Search Results (12)

Background/Objectives: The expansion of direct-to-consumer (DTC) genetic testing has generated civilian genomic databases containing tens of millions of profiles, some of which may be available, under specific conditions, for criminal investigations. Meanwhile, artificial intelligence (AI) is reshaping forensic genetics through applications such as kinship inference, DNA mixture deconvolution, probabilistic phenotyping, and the prioritization of investigative leads. This review examines the scientific, legal, and ethical implications of the convergence between DTC genetic databases, forensic investigative genetic genealogy (FIGG), and AI-assisted forensic analysis. Methods: This article presents a multidisciplinary narrative review at the intersection of forensic genomics, FIGG, artificial intelligence, genomic data governance, and bioethics, with particular attention to French, European, and international regulatory frameworks. Results: Six major dimensions structure the field: (i) the current state of forensic genomic technologies, including STRs, SNPs, and next-generation sequencing; (ii) the contribution of AI to forensic genetics and FIGG; (iii) the governance of large-scale genomic data; (iv) regulatory fragmentation across jurisdictions; (v) the principal bioethical tensions raised by the forensic use of DTC genetic databases; and (vi) future governance needs and operational recommendations. Across these dimensions, three findings emerge. First, genealogical matches and AI-supported outputs should be understood primarily as investigative leads rather than autonomous judicial evidence. Second, the relational nature of genomic data exposes non-consenting relatives to potential forensic scrutiny, thereby challenging traditional models of individual consent and privacy. Third, the absence of harmonized standards for validation, transparency, and oversight remains a major obstacle to legal certainty, judicial admissibility, and public legitimacy. Conclusions: The forensic use of DTC genetic databases should not be understood as a purely technical extension of conventional DNA profiling. It reflects a broader transformation in the relationship between genomic knowledge, criminal investigation, and fundamental rights. Its long-term legitimacy and operational viability will depend on the combined strength of scientific reliability, legal proportionality, ethical safeguards, and meaningful democratic oversight. Full article

Recent advances in forensic genetics are rapidly transforming the field from traditional DNA profiling toward integrative and predictive genomic approaches. While short tandem repeat (STR)-based typing remains the gold standard for human identification, emerging technologies such as massively parallel sequencing (MPS), forensic genetic genealogy (FGG), and artificial intelligence (AI)-driven bioinformatics are expanding the scope of forensic investigations, with MPS also widely established in clinical genomics, further supporting its application in complex and unresolved cases. This article presents a structured narrative and conceptual review of next-generation forensic genomics, based on selected peer-reviewed studies, technical guidelines, and recent review articles relevant to MPS-based marker analysis, FGG, DNA phenotyping, ancestry inference, AI-supported bioinformatics, validation, and ethical/legal issues. We discuss the transition from STRs to single nucleotide polymorphisms (SNPs) and microhaplotypes enabled by MPS, emphasizing their applications in mixture deconvolution, kinship analysis, and degraded DNA samples. The role of FGG in cold case resolution is examined, alongside methodological, legal, and ethical considerations related to the use of public genetic databases. Furthermore, we explore recent developments in DNA phenotyping and ancestry inference, focusing on predictive models of externally visible characteristics (EVCs) and their forensic utility. Particular attention is given to the growing impact of AI and machine learning in data interpretation, probabilistic genotyping, and pattern recognition across complex genomic datasets. Finally, we address current limitations, including technical standardization, population biases, data privacy concerns, and the need for robust validation frameworks. Rather than providing a systematic review, this work aims to synthesize current developments into an operational framework for integrated forensic genomics, distinguishing forensic intelligence, probabilistic interpretation, confirmatory testing, and evidentiary use. By integrating technological, analytical, and ethical perspectives, this review proposes a conceptual framework for integrated forensic genomics, in which genomic data are used not only for identification but also for forensic intelligence generation. Full article

DNA mixture interpretation remains one of the most technically demanding challenges in forensic genetics. While probabilistic genotyping (PG) systems have substantially advanced likelihood ratio (LR) evaluation, comparatively less attention has been devoted to the systematic reconstruction of contributor genotypes, particularly in no-suspect and database-search contexts. This review synthesizes recent developments in DNA mixture deconvolution through a four-strategy framework: (i) physical and biological separation, (ii) high-information genetic markers, (iii) continuous probabilistic algorithms, and (iv) integration with database searching infrastructures. Upstream approaches, including single-cell isolation and sequencing, reduce mixture complexity at the molecular level. Marker innovations such as microhaplotypes, MiniHaps and DIP-STRs increase per-locus information content and enhance resistance to degradation. Downstream probabilistic models—extended from STRs to SNPs and microhaplotypes—leverage quantitative signal data to infer contributor genotypes, with recent advances in Hamiltonian Monte Carlo, variational inference, and deep learning improving inferential stability and reconstruction accuracy. Importantly, genotype deconvolution and LR evaluation represent mathematically distinct objectives, requiring different validation metrics and potentially separate architectural optimization. The convergence of molecular innovation, algorithmic refinement, and LR-based database searching is progressively transforming mixture interpretation from a purely evidential assessment into an integrated investigative framework. Future progress will depend on standardized marker panels, deconvolution-specific performance metrics, and scalable LR-enabled database infrastructures. Full article

Background/Objectives: Interpretation of mixture profiles generated from crime scene samples is an important element in forensic genetics. Here, a workflow for mixture deconvolution of sequenced microhaplotypes (MHs) and STRs using the probabilistic genotyping software MPSproto v0.9.7 was developed, and the performance of the two types of loci was compared. Methods: Sequencing data from a custom panel of 74 MHs (the MH-74 plex) and a commercial kit with 26 autosomal STRs (the ForenSeq™ DNA Signature Prep Kit) were used. Single-source profiles were computationally combined to create 360 two-person and 336 three-person mixtures using the Python script MixtureSimulator v1.0. Additionally, 72 real mixtures typed with the MH-74 plex and 18 real mixtures typed with the ForenSeq Kit from a previous study were deconvoluted using MPSproto. Results: The deconvoluted MH profiles were more complete and had fewer wrong genotype calls than the deconvoluted STR profiles. The contributor proportion estimates were more accurate for MH profiles than for STR profiles. Wrong genotype calls were mostly caused by locus and heterozygous imbalances, noise reads, or an inaccurate contributor proportion estimation. The latter was especially problematic in STR sequencing data, when two contributors contributed equally to the mixture. A total of 34,800 deconvolutions of the simulated mixtures were performed with two defined hypotheses: H_p, “The sample consists of DNA from one/two unknown contributor(s) and the suspect” and H_d, “The sample consists of DNA from two/three unknown individuals”. All true contributors were identified (LR > 10¹⁵ for MHs and LR > 10⁹ for STRs) and all non-contributors excluded (LR < 10⁻⁶ for MHs and LR < 0.2 for STRs). Conclusions: In simulated and real mixtures, the MHs performed better than STRs. Full article

Mitochondrial (mt) DNA plays an important role in the fields of forensic and clinical genetics, molecular anthropology, and population genetics, with mixture interpretation being of particular interest in medical and forensic genetics. The high copy number, haploid state (only a single haplotype contributed per individual), high mutation rate, and well-known phylogeny of mtDNA, makes it an attractive marker for mixture deconvolution in damaged and low quantity samples of all types. Given the desire to deconvolute mtDNA mixtures, the goals of this study were to (1) create a new software, MixtureAceMT™, to deconvolute mtDNA mixtures by assessing and combining two existing software tools, MixtureAce™ and Mixemt, (2) create a dataset of in-silico MPS mixtures from whole mitogenome haplotypes representing a diverse set of population groups, and consisting of two and three contributors at different dilution ratios, and (3) since amplicon targeted sequencing is desirable, and is a commonly used approach in forensic laboratories, create biological mixture data associated with two amplification kits: PowerSeq™ Whole Genome Mito (Promega™, Madison, WI, USA) and Precision ID mtDNA Whole Genome Panel (Thermo Fisher Scientific by AB™, Waltham, MA, USA) to further validate the software for use in forensic laboratories. MixtureAceMT™ provides a user-friendly interface while reducing confounding features such as NUMTs and noise, reducing traditionally prohibitive processing times. The new software was able to detect the correct contributing haplogroups and closely estimate contributor proportions in sequencing data generated from small amplicons for mixtures with minor contributions of ≥5%. A challenge of mixture deconvolution using small amplicon sequencing is the potential generation of spurious haplogroups resulting from private mutations that differ from Phylotree. MixtureAceMT™ was able to resolve these additional haplogroups by including known haplotype/s in the evaluation. In addition, for some samples, the inclusion of known haplotypes was also able to resolve trace contributors (minor contribution 1–2%), which remain challenging to resolve even with deep sequencing. Full article

Analysis of complex DNA mixtures comprised of related individuals requires a great degree of care due to the increased risk of falsely including non-donor first-degree relatives. Although alternative likelihood ratio (LR) propositions that may aid in the analysis of these difficult cases can be employed, the prior information required for their use is not always known, nor do these alternative propositions always prevent false inclusions. For example, with a father/mother/child mixture, conditioning the mixture on the presence of one of the parents is recommended. However, the definitive presence of the parent(s) is not always known and an assumption of their presence in the mixture may not be objectively justifiable. Additionally, the high level of allele sharing seen with familial mixtures leads to an increased risk of underestimating the number of contributors (NOC) to a mixture. Therefore, fully resolving and identifying each of the individuals present in familial mixtures and excluding related non-donors is an important goal of the mixture deconvolution process and can be of great investigative value. Here, firstly, we further investigated and confirmed the problems encountered with standard bulk analysis of familial mixtures and demonstrated the ability of single cell analysis to fully distinguish first-degree relatives (FDR). Then, separation of each of the individual donors via single cell analysis was carried out by a combination of direct single cell subsampling (DSCS), enhanced DNA typing, and probabilistic genotyping, and applied to three complex familial 4-person mixtures resulting in a probative gain of LR for all donors and an accurate determination of the NOC. Significantly, non-donor first-degree relatives that were falsely included (LRs > 10²–10⁸) by a standard bulk sampling and analysis approach were no longer falsely included using DSCS. Full article

In recent years, the number of publications on microhaplotypes has averaged more than a dozen papers annually. Many have contributed to a significant increase in the number of highly polymorphic microhaplotype loci. This increase allows microhaplotypes to be very informative in four main areas of forensic uses of DNA: individualization, ancestry inference, kinship analysis, and mixture deconvolution. The random match Probability (RMP) can be as small as 10⁻¹⁰⁰ for a large panel of microhaplotypes. It is possible to measure the heterozygosity of an MH as the effective number of alleles (A_e). A_e > 7.5 exists for African populations and >4.5 exists for Native American populations for a smaller panel of two dozen selected microhaplotypes. Using STRUCTURE, at least 10 different ancestral clusters can be defined by microhaplotypes. The A_e for a locus is also identical to the Paternity Index (PI), the measure of how informative a locus will be in parentage testing. High A_e loci can also be useful in missing persons cases. Finally, high A_e microhaplotypes allow the near certainty of seeing multiple additional alleles in a mixture of two or more individuals in a DNA sample. In summary, a panel of higher A_e microhaplotypes can outperform the standard CODIS markers. Full article

When a minor DNA component to a binary mixture is present at a weight ratio of approximately 1:50 or less, the presence of this minor donor is undetectable (or barely detectable) by standard mixture deconvolution approaches. In an attempt to retrieve probative minor donor DNA profile information, multiple quintuple cell subsamples were collected from a 1:50 DNA mixture using direct single cell subsampling (DSCS) paired with probabilistic genotyping (PG), the latter validated for use with single or few cells. DSCS employs a simplified micromanipulation technique paired with an enhanced DNA profiling approach, involving direct cell lysis and a sensitive PCR process, to genotype individual cells. Multiple five-cell subsamples were used to interrogate sufficient cells from the mixture such that some of the created 5-cell “mini-mixture” subsamples contained a cell from the minor donor. The latter mini-mixture subsamples, which now comprised weight ratios of 1:4 as opposed to the bulk mixture 1:50, were analyzed with the PG systems STRmix^TM and EuroForMix resulting in a significant probative gain of information, (LR ≅ 10¹¹, compared to standard bulk mixture PG methods, LR ≅ 10¹–10²). Full article

Single nucleotide polymorphisms (SNPs) support robust analysis on degraded DNA samples. However, the development of a systematic method to interpret the profiles derived from the mixtures is less studied, and it remains a challenge due to the bi-allelic nature of SNP markers. To improve the discriminating power of SNPs, this study explored bioinformatic strategies to analyze mixtures. Then, computer-generated mixtures were produced using real-world massively parallel sequencing (MPS) data from the single samples processed with the Precision ID Identity Panel. Moreover, the values of the frequency of major allele reads (F_MAR) were calculated and applied as key parameters to deconvolve the two-person mixtures and estimate mixture ratios. Four custom R language scripts (three for autosomes and one for Y chromosome) were designed with the K-means clustering method as a core algorithm. Finally, the method was validated with real-world mixtures. The results indicated that the deconvolution accuracy for evenly balanced mixtures was 100% or close to 100%, which was the same as the deconvolution accuracy of inferring the genotypes of the major contributor of unevenly balanced mixtures. Meanwhile, the accuracy of inferring the genotypes of the minor contributor decreased as its proportion in the mixture decreased. Moreover, the estimated mixture ratio was almost equal to the actual ratio between 1:1 and 1:6. The method proposed in this study provides a new paradigm for mixture interpretation, especially for inferring contributor profiles of evenly balanced mixtures and the major contributor profile of unevenly balanced mixtures. Full article

Single-cell sequencing is a fast developing and very promising field; however, it is not commonly used in forensics. The main motivation behind introducing this technology into forensics is to improve mixture deconvolution, especially when a trace consists of the same cell type. Successful studies demonstrate the ability to analyze a mixture by separating single cells and obtaining CE-based STR profiles. This indicates a potential use of the method in other forensic investigations, like forensic DNA phenotyping, in which using mixed traces is not fully recommended. For this study, we collected single-source autopsy blood from which the white cells were first stained and later separated with the DEPArray™ N×T System. Groups of 20, 10, and 5 cells, as well as 20 single cells, were collected and submitted for DNA extraction. Libraries were prepared using the Ion AmpliSeq™ PhenoTrivium Panel, which includes both phenotype (HIrisPlex-S: eye, hair, and skin color) and ancestry-associated SNP-markers. Prior to sequencing, half of the single-cell-based libraries were additionally amplified and purified in order to improve the library concentrations. Ancestry and phenotype analysis resulted in nearly full consensus profiles resulting in correct predictions not only for the cells groups but also for the ten re-amplified single-cell libraries. Our results suggest that sequencing of single cells can be a promising tool used to deconvolute mixed traces submitted for forensic DNA phenotyping. Full article

Despite the benefits of quantitative data generated by massively parallel sequencing, resolving mitotypes from mixtures occurring in certain ratios remains challenging. In this study, a bioinformatic mixture deconvolution method centered on population-based phasing was developed and validated. The method was first tested on 270 in silico two-person mixtures varying in mixture proportions. An assortment of external reference panels containing information on haplotypic variation (from similar and different haplogroups) was leveraged to assess the effect of panel composition on phasing accuracy. Building on these simulations, mitochondrial genomes from the Human Mitochondrial DataBase were sourced to populate the panels and key parameter values were identified by deconvolving an additional 7290 in silico two-person mixtures. Finally, employing an optimized reference panel and phasing parameters, the approach was validated with in vitro two-person mixtures with differing proportions. Deconvolution was most accurate when the haplotypes in the mixture were similar to haplotypes present in the reference panel and when the mixture ratios were neither highly imbalanced nor subequal (e.g., 4:1). Overall, errors in haplotype estimation were largely bounded by the accuracy of the mixture’s genotype results. The proposed framework is the first available approach that automates the reconstruction of complete individual mitotypes from mixtures, even in ratios that have traditionally been considered problematic. Full article

As part of our program to identify novel secondary metabolites that target drug-resistant ovarian cancers, a screening of our aquatic-derived actinomycete fraction library against a cisplatin-resistant ovarian cancer cell line (OVCAR5) led to the isolation of novel diaza-anthracene antibiotic diazaquinomycin E (DAQE; 1), the isomeric mixture of diazaquinomycin F (DAQF; 2) and diazaquinomycin G (DAQG; 3), and known analog diazaquinomycin A (DAQA; 4). The structures of DAQF and DAQG were solved through deconvolution of X-Ray diffraction data of their corresponding co-crystal. DAQE and DAQA exhibited moderate LC₅₀ values against OVCAR5 of 9.0 and 8.8 μM, respectively. At lethal concentrations of DAQA, evidence of DNA damage was observed via induction of apoptosis through cleaved-PARP. Herein, we will discuss the isolation, structure elucidation, and biological activity of these secondary metabolites. Full article