Forensic science international. Genetics最新文献

Forensic estimation of the postmortem interval (PMI) becomes increasingly challenging when decomposition progresses beyond the initial weeks, as traditional medicolegal indicators lose their temporal precision. Here, we demonstrate that dual-kingdom microbial communities associated with decomposing remains form robust molecular clocks that maintain predictive power for PMI estimation well into skeletonization. Using full-length amplicon sequencing, we tracked the succession of bacteria and fungi in host-associated oral samples and the underlying gravesoil from decomposing pigs (N = 6) over nearly five months (2170.0 accumulated degree days; ADD). Microbial communities exhibited consistent three-phase succession patterns - initial disruption, intermediate colonization, and late-stage stabilization - that aligned with morphological decomposition scoring. Necrobiome succession dynamics continued long after morphological decomposition metrics reached a plateau, demonstrating the extended temporal resolution provided by microbial markers. Machine-learning models integrating microbial features with morphological data achieved robust predictive accuracy for both PMI in days and ADD, with performance varying systematically across decomposition stages. Bacterial models dominated early decomposition, dual-kingdom approaches optimized intermediate phases, and fungal models excelled during late-stage decomposition when conventional indicators fail. We identified specific microbial taxa that serve as reliable temporal indicators across sample types. These findings demonstrate that necrobiome succession extends capabilities to estimate time since death by months, offering a molecular framework for advanced decomposition cases where traditional methods lose precision.

Bald and Golden eagles are the largest raptors in North America and species of great cultural and ecological importance. Bald and Golden eagles and their parts are still used by indigenous peoples in crafts and ceremonies. The sale of eagle feathers and body parts in illegal markets continues to threaten the recovery of Bald and Golden eagle populations after decades of protection under the Endangered Species Act (1973) and the Bald and Golden Eagle Protection Act (1940). The ability to identify eagle parts and match evidence items among eagle victims is an important part of prosecuting wildlife crimes that involve these species. The three short tandem repeat (STR) multiplex panels we developed target 19 STR loci and the sex-linked chromo helicase DNA binding (CHD) gene to identify species and sex of eagles as well as provide the ability to match and individualize eagle forensic evidence. A collated database of complete genotypes from 289 Bald Eagles and 222 Golden Eagles from North America was created to serve as population references for the purpose of calculating match probabilities and likelihood ratios. The Bald Eagle database has a median theta-corrected match probability of 5.99 × 10^-8 and a median likelihood ratio of 1.67 × 10⁷. The Golden Eagle database has a median theta-corrected match probability of 4.68 × 10^-11 and a median likelihood ratio of 2.14 × 10¹⁰.

Y-chromosomal short tandem repeats (Y-STRs) are highly informative tools in forensic investigations for tracing paternal lineages and generating investigative leads when direct autosomal STR matches are not available. However, the discriminative power of expanded Y-STR panels and optimal strategies for database searching remain inadequately defined. Here, we first presented kinshipY, an interactive online platform that streamlines the analysis of Y-STR mutations, pedigree structure visualization, genetic distance calculation, and statistical power evaluation. Using this tool, we then empirically estimated the mutation rates of 80 Y-STRs (Forensic Analysis System Multiplecues SetB Kit) based on 488 father-son pairs. Finally, the differentiation rates among male relatives and between unrelated males were evaluated using three deep-rooted families spanning 1-27 meioses. The results showed that a total of 120 mutations were identified, yielding an overall mutation rate of 3.1 × 10⁻³ (95 % CIs: 2.5 × 10^-3 - 3.7 × 10^-3). Single-step mutations accounted for 95 % of events, with gains and losses occurring at nearly equal frequencies. The SetB panel distinguished 27.96 % of father-son pairs and 50 % of siblings, with differentiation rates reaching 100 % for relationships separated by ≥ 11 meioses. To differentiate males from distinct lineages, we established a threshold‑setting strategy that balances both the false positive rate (FPR) and false negative rate (FNR), demonstrating that step‑difference‑based thresholds outperform locus‑difference‑based thresholds. For the SetB panel, a step‑difference threshold of ≥ 15 enabled the differentiation of 99.59 % of unrelated males. In contrast, panels with fewer Y‑STRs-Yfiler (16 Y‑STRs), Class A (19 Y‑STRs), Yfiler Plus (25 Y‑STRs), and Class A+B (32 Y‑STRs)-exhibited significantly higher FPR and FNR. In summary, this study demonstrates the enhanced resolution offered by the SetB panel. For Y-STR database searching, we recommend the following: (1) use a powerful marker set whenever possible; (2) adopt a step-difference-based matching strategy; (3) apply dynamic, panel-specific thresholds; and (4) when integrating forensic investigative genetic genealogy, include more distant relatives for threshold estimation. These recommendations could provide valuable guidance for forensic practice.

DNA methylation (DNAm) profiling has proved to be a reliable method for estimating the chronological age of an unknown sample donor, offering valuable leads in police investigations. However, a major limitation of current approaches using autosomal age-predictive CpGs is their inapplicability to multi-donor DNA samples (DNA mixtures), which are frequently encountered in forensic casework. Here, we present a novel approach, UnMixMe (UnMix DNA Methylation profiles), to deconvolute the DNAm profile of an unknown individual (the suspect) from a two-person DNA mixture and subsequently use this profile to predict the suspect's chronological age. Importantly, it relies on knowing 1) the DNAm profile of the other contributor (the victim) and 2) the DNA mixture ratio between the two contributors. The latter was estimated via the traditional STR profile for human identification. We tested our approach on mock trace DNA mixtures prepared from blood samples with varying suspect-to-victim ratios (10:1, 4:1, 2:1, 1:1, 1:2, 1:4, and 1:10) from two male-female pairs. We measured DNAm levels (β-values) using the Illumina EPIC v2.0 microarray and deconvoluted the DNAm profiles of the DNA mixtures to retrieve the DNAm profile of the suspect. The age of the suspect was predicted using four array-based epigenetic clocks (BLUP, EN, Horvath, and skinHorvath clock). We achieved age prediction accuracy comparable to that of the single-source suspect DNA sample, except for DNA mixtures with a low suspect-to-victim ratio (1:4 and 1:10). We identified three main factors affecting the age prediction accuracy: 1) precision of the DNAm technology, 2) accuracy of DNA mixture ratio estimation, and 3) accuracy of the prediction model used. Importantly, the age difference between DNA mixture contributors did not influence prediction accuracy. With this proof-of-concept study, we establish that autosomal DNAm profiles from two-person DNA mixtures can be successfully deconvoluted when one contributor is known and highlight the potential of this method for predicting chronological age in mixed DNA samples.

A common task in forensic biology is to interpret and evaluate short tandem repeat DNA profiles. The first step in these interpretations is to assign a number of contributors to the profiles, a task that is most often performed manually by a scientist using their knowledge of DNA profile behaviour. Studies using constructed DNA profiles have shown that as DNA profiles become more complex, and the number of DNA-donating individuals increases, the ability for scientists to assign the true number decreases. There have been a number of machine learning algorithms developed that seek to assign the number of contributors to a DNA profile, however due to practical limitations in being able to generate DNA profiles in a laboratory, the algorithms have been based on summaries of the available information. In this work we develop an analysis pipeline that simulates the electrophoretic signal of an STR profile, allowing virtually unlimited, pre-labelled training material to be generated. We show that by simulating 100 000 profiles and training a number of contributors estimation tool using a deep neural network architecture (in an algorithm named deepNoC) that a high level of performance is achieved (89 % for 1-10 contributors). The trained network can then have fine-tuning training performed with only a few hundred laboratory-produced profiles in order to achieve the same accuracy within a specific laboratory. We also build into deepNoC secondary outputs that provide a level of explainability to a user of the algorithm and show how they can be displayed in an intuitive manner.

The forensic investigation of corpses recovered from aquatic environments presents a major practical challenge. Recent studies have demonstrated that the bacterial community in the lung serves as a valuable indicator for diagnosing drowning, determining the drowning medium and estimating postmortem submersion interval (PMSI). However, the application and significance of lung multi-kingdom microbiome (archaea, eukaryota, and viruses) remains inadequately characterized. Meanwhile, the insufficient sequencing depth of commonly employed techniques, such as amplicon sequencing, restricts our understanding of microbial communities. In this study, we characterized the postmortem lung microbiome of mice submerged in water for up to 10 days using metagenomic sequencing, and subsequently validated the potential microbial biomarkers in both murine and human forensic specimens via qPCR. Integrated analyses were conducted followed by the confirmation of significant lung bacterial communities for drowning diagnosis, inference of drowning site, and estimation of the PMSI. Our findings revealed that bacteria constituted the predominant component of the lung microbiome in submerged murine carcasses, with eukaryota serving as the secondary dominant taxa. Seventeen bacterial and nine eukaryotic features at the species level were identified as potential biomarkers for drowning diagnosis. By detecting the specific molecular markers for Aeromonas species in both murine and human samples, the positive detection of Aeromonas species, particularly Aeromonas hydrophila, provides solid evidence for drowning diagnosis. Additionally, 14 and 17 bacterial species were identified as biomarkers for the inference of drowning site and estimation of PMSI, respectively. Based on the identified potential biomarkers, robust forensic models were constructed using the random forest (RF) algorithm. The accuracy of the bacterial model for drowning diagnosis was 89.29 %, while the accuracy of the eukaryotic model was 87.5 %. For the inference of the drowning site, the bacterial model achieved an accuracy of 100 %. Furthermore, the estimation of the PMSI yielded a mean absolute error of 0.66 ± 0.097 days. Collectively, our findings revealed that the selected 17 bacterial and 9 eukaryotic features in the lungs, particularly Aeromonas hydrophila, are beneficial for drowning diagnosis. Additionally, the other selected bacterial species contribute to the estimation of the drowning site and PMSI, thereby providing more comprehensive and refined information for accurate forensic investigations of corpses recovered from aquatic environments.

Biogeographical ancestry (BGA) inference is an important tool in forensic genetics. However, typical approaches often rely on predefined population labels and limited marker sets, which constrain both resolution and flexibility. In this study, we evaluate the potential of unsupervised feature selection for BGA inference using Sparse K-means with Feature Ranking (SKFR), as implemented in OpenADMIXTURE. We leveraged the largest dataset used in a forensic context to date, comprising approximately 6500 individuals genotyped at ∼600,000 SNPs on the Human Origins (HO) array. Based on this dataset, we evaluated SKFR-selected ancestry-informative marker (AIM) panels ranging from 1500 to 2200 SNPs. Clustering performance was assessed using OpenADMIXTURE and quantified with G' similarity. Among the tested panels, a 1,900-SNP panel showed the most consistent clustering results and was selected for further evaluation. To examine forensic relevance, we compared this panel to a randomly selected SNP panel of the same size. Both panels produced broadly similar clustering patterns with OpenADMIXTURE, likely reflecting the marker composition of the HO array. The performance of the 1900 SKFR-selected SNPs was then evaluated using GENOGEOGRAPHER, a likelihood-based tool for BGA inference. Assignment analyses within the held-out test set provided a detailed overview of concordant and discordant assignments under the chosen reference metapopulations. While differences between the SKFR and random panels were modest, the SKFR panel showed consistently stronger and more stable assignment performance, demonstrating that unsupervised marker selection can add value even under the constraints of SNP arrays enriched for ancestry-informative variants. Overall, our study offers a systematic critical evaluation of unsupervised AIM selection and its limitations in practical settings. We show that panel size, array ascertainment, and reference dataset composition jointly shape ancestry-inference performance, and we encourage inference approaches that are not tied to fixed marker panels but instead make use of as many informative SNPs as feasible.

TrACE (Trace Analysis Collaborative Exercise) represents a novel, strictly expert driven and transparent concept of external quality control based on a combination of proficiency testing and interlaboratory comparisons. TrACE is an official proficiency test scheme of the German Stain Commission and acts in accordance with the recommendations for proficiency testing issued by this commission and outlined in DIN EN ISO 17025. TrACE offers modules on all aspects of forensic genetics that address challenges encountered in real casework. Basic modules represent proficiency tests that cover the physical examination of items, the identification of body fluids, DNA extraction, the analysis of autosomal and Y-chromosomal STRs and mitochondrial DNA (mtDNA), and the interpretation and verbalisation of results, including complex mixtures. Advanced and extended modules provide interlaboratory tests with more challenging items and novel methodology such as probabilistic genotyping and forensic DNA phenotyping (FDP). Each module is coordinated by an internationally recognised expert in the respective field. The members of the TrACE team are based in case work and/or academic forensic laboratories.

Population databases allow us to attach probabilities to DNA evidence by the estimation of genotype frequencies, which rely on accurate allele frequency estimates. As short tandem repeat (STR) marker sets for human identification have expanded to include more discriminating markers, and especially now that sequencing techniques allow us to distinguish between alleles based on variation in underlying base-pair structure, it is important to reevaluate existing guidance on population database sizes for the estimation of allele frequencies. In this paper, we revisit the topic of population sampling by focusing on the representation of alleles, i.e. whether alleles are observed or not, in a sample of individuals containing data for highly polymorphic autosomal STR loci. The effect of both length- and sequence-based STR data on population sample size implications are demonstrated, and differences between lesser and more polymorphic markers are discussed. The consequences of using a limited number of individuals are explored and the impact of increasing population sample sizes by combining different data sets is shown to help determine the point at which further sampling may no longer provide significant value. Finally, different approaches for accommodating previously unobserved alleles and their impact on DNA evidence evaluations are discussed.