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In forensic genetics, the allele and haplotype frequencies of relevant populations are required to determine the weight of evidence for DNA matches. In recent years, ethical concerns have been raised about population studies published in forensic journals and compiled in Forensic Genetics Frequency Databases (FGFDs), particularly regarding consent procedures for sample collection, the overrepresentation of vulnerable groups, and the risk of donor re-identification. A comprehensive review of ethical practices in forensic population genetics research on identity DNA polymorphisms and ancestry informative markers was conducted, focusing on papers published in Forensic Science International: Genetics and indexed in MEDLINE (2007-2025) and Forensic Science International: Reports (2019-2025). A decline in the number of published forensic population genetics papers was observed following the adoption of dedicated ethical guidelines in 2020. However, increased attention to ethical issues, such as the need for informed consent (IC) of sample donors and approval by pertinent ethical review boards (ERB), predates 2020, with a linear increase over time in the proportion of papers reporting IC and ERB approval. Among the included papers, 48.7% were conducted by or in collaboration with law enforcement laboratories, 39.1% involved minority populations, and 14.3% used forensic casework samples, all of which represent potential sources of ethical concern. In most studies conducted by multinational teams, researchers from the countries of the sampled populations were involved, with the notable exception of Africa, with 31.0% of studies having no African-affiliated authors. Furthermore, even after 2020, a substantial proportion of studies (11.8%) reported complete genotypes of DNA markers associated with a high risk of re-identification. The most common form of non-compliance with the 2020 ethical guidelines was the failure to report written informed consent (40.0%). Overall, the proportion of studies classified as ethically "low risk" according to the Forensic Database Advisory Board increased significantly after 2020, reaching 78.5% of the papers published in that period. Notably, during the preceding period (2010-2020), the proportion of low risk studies was significantly higher for DNA markers for which editorial guidelines required quality control by an FGFD (42.1%) than for other markers (30.3%). To support editors and peer reviewers, suggestions are proposed to improve ethical guidelines for the publication of forensic population genetics data, with particular attention to ERB approval documentation, secondary use of biological samples, and recognition of the role of researchers from the country of population samples collection in multinational studies.
Ancient DNA (aDNA) research has revolutionised archaeology and forensic science by enabling genomic recovery from highly degraded remains. This review explores the biochemical and environmental factors influencing aDNA preservation, alongside methodological advances that have improved data yield and authenticity. Techniques such as next-generation sequencing (NGS), single-stranded library preparation, and hybridisation capture have transformed the field, allowing recovery from ultrashort fragments and challenging contexts such as warm climates. Authentication strategies-including cytosine deamination profiling, fragment length analysis, and rigorous contamination controls-remain essential to ensure reliability. Applications of aDNA extend beyond ancestry reconstruction and population genetics to include forensic identification, kinship analysis, and pathogen detection. Lessons from forensic genetics, such as stringent validation and contamination mitigation, have informed best practices in archaeological contexts. However, ethical considerations are central to both domains. Issues of Indigenous data sovereignty, consent, repatriation, and culturally sensitive interpretation demand transparent, community-led research frameworks. These principles align with international agreements such as the Nagoya Protocol and emerging guidelines for equitable benefit-sharing. Despite significant progress, challenges persist, including geographic sampling bias, interpretive uncertainty, and the need for interdisciplinary integration. Future directions emphasise long-read sequencing, metagenomic approaches, and artificial intelligence-driven analytics, alongside robust ethical governance. By combining technological innovation with culturally responsible practices, aDNA research continues to advance our understanding of human history while reinforcing the importance of ethical stewardship in forensic and archaeological science.
Shotgun sequencing has emerged as a powerful tool in forensic genetics, as it allows for comprehensive genetic profiles to be generated from highly degraded DNA. The method enables simultaneous access to a wide range of markers, thereby supporting applications such as human identification (HID), DNA intelligence, and forensic investigative genetic genealogy (FIGG). However, the accuracy and utility of shotgun sequencing data are highly dependent on the bioinformatic analysis. In this study, we benchmarked widely used bioinformatic tools using shotgun sequencing data from both high-quality (blood and buccal) and low-quality (hair) forensic samples. Specifically, we evaluated five alignment algorithms (Bowtie2, BWA-ALN, BWA-MEM, CLC, and CLC LightSpeed), four genotype calling methods (ANGSD, ATLAS, GATK HaplotypeCaller, and a custom rule-based approach), and three imputation methods (Beagle4.1, Beagle5.4, and GLIMPSE2). All investigated tools were found to be suitable for analysing high-quality reference samples. However, their performance varied significantly when applied to low-quality (hair) forensic samples. The combination of BWA-MEM, ANGSD, or GATK HaplotypeCaller, and imputation with GLIMPSE2 produced the lowest degree of discordance. The work presented here emphasises the importance of informed bioinformatic tool selection and optimisation, and it provides practical recommendations for analysing shotgun sequencing data in forensic genetics.
Forensic epigenetics is emerging as a powerful extension of traditional forensic genetics, offering the capacity to infer age, lifestyle, and environmental exposures from epigenetic marks. Yet its promise is shadowed by significant ethical, legal, and social questions. This article analyzes the scientific foundations and practical applications of forensic epigenetic techniques while interrogating their implications for privacy, discrimination, and human rights. It argues that the promise of enhanced investigative capability must be balanced against risks of misuse, stigmatization, and function creep. Drawing on comparative perspectives in law and bioethics, the authors emphasize the importance of proportional governance frameworks that uphold transparency, accountability, and respect for persons. Suggestions are made for the responsible integration of epigenetic data in forensic contexts, if and when, it meets sufficiently rigorous standards.
Standard short tandem repeat (STR) DNA profiling has transformed forensic science by enabling reliable human identification across routine and complex casework. However, conventional 'bulk' DNA extraction and analysis inherently co-detect alleles from all biological contributors within a stain, thus potentially confounding the interpretation of DNA mixtures. Probabilistic genotyping software (PGS) has greatly improved the recovery of probative genetic information however complex mixtures such as those involving numerous individuals, first-degree relatives, or contributors present at low template levels, remain resistant to complete or confident deconvolution. Single-cell forensic genetics has emerged as a promising solution to these limitations. Rather than analyzing a pooled mixture of cellular and cell-free material, single-cell approaches isolate and genotype individual cells, typically tens to hundreds per sample, and statistically aggregate these profiles to infer the constituent single-source donor genotypes. In this review, we examine the evolution of single-cell DNA analysis (scDNA) within forensic science; current methods for cell recovery, isolation, and genotyping; approaches to data interpretation; casework applications; limitations and validation challenges; and future research directions. We place special emphasis on the significant methodological advances and empirical findings that have emerged in the past several years. Collectively, these developments signal that single-cell analysis may become an important supplement to established forensic DNA methods, particularly for the most challenging mixture types encountered in casework.
Accurate species identification of necrophagous insects is crucial in forensic investigations. To overcome the limitations of complex morphological identification-especially for larval stages-and time-consuming DNA sequencing methods, we developed a triplex recombinase polymerase amplification combined with a lateral flow assay (RPA-LFA). This assay targets three common forensically important insects: Chrysomya megacephala, Lucilia sericata, and Sarcophaga peregrina. We comprehensively evaluated the specificity, sensitivity, and mixed DNA detection capability of the assay. The results demonstrated extremely high specificity; the specific test lines (T-lines), indicated by different positions, accurately differentiated the target species without cross-reactivity with four other common necrophagous insects. Furthermore, the sensitivity analysis revealed low limits of detection, ranging from 62 pg to 250 pg. Additionally, the assay accurately identified the target species within complex mixed DNA samples. In conclusion, the developed triplex RPA-LFA system enables rapid and visual species identification of forensic insects, providing an efficient and reliable novel tool for forensic field investigations.
The interpretation of complex DNA mixtures remains a persistent challenge in forensic genetics. Although probabilistic genotyping systems for capillary electrophoresis (CE)-based short tandem repeats (STRs) represent significant progress, their applicability is constrained by the inherent limitations of CE platform when analyzing highly complex mixtures. Microhaplotypes (MHs) are multi-allelic markers compatible with next-generation sequencing (NGS) and present a promising alternative. However, the absence of methods for quantitatively interpreting NGS-based MH data hinders their practical application. To address this gap, we present SMART-MHmix, a probabilistic genotyping framework designed to model mixture profiles from NGS-sequenced MH loci and perform statistical evidentiary assessment. The framework calculates the probability of observed allele read counts conditional on their expected values, where the expected read counts are determined from the product of contributor template DNA amounts, locus-specific amplification efficiencies, and replicate-specific effects. The core likelihood calculation integrates three key components: a log-normal distribution modeling true allele signals, probabilistic models for stochastic drop-in and drop-out events, and Markov Chain Monte Carlo (MCMC) sampling for robust inference. We evaluated the performance of SMART-MHmix using 104 synthetic DNA mixtures of 2 to 5 contributors, profiled with the MHSeqTyper47 kit (47 autosomal MH loci). SMART-MHmix demonstrated robust performance across all mixture complexities. Likelihood ratio (LR) analysis provided strong discriminating power, with LRs for true contributors reaching up to 1047 and over 86% exceeding 105 across all mixture complexities, and correct support for exclusion for non-contributors. Using a threshold of θ=1, both sensitivity and specificity exceeded 90% in all tested scenarios. Mixture deconvolution enabled accurate genotype inference for major contributors, with high values for both the number of resolved loci and match success. Inference of the second contributor was reliable when the major contributor proportion exceeded 60%. For minor contributors ranked third or lower (by template DNA proportion), both the number of resolved loci and match success declined as mixture complexity increased. Overall, SMART-MHmix represents a dedicated continuous probabilistic model for MH-NGS data, enabling reliable analysis of complex forensic DNA mixtures with the potential to outperform conventional CE-STR workflows.
This study evaluated the ability to produce FORensic Capture Enrichment (FORCE) genotypes using amplicon-based and capture-based enrichment assays. The FORCE panel is a standardized set of single nucleotide polymorphism (SNP) markers developed for forensic applications. Twelve DNA samples were prepared and distributed to the laboratories for testing: five control DNA samples, a dilution series ranging from 10 ng to 0.03 ng, two degraded DNA samples with 200 bp and 150 bp average fragment lengths, and one inhibited sample spiked with humic acid. Fifteen laboratories from three different continents participated in this study, choosing from one of four manufacturer-developed enrichment assays to complete the experiments, setting their own parameters for sequencing and other user-defined steps to accommodate their own preferences and expertise. A total of eighteen methods were evaluated, as three laboratories performed two methods. The results showed that all four assays were successful in producing full FORCE SNP genotypes from high quality samples. However, significant differences between and within assays and methods were observed. Read count variability and enrichment type led to significant differences in call rate. Robust SNP recovery was observed across all assays at 0.3 ng DNA input, with an amplicon-based assay producing high SNP call rates at 0.03 ng DNA input. Capture and single primer extension assays produced consistently high SNP call rates from degraded samples with 150-200 bp fragments. Future research to optimize laboratory parameters may reduce the variation in SNP data, so that labs may equitably adopt SNP methods to make use of these powerful forensic markers.
SNP analysis in forensic genetics has expanded substantially over the past two decades, particularly for ancestry inference and forensic DNA phenotyping (FDP). However, interpretation of data from such analyses in complex, admixed populations is challenging and, if not carefully contextualized, risks misdirecting criminal investigations. Here we revisit Operation Minstead, a major UK criminal investigation leading to the conviction of serial sex offender Delroy Grant in 2011. In describing our ancestry and FDP analyses of Grant's DNA, we critically examine the role and limitations of early forensic SNP analyses in an operational context. Although DNA was readily available, SNP-based ancestry and phenotyping analyses did not meaningfully advance the investigation. We review the population genetics inferences made by different parties between 2004 and 2008, including reports from a commercial ancestry testing company and independent population genetics expertise, and contrast them with analyses performed by the Forensic Genetics Unit, University of Santiago de Compostela. Our evaluation highlights key methodological issues, including overinterpretation of the inferred co-ancestry proportions of the Minstead suspect, lack of transparency in proprietary SNP panels and reference population data used, and insufficient consideration of within-group variance in admixed populations. We discuss the limitations for inferring common and rare pigmentation patterns in early FDP analyses in individuals with predominantly African genomic backgrounds, and the challenges posed by incomplete marker coverage and limited biological understanding of the expression of pigmentation phenotypes at the time. This case illustrates the risks associated with excessive geographic precision in ancestry inference, especially when likelihoods are modest, and overlap of population variation is substantial. We emphasize the importance of combining information from uniparental markers, X-SNPs, and carefully selected ancestry-informative markers with extreme allele frequency differences, plus the value of likelihood-based frameworks over categorical interpretations. Finally, we discuss how lessons learned from Operation Minstead have informed the development, validation, and interpretation of forensic SNP panels in subsequent years. Overall, this retrospective analysis highlights the need for caution, transparency, and statistical rigor in the forensic application of SNP-based ancestry and phenotyping tests, as well as providing guidance for their careful use in current and future criminal investigations.
Facial approximation aids identification of unknown individuals in forensic and anthropological contexts. Digital approximation methods estimate Facial Soft‑Tissue Thickness (FSTT) and facial shape from virtual skulls, which are meant to lower subjectivity. Yet substantial variability persists in predicted outcomes, particularly in the nasal and lower facial regions. AFA3D (Anthropological Facial Approximation in Three Dimensions), developed by Guyomarc'h et al. (2014) from French data, generates facial predictions using statistical shape modelling, FSTT‑based warping, and iterative algorithms. Earlier studies reported moderate error in the mouth and smaller errors in nasal and orbital areas, but its broader performance remains insufficiently evaluated. This study assesses AFA3D by comparing approximated faces with original facial meshes from 40 CT-scans, 10 each from Czech, Slovak, Egyptian, and French samples. Geometric morphometric comparison was conducted using Morphome3cs II. Across samples, 75.9-84.2% of facial surfaces fell within ±2.5 mm deviation. Systematic regional errors were observed in the nose, lips, chin, cheeks, and upper face, with males generally showing greater localised discrepancies than females. These patterns correspond to anatomical regions with limited skeletal constraint and to sex‑linked cranial structural differences, as observed in previous approximation validations. Overall, AFA3D produces predictions with consistent regional error patterns, underscoring the need for more detailed mapping of local deviations, better modelling of posture‑related influences, and continued refinement of automated approximation methods to strengthen forensic reliability.
DNA methylation at CpG sites has emerged as a powerful epigenetic biomarker for predicting forensically relevant traits, including chronological age, the biological origin of forensic samples encompassing body fluid and tissue sources, and lifestyle-associated factors such as smoking. Existing models for age estimation, body fluid and tissue of origin identification, and smoking inference have demonstrated robust performance, but their reliance on separate assays limits practical application. To address this gap, we developed COSA (a Consolidated prediction panel for Origin, Smoking, and Age), an integrated methylation-based assay implemented through amplicon-based massively parallel sequencing (MPS). COSA consolidates 126 previously reported CpG markers from multiple validated models into 67 amplicons, thereby enabling the simultaneous prediction of body fluid and tissue of origin, smoking status, and chronological age from a single analysis. By unifying these established markers, COSA provides a scalable and streamlined solution for comprehensive forensic epigenetic profiling. The panel comprises three functional modules. First, body fluid identification incorporates 9 CpG markers specific to blood, semen, saliva, menstrual blood, and vaginal fluid, supporting accurate determination of sample origin in forensic framework. Additionally, body fluid and tissue of origin inference extends to internal organs through 18 CpGs targeting blood, liver, skeletal muscle, heart, brain, epidermis, dermis, kidney, and lung. Second, lifestyle inference is supported by 13 CpGs, including the well-characterized cg05575921 locus in the AHRR gene for smoking prediction. Third, age estimation is incorporated through three fluid-specific models optimized for blood, saliva, and semen, which are the fluids most frequently encountered in forensic investigations. Methodological refinements were essential to achieve balanced multiplex amplification. Multiplex PCR for bisulfite-converted DNA is challenged by issues related to primer compatibility and GC-content variation. To overcome this, we implemented a touchdown PCR strategy that improved amplification balance and coverage uniformity across multiple loci. Several primer sets were newly designed or modified to optimize amplicon length and annealing temperature, ensuring robust co-amplification within the 67-amplicon panel. Importantly, using as little as 20 ng of bisulfite-converted DNA, the COSA panel supported inference of biological origin, smoking status, and chronological age. Overall, COSA integrates three major forensic prediction modules, including origin classifiers for body fluids and organ tissues, fluid-specific age estimators, and a smoking-status predictor within a single DNA workflow. This panel represents a practical and scalable tool for forensic laboratories seeking to maximize information yield from limited DNA, advancing the application of epigenetics in human identification and investigative intelligence.
DNA mixtures are common in forensic samples and limit the effectiveness of recently developed intelligence tools based on novel sequencing technologies, such as biogeographic ancestry prediction. DIP-STR markers, which combine a low-mutation insertion-deletion (DIP) with a closely linked high-mutation short tandem repeat (STR), enable allele-specific detection of minor contributors in two-person DNA mixtures and contain information on population structure. In this study, we evaluated a set of 46 DIP-STR markers across globally representative populations to assess their potential for biogeographic ancestry inference. Using both clustering and likelihood-based assignment approaches, major continental groups remain distinguishable even when the genetic information is limited to the small fraction of markers detecting the minor component of a mixture. These data show that DIP-STRs offer a complementary solution by providing both minor DNA detection and ancestry information in scenarios where standard panels struggle, such as challenging mixed DNA samples. Furthermore, modelling the distinct evolutionary dynamics of DIPs and STRs within haplotypes has the potential to improve both the resolution and robustness of ancestry inference. Together, these findings establish DIP-STRs as a viable tool for forensic ancestry analysis and provide a global reference dataset for future method development.
This study refines a Bayesian probabilistic framework for adult age-at-death estimation based on root dentin translucency (RDT). Model comparison using the Bayesian Information Criterion demonstrated that a univariate model relying solely on RDT was the most parsimonious and efficient, achieving an R² of 63.60%. The framework was validated through entirely external samples: a Peruvian case study, and samples from Brazil (N = 247), the UK (N = 113), and Guatemala (N = 302). Bayesian updating using data from the Forensic International Dental Database (FIDB; N = 4522) substantially increased the strength of the evidence, with Likelihood Ratios rising from a mean of 2160 to nearly 10,000, elevating the inference to a level of very strong support and extremely strong support. Results confirmed the inter-population stability of the model and its capacity to mitigate the characteristic trajectory effect of overestimation in young adults and underestimation in older adults. This approach provides a transparent, robust, and globally applicable tool for forensic anthropology, successfully balancing statistical rigor with practical utility. Furthermore, it establishes a foundational platform, named BayAD, that enables the future integration of additional skeletal traits within an age-at-death estimation framework.
This is a response to the Editorial ""Minimum FSI: Genetics requirements for publishing data on DNA transfer and recovery, given activities" by Gill at el.
The human microbiome is ubiquitous across nearly all body sites and exhibits biological characteristics that enable stable detection under diverse environmental conditions. Microbial DNA can be analyzed even when human DNA is present in trace amounts or is severely degraded, supporting its potential as a complementary approach to conventional DNA-based identification. However, systematic validation of whether the microbiome simultaneously maintains individual specificity and long-term stability remains limited. In this study, we conducted a longitudinal analysis of the microbiome from the scalp, cheek, hand, and saliva of five healthy Korean participants over a period of up to three years. Intra-individual temporal stability and inter-individual variability were evaluated across multiple temporal scales, ranging from daily to annual intervals. Microbial community dynamics were assessed using relative abundance analysis, beta diversity metrics, and the theta-YC similarity index. Individual identification performance was evaluated for each sampling source using an XGBoost-based machine learning approach. Skin sites and saliva represented distinct ecological niches, with intra-individual similarity exceeding inter-individual similarity across sampling sites and temporal intervals. Although transient community shifts were observed in frequently exposed sites such as the scalp and hand under the four-season climate of the Republic of Korea, individual-specific microbial signatures were maintained over time. The XGBoost-based identification models achieved high accuracy, particularly for saliva (93.3%) and cheek (92.9%) samples. These findings support the potential of the skin and saliva microbiome as complementary tools for individual identification, particularly in forensic contexts where conventional human DNA analysis is limited.
Forensic lineage markers pose a challenge in forensic genetics as their evidential value can be difficult to quantify. Lineage marker population frequencies can serve as one way to express evidential value. However, for some markers, e.g., high-quality whole mitochondrial DNA genome sequences (mitogenomes), population data remain limited. In this paper, we offer a new method, MitoFREQ, for estimating the population frequencies of mitogenomes. MitoFREQ uses the mitogenome resources HelixMTdb and gnomAD, harbouring information from 195,983 and 56,406 mitogenomes, respectively. Neither HelixMTdb nor gnomAD can be queried directly for individual mitogenome frequencies, but offers single nucleotide variant (SNV) allele frequencies for each of 30 "top-level" haplogroups (TLHG), which mainly correspond to the first letter of major mitochondrial DNA (mtDNA) haplogroups (e.g., A, B, C, D, E, etc.) except for the L0, L1, L2, L3, L4-6, HV, and R/B haplogroups. We propose using the HelixMTdb and gnomAD resources by classifying a given mitogenome within the TLHG scheme and subsequently using the frequency of its rarest SNV within that TLHG weighted by the TLHG frequency. We show that this method is guaranteed to provide a higher population frequency estimate than if a refined haplogroup and its SNV frequencies were used. Further, we show that top-level haplogrouping can be achieved by using only 227 specific positions for 99.9% of the tested mitogenomes, potentially making the method available for low-quality samples. The method was tested on two types of datasets: high-quality forensic reference datasets and a diverse collection of scrutinized mitogenomes from GenBank. This dual evaluation demonstrated that the approach is robust across both curated forensic data and broader population-level sequences. This method produced likelihood ratios in the range of 100-100,000, demonstrating its potential to strengthen the statistical evaluation of forensic mtDNA evidence. We have developed an open-source R package mitofreq that implements our method, including a Shiny app where custom TLHG frequencies can be supplied.
This study presents the first systematic review examining the prevalence of DNA mixture inversion, defined as the occurrence of a major contributor to a DNA profile generated through indirect transfer via an intermediary individual. Following PRISMA guidelines, 45 empirical studies published between 1997 and 2022 were identified as containing relevant data to assess the prevalence of mixture inversion. Analysis of these studies, encompassing 3851 samples, showed that mixture inversion occurred in 1.69% of samples, consistent with previous reports. No significant differences were observed across substrate types. However, handlers classified as low shedders exhibited a higher frequency of mixture inversion compared to intermediate or high shedders. Mixture inversion was more likely following brief handling durations (<30 s) and when handlers had not recently washed their hands. These findings demonstrate that although mixture inversion is relatively uncommon, it may have important implications for interpreting DNA evidence at the activity level.
To maximize the usefulness of DNA obtained from biological samples in forensic genetics, it is crucial to avoid DNA contamination throughout all procedures, from sample collection at crime scenes to STR profile generation in DNA laboratories. This study reports a rare and atypical case of DNA contamination in a forensic setting. During the analysis of biological evidence from a cold case preserved for 18 years, the STR profile obtained from the surface of a plastic bag matched that of an investigator, identified through the DNA elimination database. Case reconstruction confirmed that the investigator-who was located 80 km from the DNA laboratory and had never entered the crime scene or the sample storage room-was not a suspect and that the obtained STR profile originated from contamination. The most plausible explanation for the contamination was indirect transfer: investigator's DNA had adhered to a colleague's clothing and was subsequently dislodged and deposited onto the surface of the plastic bag as the colleague approached the sample pretreatment area. This study integrates trace DNA profiling of challenged samples with rapid contamination investigation and proposes prevention and control measures. This case underscores that, although DNA is widely regarded as the "gold standard" in forensic genetics, its interpretation must be considered within the context of the entire case. Conclusions should not be drawn based solely on a single DNA result.