Investigating the detection of two-person DNA mixtures using tri-allelic InDels.

DNA interpretation relies on the evaluation of results under at least two mutually exclusive propositions. This study evaluates the application of the International Society for Forensic Genetics (ISFG) recommendations concerning the formulation of such propositions across 15 laboratories in six countries and examines how evaluations are conducted when two persons of interest are considered. They were asked to assess results considering propositions about the source of the DNA during an interlaboratory comparison organized by the French Speaking Working Group of the ISFG. This article focuses on a DNA mixture from a mock case involving a complainant and two persons of interest. Seven of the eight ISFG recommendations were applicable to this interlaboratory comparison, with six being implemented by all laboratories. However, when two persons of interest were submitted for comparison without further case information, only seven laboratories followed Recommendation 3 by assigning a different likelihood ratio (LR) to each potential contributor. One of them used multiple propositions (more than two mutually exclusive propositions) and considered that each person, in turn, could or could not be the source of the DNA with or without the other person. The eight remaining laboratories assigned only one LR considering that both persons were contributors, or neither. As stated in the ISFG recommendations, such a practice should be avoided as it could lead to an overestimation of the LR for one of the contributors. We also demonstrate the effect of considering, or not, the presence of the DNA of persons whose contribution to the DNA mixture was not disputed (i.e., "conditioning" the DNA results on the DNA profiles of the undisputed source) on the LR. When conditioning is applied in ground truth experiments, the results provide stronger support for the proposition known to be true compared to the alternative. More precisely, the LR increased by a factor of 100-10'000 when conditioning, depending on the laboratory. The LRs assigned in two real cases are presented to illustrate the need to consider new information, such as the presence of a potential contributor to a DNA mixture, when evaluating results, and multiple propositions when several persons of interest are considered. It can significantly change the LR value.

DNAmix2021 was a large-scale inter-laboratory study conducted to assess variation in interpretations, comparisons, and statistical analyses of DNA mixtures. Analyses were based on 765 responses by 106 participants from 52 labs. Eight distinct DNA mixtures were created, each of which was provided to participants as a contributor packet (the person of interest (POI) was in the mixture), or as a non-contributor packet (the POI was NOT in the mixture). Accuracy on contributor packets was notably associated with the percent of DNA contributed by the POI: packets in which the POI contributed less than 8% of the DNA (≤25 pg) had much higher rates of false exclusions (contrary to ground truth) and indeterminate responses, compared to packets in which the POI contributed more DNA. A lab's ability to discern a low-level contributor is largely a function of its operating procedures: the community may wish to consider whether the exclusion of very low-level contributors can or should be considered incorrect. Most false inclusions were reported on one non-contributor packet that had high allele sharing with a POI that was a sibling of a contributor to the mixture. Most false exclusions and false inclusions were associated with incorrect estimates of the number of contributors. The few false inclusions may also be explained as a combination of inclusions without supporting statistics and/or not conditioning on reference profiles. The only likelihood ratios indicating very strong support that were contrary to ground truth were on contributor packets with a low proportion of the DNA contributed by the POI.

Cell-type-dependent expression of a somatic tri-allelic pattern at D18S51 and Penta D: The statistical interpretation of forensic evidence in sexual assault investigations.

A continuous model for interpreting microhaplotype profiles of forensic DNA mixtures.

This study introduces a methodology for inferring the weight of the evidence (WoE) in the single nucleotide polymorphism (SNP)-typed DNA mixtures of forensic interest. First, we redefined some algebraic formulae to approach the semi-continuous calculation of likelihoods and likelihood ratios (LRs). To address the allelic dropouts, a peak height ratio index (“h,” an index of heterozygous state plausibility) was incorporated into semi-continuous formulae to act as a proxy for the “split-drop” model of calculation. Second, the original ratio at which a person of interest (POI) has entered into the mixture was inferred by evaluating the DNA amounts conferred by unique genotypes to any possible permutation of any locus of the typing protocol (unique genotypes are genotypes that appear just once in the relevant permutation). We compared this expected ratio (MRex) to all the mixing ratios emerging at all other permutations of the mixture (MRobs) using several (1 - χ2) tests to evaluate the probability of each permutation to exist in the mixture according to quantitative criteria. At the level of each permutation state, we multiplied the (1 - χ2) value to the genotype frequencies and the h index. All the products of all the permutation states were finally summed to give a likelihood value that accounts for three independent properties of the mixtures. Owing to the (1 - χ2) index and the h index, this approach qualifies as a fully continuous methodology of LR calculation. We compared the MRs and LRs emerging from our methodology to those generated by the EuroForMix software ver. 3.0.3. When the true contributors were tested as POIs, our procedure generated highly discriminant LRs that, unlike EuroForMix, never overcame the corresponding single-source LRs. When false contributors were tested as POIs, we obtained a much lower LR value than that from EuroForMix. These two findings indicate that our computational method is more reliable and realistic than EuroForMix.

Evidentiary evaluation of complex low-template DNA mixtures using high-efficiency microhaplotype panels.

It is common practice to evaluate DNA profiling evidence with likelihood ratios using allele frequency estimates from a relevant population. When multiple populations may be relevant, a choice has to be made. For two-person mixtures without dropout, it has been reported that conservative estimates can be obtained by using the Person of Interest’s population with a value of 3%. More accurate estimates can be obtained by explicitly modelling different populations. One option is to present a minimum likelihood ratio across populations; another is to present a stratified likelihood ratio that incorporates a weighted average of likelihoods across multiple populations. For high template single source profiles, any difference between the methods is immaterial as far as conclusions are concerned. We revisit this issue in the context of potentially low-level and mixed samples where the contributors may originate from different populations and study likelihood ratio behaviour. We first present a method for evaluating DNA profiling evidence using probabilistic genotyping when the contributors may originate from different ethnic groups. In this method, likelihoods are weighted across a prior distribution that assigns sample donors to ethnic groups. The prior distribution can be constrained such that all sample donors are from the same ethnic group, or all permutations can be considered. A simulation study is used to determine the effect of either assumption on the likelihood ratio. The likelihood ratios are also compared to the minimum likelihood ratio across populations. We demonstrate that the common practise of taking a minimum likelihood ratio across populations is not always conservative when . Population stratification methods may also be non-conservative in some cases. When is used in the likelihood ratio calculations, as is recommended, all compared approaches become conservative on average to varying degrees.

Validation of a DNA mixture statistics tool incorporating allelic drop-out and drop-in

The open-source software LRmix can be used to analyse SNP mixtures

In recent years, a new compound genetic marker, deletion/insertion polymorphism-single nucleotide polymorphism (DIP-SNP), has been applied in forensic DNA mixture analysis. DIP-SNP genotyping based on capillary electrophoresis (CE) typically requires two separate amplification reactions and is difficult to interpret in complex DNA mixtures. In this study, four candidate imprinted DIP-SNP markers were selected to establish a multiplex polymerase chain reaction (PCR) set based on the amplification refractory mutation system (ARMS) principle for a one-tube reaction. Furthermore, parentally imprinted allele (PIA) typing was applied to selectively detect parental alleles. The imprinting patterns of rs35918685-rs185148 and rs11667883-rs76183558 were confirmed to be maternally imprinted, and both markers showed imprinting consistency in saliva, vaginal fluid, and menstrual blood. In DNA mixture analysis, these two markers successfully detected a minor DNA contributor in a two-person DNA mixture at a 1:20 ratio, indicating high analytical sensitivity for low template input and imbalanced mixtures. An improved RMNE method, described in a recent study, was applied to evaluate its effectiveness in narrowing down potential contributors in DNA mixtures, and a comparison of RMNE probabilities between conventional genotyping and PIA genotyping demonstrated the improved efficiency of PIA-based interpretation. These findings highlight the high forensic potential of imprinted DIP-SNP markers, particularly in the analysis of imbalanced DNA mixtures.

Improved individual identification in DNA mixtures of unrelated or related contributors through massively parallel sequencing

Comparing multiple POI to DNA mixtures

The effect of varying the number of contributors on likelihood ratios for complex DNA mixtures

Next-generation sequencing (NGS) allows for better identification of insertion and deletion polymorphisms (InDels) and their combination with adjacent single nucleotide polymorphisms (SNPs) to form compound markers. These markers can improve the polymorphism of microhaplotypes (MHs) within the same length range, and thus, boost the efficiency of DNA mixture analysis. In this study, we screened InDels and SNPs across the whole genome and selected highly polymorphic markers composed of InDels and/or SNPs within 300bp. Further, we successfully developed and evaluated an NGS-based panel comprising 55 loci, of which 24 were composed of both SNPs and InDels. Analysis of 124 unrelated Southern Han Chinese revealed an average effective number of alleles (Ae ) of 7.52 for this panel. The cumulative power of discrimination and cumulative probability of exclusion values of the 55 loci were 1-2.37×10-73 and 1-1.19×10-28 , respectively. Additionally, this panel exhibited high allele detection rates of over 97% in each of the 21 artificial mixtures involving from two to six contributors at different mixing ratios. We used EuroForMix to calculate the likelihood ratio (LR) and evaluate the evidence strength provided by this panel, and it could assess evidence strength with LR, distinguishing real and noncontributors. In conclusion, our panel holds great potential for detecting and analyzing DNA mixtures in forensic applications, with the capability to enhance routine mixture analysis.

In criminal investigations, forensic scientists need to evaluate DNA mixtures. The estimation of the number of contributors and evaluation of the contribution of a person of interest (POI) from these samples are challenging. In this study, we developed a new open-source software “Kongoh” for interpreting DNA mixture based on a quantitative continuous model. The model uses quantitative information of peak heights in the DNA profile and considers the effect of artifacts and allelic drop-out. By using this software, the likelihoods of 1–4 persons’ contributions are calculated, and the most optimal number of contributors is automatically determined; this differs from other open-source software. Therefore, we can eliminate the need to manually determine the number of contributors before the analysis. Kongoh also considers allele- or locus-specific effects of biological parameters based on the experimental data. We then validated Kongoh by calculating the likelihood ratio (LR) of a POI’s contribution in true contributors and non-contributors by using 2–4 person mixtures analyzed through a 15 short tandem repeat typing system. Most LR values obtained from Kongoh during true-contributor testing strongly supported the POI’s contribution even for small amounts or degraded DNA samples. Kongoh correctly rejected a false hypothesis in the non-contributor testing, generated reproducible LR values, and demonstrated higher accuracy of the estimated number of contributors than another software based on the quantitative continuous model. Therefore, Kongoh is useful in accurately interpreting DNA evidence like mixtures and small amounts or degraded DNA samples.