Probabilistic Genotyping Software Research Articles

A comparison of likelihood ratios calculated from surface DNA mixtures using MPS and CE Technologies

This study evaluates the performance of analysing surface DNA samples using massively parallel sequencing (MPS) compared to traditional capillary electrophoresis (CE). A total of 30 samples were collected from various surfaces in an office environment and were analysed with CE and MPS. These were compared against 60 reference samples (office inhabitants). To identify contributors, likelihood ratios (LRs) were calculated for MPS and CE data using the probabilistic genotyping software MPSproto and EuroForMix respectively. Although a higher number of sequences/peaks were observed per DNA profile in MPS compared to CE, LR values were found to be lower for MPS data formats. This might be the result of the increased complexity of MPS data, along with a possible elevation of unknown alleles and/or artefacts. The study highlights avenues for improving MPS data quality and analysis to facilitate more robust interpretation of challenging casework-like samples.

Value of DNA mixture-to-mixture comparisons within an operational context

Since 1995, national forensic DNA databases have used a maximum number of contributors, and a minimum number of loci to reduce the risk of providing false leads. DNA profiles of biological traces that do not meet these criteria cannot be loaded into these databases. In 2023, about 10 % of more than 15,000 trace DNA profiles analyzed in western Switzerland were not compared at the national level, even though they were considered to be interpretable, mainly because they contained the DNA from more than two persons. In this situation, police services can request local comparisons with DNA profiles of known persons and/or with other traces, but this occurs in only a small proportion of cases, so that DNA mixtures are rarely used to help detect potential series. The development of probabilistic genotyping software and its associated tools have made possible the efficient performance of this type of comparison, which is based on likelihood ratios (LR) rather than on the number of shared alleles.To highlight potential common contributors for investigation and intelligence purposes, the present study used the mixture-to-mixture tool of the software STRmix v2.7 to compare 235 DNA profiles that cannot be searched the Swiss DNA database. These DNA profiles originated from traces collected by six different police services in 2021 and 2022. Traces were selected by the police based on information that indicated that they were from potential series. Associations between profiles were compared with expected investigative associations to define the value of this approach. Among the 27,495 pairwise comparisons of DNA profiles, 88 pairs (0.3 %) showed at least one potential common contributor when using a LR threshold of 1000. Of these 88 pairs, 60 (68.2 %) were qualified by the police services as “expected” (60/88), 22 (25.0 %) as “possible”, and six (6.8 %) as “unexpected”. Although it is important to consider the limits of this approach (e.g., adventitious or missed associations, cost/benefit evaluation, integration of DNA mixture comparison in the process), these findings indicate that non CODIS loadable DNA mixtures could provide police agencies with information concerning potential series at both the local and national level.

‘Low’ LRs obtained from DNA mixtures: On calibration and discrimination performance of probabilistic genotyping software

The validity of a probabilistic genotyping (PG) system is typically demonstrated by following international guidelines for the developmental and internal validation of PG software. These guidelines mainly focus on discriminatory power. Very few studies have reported with metrics that depend on calibration of likelihood ratio (LR) systems. In this study, discriminatory power as well as various calibration metrics, such as Empirical Cross-Entropy (ECE) plots, pool adjacent violator (PAV) plots, log likelihood ratio cost (Cllr and Cllrcal), fiducial calibration discrepancy plots, and Turing’ expectation were examined using the publicly-available PROVEDIt dataset. The aim was to gain deeper insight into the performance of a variety of PG software in the ‘lower’ LR ranges (∼LR 1–10,000), with focus on DNAStatistX and EuroForMix which use maximum likelihood estimation (MLE). This may be a driving force for the end users to reconsider current LR thresholds for reporting. In previous studies, overstated ‘low’ LRs were observed for these PG software. However, applying (arbitrarily) high LR thresholds for reporting wastes relevant evidential value. This study demonstrates, based on calibration performance, that previously reported LR thresholds can be lowered or even discarded. Considering LRs >1, there was no evidence for miscalibration performance above LR ∼1000 when using Fst 0.01. Below this LR value, miscalibration was observed. Calibration performance generally improved with the use of Fst 0.03, but the extent of this was dependent on the dataset: results ranged from miscalibration up to LR ∼100 to no evidence of miscalibration alike PG software using different methods to model peak height, HMC and STRmix. This study demonstrates that practitioners using MLE-based models should be careful when low LR ranges are reported, though applying arbitrarily high LR thresholds is discouraged. This study also highlights various calibration metrics that are useful in understanding the performance of a PG system.

A collaborative study on the precision of the Markov chain Monte Carlo algorithms used for DNA profile interpretation

Several fully continuous probabilistic genotyping software (PGS) use Markov chain Monte Carlo algorithms (MCMC) to assign weights to different proposed genotype combinations at a locus. Replicate interpretations of the same profile in these software are expected not to produce identical weights and likelihood ratio (LR) values due to the Monte Carlo aspect. This paper reports a detailed precision study under reproducibility conditions conducted as a collaborative exercise across the National Institute of Standards and Technology (NIST), Federal Bureau of Investigation (FBI), and Institute of Environmental Science and Research (ESR). Replicate interpretations generated across the three laboratories used the same input files, software version, and settings but different random number seed and different computers. This work demonstrates that using different computers to analyze replicate interpretations does not contribute to any variations in LR values. The study quantifies the magnitude of differences in the assigned LRs that is only due to run-to-run MCMC variability and addresses the potential explanations for the observed differences.

Extending the discussion on inconsistency in forensic decisions and results.

The subject of inter- and intra-laboratory inconsistency was recently raised in a commentary by Itiel Dror. We re-visit an inter-laboratory trial, with which some of the authors of this current discussion were associated, to diagnose the causes of any differences in the likelihood ratios (LRs) assigned using probabilistic genotyping software. Some of the variation was due to different decisions that would be made on a case-by-case basis, some due to laboratory policy and would hence differ between laboratories, and the final and smallest part was the run-to-run difference caused by the Monte Carlo aspect of the software used. However, the net variation in LRs was considerable. We believe that most laboratories will self-diagnose the cause of their difference from the majority answer and in some, but not all instances will take corrective action. An inter-laboratory exercise consisting of raw data files for relatively straightforward mixtures, such as two mixtures of three or four persons, would allow laboratories to calibrate their procedures and findings.

An overview of autosomal STRs and identity SNPs in a Norwegian population using massively parallel sequencing

In recent years, probabilistic genotyping software has been adapted for the analysis of massively parallel sequencing (MPS) forensic data. Likelihood ratios (LR) are based on allele frequencies selected from populations of interest. This study provides an outline of sequence-based (SB) allele frequencies for autosomal short tandem repeats (aSTRs) and identity single nucleotide polymorphisms (iSNPs) in 371 individuals from Southern Norway. 27 aSTRs and 94 iSNPs were previously analysed with the ForenSeq™ DNA Signature Prep Kit (Verogen). The number of alleles with frequencies less than 0.05 for sequenced-based alleles was 4.6 times higher than for length-based alleles. Consistent with previous studies, it was observed that sequence-based data (both with and without flanks) exhibited higher allele diversity compared to length-based (LB) data; random match probabilities were lower for SB alleles confirming their advantage to discriminate between individuals. Two alleles in markers D22S1045 and Penta D were observed with SNPs in the 3´ flanking region, which have not been reported before. Also, a novel SNP with a minor allele frequency (MAF) of 0.001, was found in marker TH01. The impact of the sample size on minor allele frequency (MAF) values was studied in 88 iSNPs from Southern Norway (n = 371). The findings were then compared to a larger Norwegian population dataset (n = 15,769). The results showed that the smaller Southern Norway dataset provided similar results, and it was a representative sample. Population structure was analyzed for regions within Southern Norway; FST estimates for aSTR and iSNPs did not indicate any genetic structure. Finally, we investigated the genetic differences between Southern Norway and two other populations: Northern Norway and Denmark. Allele frequencies between these populations were compared, and we found no significant frequency differences (p-values > 0.0001). We also calculated the pairwise FST values per marker and comparisons between Southern and Northern Norway showed small differences. In contrast, the comparisons between Southern Norway and Denmark showed higher FST values for some markers, possibly driven by distinct alleles that were present in only one of the populations. In summary, we propose that allele frequencies from each population considered in this study could be used interchangeably to calculate genotype probabilities.

Forensic DNA Profiling: Legal and Ethical Considerations

Forensic DNA analysis is vital in criminal investigations, posing intricate legal and ethical dilemmas. This emphasises the need to balance DNA technology benefits with individual rights, prioritizing privacy, consent, and ethical practices. Stringent regulation of DNA data management is essential to prevent misuse and ensure privacy protection. Advances like probabilistic genotyping software improve interpretation accuracy, addressing complexities in mixture analysis. Responsible DNA use also requires navigating emerging technologies like next-generation sequencing while upholding ethical standards. Establishing transparency, accuracy, and ethical conduct throughout the analysis process is crucial for building public trust. Regulatory oversight and accountability mechanisms are necessary to address errors and maintain fairness in DNA data management. Through continuous dialogue and adherence to ethical standards, the forensic community can uphold scientific integrity and preserve public confidence in DNA analysis.

A diagnosis of the primary difference between EuroForMix and STRmix™.

There is interest in comparing the output, principally the likelihood ratio, from the two probabilistic genotyping software EuroForMix (EFM) and STRmix™. Many of these comparison studies are descriptive and make little or no effort to diagnose the cause of difference. There are fundamental differences between EFM and STRmix™ that are causative of the largest set of likelihood ratio differences. This set of differences is for false donors where there are many instances of LRs just above or below 1 for EFM that give much lower LRs in STRmix™. This is caused by the separate estimation of parameters such as allele height variance and mixture proportion using MLE under Hp and Ha for EFM. This can result in very different estimations of these parameters under Hp and Ha . It results in a departure from calibration for EFM in the region of LRs just above and below 1.

An Investigation into Compound Likelihood Ratios for Forensic DNA Mixtures.

Simple propositions are defined as those with one POI and the remaining contributors unknown under Hp and all unknown contributors under Ha. Conditional propositions are defined as those with one POI, one or more assumed contributors, and the remaining contributors (if any) unknown under Hp, and the assumed contributor(s) and N unknown contributors under Ha. In this study, compound propositions are those with multiple POI and the remaining contributors unknown under Hp and all unknown contributors under Ha. We study the performance of these three proposition sets on thirty-two samples (two laboratories × four NOCs × four mixtures) consisting of four mixtures, each with N = 2, N = 3, N = 4, and N = 5 contributors using the probabilistic genotyping software, STRmix™. In this study, it was found that conditional propositions have a much higher ability to differentiate true from false donors than simple propositions. Compound propositions can misstate the weight of evidence given the propositions strongly in either direction.

Developmental validation of STRmix™ NGS, a probabilistic genotyping tool for the interpretation of autosomal STRs from forensic profiles generated using NGS

We describe the developmental validation of the probabilistic genotyping software – STRmix™ NGS – developed for the interpretation of forensic DNA profiles containing autosomal STRs generated using next generation sequencing (NGS) also known as massively parallel sequencing (MPS) technologies. Developmental validation was carried out in accordance with the Scientific Working Group on DNA Analysis Methods (SWGDAM) Guidelines for the Validation of Probabilistic Genotyping Systems and the International Society for Forensic Genetics (ISFG) recommendations and included sensitivity and specificity testing, accuracy, precision, and the interpretation of case-types samples. The results of developmental validation demonstrate the appropriateness of the software for the interpretation of profiles developed using NGS technology.

Compound and Conditioned Likelihood Ratio Behavior within a Probabilistic Genotyping Context.

In cases where multiple questioned individuals are separately supported as contributors to a mixed DNA profile, guidance documents recommend performing a comparison to see if there is support for their joint contribution. Anecdotal observations suggest the summed log of the individual likelihood ratios (LR), termed the simple LR product, should be roughly equivalent to or less than the log(LR) for the joint likelihood ratio, termed the compound LR. To assist casework analysts in evaluating statistical weights applied to a case at hand, this study assessed how consistently compound LRs conform to an additive behavior when compared to the simple LR product counterparts. Two-, three-, and four-person DNA mixture data, of various mixture proportions and DNA inputs, were interpreted by STRmix® version 2.8 Probabilistic Genotyping Software. Relative magnitudes of LR increases were found to be dependent on both template level and mixture composition. The distribution of log(LR) differences between all compound/simple LR comparisons was ~-2.7 to ~28.3. This level of information gain was similar to that for compound LR comparisons, with and without interpretation conditioning (~-3.2 to ~27.7). In both scenarios, the probability density peaked at approximately 0.5, indicating the information gain from constrained genotype combinations has a comparable impact on the outcome of LR calculations whether the restriction is applied before or after interpretation.

Y-STR mixture deconvolution by single-cell analysis.

Since Y-STR typing only amplifies male Y chromosomal DNA, it can simplify the interpretation of some DNA mixtures that contain female DNA. However, if there are multiple male contributors, mixed Y-STR DNA profiles will often be obtained. Y-STR mixture analysis cases are particularly challenging though as, currently, there are no validated probabilistic genotyping (PG) software solutions commercially available to aid in their interpretation. One approach to fully deconvoluting these challenging mixtures into their individual donors is to conduct single-cell genotyping by isolating individual cells from a mixture prior to conducting DNA typing. In this work, a physical micromanipulation technique involving a tungsten needle and direct PCR with decreased reaction volume and increased cycle number was applied to equimolar 2- and 3-person buccal cell male DNA mixtures and a mock touch DNA case scenario involving the consecutive firing of a handgun by two males. A consensus DNA profiling approach was then utilized to obtain YFiler™ Plus Y-STR haplotypes. Buccal cells were used to optimize and test the direct single-cell subsampling approach, and 2-3 person male buccal cell mixtures were fully deconvoluted into their individual donor Y-STR haplotypes. Single-cell (or agglomerated cell clump) subsampling from the gun's trigger recovered single-source Y-STR profiles from both individuals who fired the gun, the owner, and the other unrelated male. Only the non-owner's DNA was found in the cells recovered from the handle. In summary, direct single-cell subsampling as described represents a potential simple way to analyze and interpret Y-STR mixtures.

Limitations of qPCR to estimate DNA quantity: An RFU method to facilitate inter-laboratory comparisons for activity level, and general applicability

The application of qPCR to estimate the quantity of DNA present is usually based upon a short amplicon (typically c.80bp) and a longer amplicon (typically c.200–300bp) where the latter is used to determine the amount of degradation present in a sample. The data are used to make decisions about a) whether there is sufficient template to amplify? b) how much of the elution volume to forward to PCR? A typical multiplex amplifies template in the region of 100–500bp. Consequently, the results from an 80bp amplicon will tend to overestimate the actual amplifiable quantity that is present in a degraded sample. To compensate, a method is presented that relates the quantity of amplifiable DNA to the average RFU of the amplified fragments. This provides greatly improved accuracy of the estimated quantity of DNA present, which may differ by more than an order of magnitude compared to qPCR. The relative DNA quantities can be apportioned per contributor once mixture proportions are ascertained with probabilistic genotyping software (EuroForMix). The motivation for this work was to provide an improved method to generate data to prepare distributions that are used to inform activity level propositions. However, other applications will benefit, particularly those where extraction and quantification are bypassed: For example direct PCR and Rapid DNA technology. The overall aim of this work was to provide a method of quantification that is standardised and can be used to compare results between different laboratories that use different multiplexes. A software solution ”ShinyRFU” is provided to aid calculations.

Internal Validation of MaSTR™ Probabilistic Genotyping Software for the Interpretation of 2-5 Person Mixed DNA Profiles.

Mixed human deoxyribonucleic acid (DNA) samples present one of the most challenging pieces of evidence that a forensic analyst can encounter. When multiple contributors, stochastic amplification, and allele drop-out further complicate the mixture profile, interpretation by hand becomes unreliable and statistical analysis problematic. Probabilistic genotyping software has provided a tool to address complex mixture interpretation and provide likelihood ratios for defined sets of propositions. The MaSTR™ software is a fully continuous probabilistic system that considers a wide range of STR profile data to provide likelihood ratios on DNA mixtures. Mixtures with two to five contributors and a range of component ratios and allele peak heights were created to test the validity of MaSTR™ with data similar to real casework. Over 280 different mixed DNA profiles were used to perform more than 2600 analyses using different sets of propositions and numbers of contributors. The results of the analyses demonstrated that MaSTR™ provided accurate and precise statistical data on DNA mixtures with up to five contributors, including minor contributors with stochastic amplification effects. Tests for both Type I and Type II errors were performed. The findings in this study support that MaSTR™ is a robust tool that meets the current standards for probabilistic genotyping.

A tool for simulating single source and mixed DNA profiles

Simulation studies play an important role in the study of probabilistic genotyping systems, as a low cost and fast alternative to in vitro studies. With ongoing calls for further study of the behaviour of probabilistic genotyping systems, there is a continuous need for such studies. In most cases, researchers use simplified models, for example ignoring complexities such as peak height variability due to lack of availability of advanced tools. We fill this void and describe a tool that can simulate DNA profiles in silico for the validation and investigation of probabilistic genotyping software. Contributor genotypes are simulated by randomly sampling alleles from selected allele frequencies. Some or all contributors may be related to a pedigree and the genotypes of non-founders are obtained by random gene dropping. The number of contributors per profile, and ranges for parameters such as DNA template amount and degradation parameters can be configured. Peak height variability is modelled using a lognormal distribution or a gamma distribution. Profile behaviour of simulated profiles is shown to be broadly similar to laboratory generated profiles though the latter shows more variation. Simulation studies do not remove the need for experimental data. The tool has been made available as an R-package named simDNAmixtures.

Investigation into the effect of mixtures comprising related people on non-donor likelihood ratios, and potential practises to mitigate providing misleading opinions

The interpretation of mixtures containing related individuals can be difficult due to allele sharing between the contributors. Challenges include the assignment of the number of contributors (NoC) to the mixture with the under assignment of NoC resulting in false exclusions of true donors. Non-donating relatives of the true contributors to mixtures of close relatives can result in likelihood ratios supporting their adventitious inclusion within the mixture. We examine the effect of non-donor likelihood ratios on mixtures of first order relatives. Mixtures of full siblings and parent-child were created by mixing the DNA from known family members in vitro, or by in silico simulation. Mixtures were interpreted using the probabilistic genotyping software STRmix™ and likelihood ratios were assigned for the true donors and non-donors who were either further relatives of the true donors or unrelated to the true donors. The two donor balanced mixtures deconvoluted straightforwardly when analysed as NoC = 2 giving approximately the experimental design 1:1 ratio. When analysed as NoC = 3 a very large number of non-donor genotypes produced LRs close to 1 including many instances of adventitious support. The in vitro three donor balanced mixtures proved difficult to assign as NoC = 3 by a blind examination of the profile. It is likely that many of these would be misassigned as NoC = 2. The analysis of the in vitro and in silico mixtures assuming NoC = 3 with no use of a conditioning profile or with the use of a conditioning profile but without informed priors on the mixture proportions (Mx priors) was ineffective. If the profile can be assigned as NoC = 3 then assignment of the Mx priors is straightforward. This analysis gave no false exclusions. Adventitious support did happen for relatives with high allele sharing. Adventitious support was not observed for any unrelated non-donors. The analysis of the three-person mixtures as NoC = 2 produced many false exclusions and fewer instances of adventitious support. The three donor unbalanced mixtures could all be assigned as NoC= 3. Analysis without Mx priors produced an alternate genotype explanation.

Exploring likelihood ratios assigned for siblings of the true mixture contributor as an alternate contributor

Relatives tend to have more DNA in common than unrelated people. The closer the biological relationship, the higher the chance of alleles being identical by descent between the individuals. Therefore, when considering a mixed DNA profile, close relatives of the true contributor may not always be excluded as a possible contributor to a mixture due to allele sharing. In these situations, it might be more appropriate under the alternate proposition to consider that the DNA could have originated from a relative of the person of interest rather than an unrelated individual. The probabilistic genotyping software STRmix™ automatically provides LRs considering close biological relatives as alternate sources of the DNA. In this paper, we investigate the support for siblings of the true contributor to a mixture (who are not present in the mixture themselves). We interpret the mixtures and assign LRs using STRmix™ and investigate whether the resulting LRs could be used to indicate whether the true contributor could be a sibling of the POI. Most siblings will have one or more alleles that are not observed in the mixture profile. Support for siblings to have contributed can only occur when allelic dropout is a possibility at the loci where the siblings have alleles that are not observed in the profile. In these data, that was only observed in components with assigned template of 588 rfu or less.

Development and validation of Kongoh ver. 3.0.1: Open-source software for DNA mixture interpretation in the GlobalFiler system based on a quantitative continuous model.

Probabilistic genotyping software based on continuous models is effective for interpreting DNA profiles derived from DNA mixtures and small DNA samples. In this study, we updated our previously developed Kongoh software (to ver. 3.0.1) to interpret DNA profiles typed using the GlobalFiler™ PCR Amplification Kit. Recently, highly sensitive typing systems such as the GlobalFiler system have facilitated the detection of forward, double-back, and minus 2-nt stutters; therefore, we implemented statistical models for these stutters in Kongoh. In addition, we validated the new version of Kongoh using 2-4-person mixtures and DNA profiles with degradation in the GlobalFiler system. The likelihood ratios (LRs) for true contributors and non-contributors were well separated as the information increased (i.e., larger peak height and fewer contributors), and these LRs tended to neutrality as the information decreased. These trends were observed even in profiles with DNA degradation. The LR values were highly reproducible, and the accuracy of the calculation was also confirmed. Therefore, Kongoh ver. 3.0.1 is useful for interpreting DNA mixtures and degraded DNA samples in the GlobalFiler system.

MaSTR™: an effective probabilistic genotyping tool for interpretation of STR mixtures associated with differentially degraded DNA.

The recently developed probabilistic genotyping software package MaSTR™ (SoftGenetics LLC) was used to develop statistical weight estimates for a variety of two-person STR mixture profiles with differentially degraded sources of DNA. A total of 864 analyses, on 144 two-person profiles, were performed. Mixture ratios ranged from 1:1 to 1:10, including pristine sources of DNA and various combinations of artificially degraded DNA (average size fragments of 150 or 250 bps). Quantities of DNA template were varied (0.1 to 0.5 ngs of total input) and MaSTR™ analysis was performed with eight chains of 10,000 or 40,000 iterations, with or without a conditioning profile to generate likelihood ratio (LR) values. Overall, the software performed as expected. The resulting log(LR) values for pristine mixture profiles were typically greater than 1030. Lower-quality mixture data associated with sources of DNA at ~ 0.05 ngs for each contributor resulted in peak imbalance and allelic dropout which reduced the weight in support of a contributor. This was exacerbated by higher levels of degradation, with some instances resulting in log(LR) values in support of an exclusion. These studies provide additional support for the use of probabilistic genotyping software solutions in forensic investigations, addressing concerns raised by the President's Council of Advisors on Science and Technology (PCAST).

Open practices in our science and our courtrooms

Advocates of transparency in science often point to the benefits of open practices for the scientific process. Here, we focus on a possibly underappreciated effect of standards for transparency: their influence on non-scientific decisions. As a case study, we consider the current state of probabilistic genotyping software in forensics.