistic interpretation of DNA profiles.

The data management, interpretation and comparison of sets of DNA profiles can be complex, time-consuming and error-prone when performed manually. This, combined with the growing numbers of genetic markers in forensic identification systems calls for expert systems that can automatically compare genotyping results within (large) sets of DNA profiles and assist in profile interpretation. To that aim, we developed a user-friendly software program or DNA eXpert System that is denoted DNAxs. This software includes features to view, infer and match autosomal short tandem repeat profiles with connectivity to up and downstream software programs. Furthermore, DNAxs has imbedded the 'DNAStatistX' module, a statistical library that contains a probabilistic algorithm to calculate likelihood ratios (LRs). This algorithm is largely based on the source code of the quantitative probabilistic genotyping system EuroForMix [1]. The statistical library, DNAStatistX, supports parallel computing which can be delegated to a computer cluster and enables automated queuing of requested LR calculations. DNAStatistX is written in Java and is accessible separately or via DNAxs. Using true and non-contributors to DNA profiles with up to four contributors, the DNAStatistX accuracy and precision were assessed by comparing the DNAStatistX results to those of EuroForMix. Results were the same up to rare differences that could be attributed to the different optimizers used in both software programs. Implementation of dye specific detection thresholds resulted in larger likelihood values and thus a better explanation of the data used in this study. Furthermore, processing time, robustness of DNAStatistX results and the circumstances under which model validations failed were examined. Finally, guidelines for application of the software are shared as an example. The DNAxs software is future-proof as it applies a modular approach by which novel functionalities can be incorporated.

Towards broadening Forensic DNA Phenotyping beyond pigmentation: Improving the prediction of head hair shape from DNA.

Human head hair shape, commonly classified as straight, wavy, curly or frizzy, is an attractive target for Forensic DNA Phenotyping and other applications of human appearance prediction from DNA such as in paleogenetics. The genetic knowledge underlying head hair shape variation was recently improved by the outcome of a series of genome-wide association and replication studies in a total of 26,964 subjects, highlighting 12 loci of which 8 were novel and introducing a prediction model for Europeans based on 14 SNPs. In the present study, we evaluated the capacity of DNA-based head hair shape prediction by investigating an extended set of candidate SNP predictors and by using an independent set of samples for model validation. Prediction model building was carried out in 9674 subjects (6068 from Europe, 2899 from Asia and 707 of admixed European and Asian ancestries), used previously, by considering a novel list of 90 candidate SNPs. For model validation, genotype and phenotype data were newly collected in 2415 independent subjects (2138 Europeans and 277 non-Europeans) by applying two targeted massively parallel sequencing platforms, Ion Torrent PGM and MiSeq, or the MassARRAY platform. A binomial model was developed to predict straight vs. non-straight hair based on 32 SNPs from 26 genetic loci we identified as significantly contributing to the model. This model achieved prediction accuracies, expressed as AUC, of 0.664 in Europeans and 0.789 in non-Europeans; the statistically significant difference was explained mostly by the effect of one EDAR SNP in non-Europeans. Considering sex and age, in addition to the SNPs, slightly and insignificantly increased the prediction accuracies (AUC of 0.680 and 0.800, respectively). Based on the sample size and candidate DNA markers investigated, this study provides the most robust, validated, and accurate statistical prediction models and SNP predictor marker sets currently available for predicting head hair shape from DNA, providing the next step towards broadening Forensic DNA Phenotyping beyond pigmentation traits.