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

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

DNA mixtures are a common sample type in forensic genetics, and we typically assume that contributors to the mixture are unrelated when calculating the likelihood ratio (LR). However, scenarios involving mixtures with related contributors, such as in family murder or incest cases, can also be encountered. Compared to the mixtures with unrelated contributors, the kinship within the mixture would bring additional challenges for the inference of the number of contributors (NOC) and the construction of probabilistic genotyping models. To evaluate the influence of potential kinship on the individual identification of the person of interest (POI), we conducted simulations of two-person (2 P) and three-person (3 P) DNA mixtures containing unrelated or related contributors (parent-child, full-sibling, and uncle-nephew) at different mixing ratios (for 2 P: 1:1, 4:1, 9:1, and 19:1; for 3 P: 1:1:1, 2:1:1, 5:4:1, and 10:5:1), and performed massively parallel sequencing (MPS) using MGIEasy Signature Identification Library Prep Kit on MGI platform. In addition, in silico simulations of mixtures with unrelated and related contributors were also performed. In this study, we evaluated 1): the MPS performance; 2) the influence of multiple genetic markers on determining the presence of related contributors and inferring the NOC within the mixture; 3) the probability distribution of MAC (maximum allele count) and TAC (total allele count) based on in silico mixture profiles; 4) trends in LR values with and without considering kinship in mixtures with related and unrelated contributors; 5) trends in LR values with length- and sequence-based STR genotypes. Results indicated that multiple numbers and types of genetic markers positively influenced kinship and NOC inference in a mixture. The LR values of POI were strongly dependent on the mixing ratio. Non- and correct-kinship hypotheses essentially did not affect the individual identification of the major POI; the correct kinship hypothesis yielded more conservative LR values; the incorrect kinship hypothesis did not necessarily lead to the failure of POI individual identification. However, it is noteworthy that these considerations could lead to uncertain outcomes in the identification of minor contributors. Compared to length-based STR genotyping, using sequence-based STR genotype increases the individual identification power of the POI, concurrently improving the accuracy of mixing ratio inference using EuroForMix. In conclusion, the MGIEasy Signature Identification Library Prep kit demonstrated robust individual identification power, which is a viable MPS panel for forensic DNA mixture interpretations, whether involving unrelated or related contributors.

Deconvoluting multi-person biological mixtures and accurate characterization and identification of separated contributors using non-targeted single-cell DNA sequencing

The genetic characterization and identification of individuals who contributed to biological mixtures are complex and mostly unresolved tasks. These tasks are relevant in various fields, particularly in forensic investigations, which frequently encounters crime scene stains generated by more than one person. Currently, forensic mixture deconvolution is mostly performed subsequent to forensic DNA profiling at the level of the mixed DNA profiles, which comes with several limitations. Some previous studies attempted at separating single cells prior to forensic DNA profiling. However, these approaches are biased at selection of the cells and, due to their targeted DNA analysis on low template DNA, provide incomplete and unreliable forensic DNA profiles. We recently demonstrated the feasibility of performing mixture deconvolution prior to forensic DNA profiling through the utilization of a non-targeted single-cell transcriptome sequencing (scRNA-seq). In addition to individual-specific mixture deconvolution, this approach also allowed accurate characterisation of biological sex, biogeographic ancestry and individual identification of the separated mixture contributors. However, RNA has the forensic disadvantage of being prone to degradation, and sequencing RNA - focussing on coding regions - limits the number of single nucleotide polymorphisms (SNPs) utilized for genetic mixture deconvolution, characterization, and identification. These limitations can be overcome by performing single-cell sequencing on the level of DNA instead of RNA. Here, for the first time, we applied non-targeted single-cell DNA sequencing (scDNA-seq) by applying the scATAC-seq (Assay for Transposase-Accessible Chromatin with sequencing) technique to address the challenges of mixture deconvolution in the forensic context. We demonstrated that scATAC-seq, together with our recently developed De-goulash data analysis pipeline, is capable of deconvoluting complex blood mixtures of five individuals from both sexes with varying biogeographic ancestries. We further showed that our approach achieved correct genetic characterization of the biological sex and the biogeographic ancestry of each of the separated mixture contributors and established their identity. Furthermore, by analysing in-silico generated scATAC-seq data mixtures, we demonstrated successful individual-specific mixture deconvolution of i) highly complex mixtures of 11 individuals, ii) balanced mixtures containing as few as 20 cells (10 per each individual), and iii) imbalanced mixtures with a ratio as low as 1:80. Overall, our proof-of-principle study demonstrates the general feasibility of scDNA-seq in general, and scATAC-seq in particular, for mixture deconvolution, genetic characterization and individual identification of the separated mixture contributors. Furthermore, it shows that compared to scRNA-seq, scDNA-seq detects more SNPs from fewer cells, providing higher sensitivity, that is valuable in forensic genetics.

Addressing uncertain assumptions in DNA evidence evaluation

Evidential value of DNA mixtures is typically expressed by a likelihood ratio. However, selecting appropriate propositions can be contentious, because assumptions may need to be made around, for example, the contribution of a complainant's profile, or relatedness between contributors. A choice made one way or another disregards any uncertainty that may be present about such an assumption. To address this, a complex proposition that considers multiple sub-propositions with different assumptions may be more appropriate. While the use of complex propositions has been advocated in the literature, the uptake in casework has been limited. We provide a mathematical framework for evaluating DNA evidence given complex propositions and discuss its implementation in the DBLR™ software. The software simultaneously handles multiple mixed samples, reference profiles and relationships as described by a pedigree, which unlocks a variety of applications. We provide several examples to illustrate how complex propositions can efficiently evaluate DNA evidence. The addition of this feature to DBLR™ provides a tool to approach the long-accepted, but often impractical suggestion that propositions should be exhaustive within a case context.